The Illusion of Control: What the Second Line Gets Wrong—and What Regulators and Failures Reveal

By Jeremy Swenson

Thirty-one. That is how many unaddressed safety-and-soundness supervisory warnings Silicon Valley Bank was sitting on when it collapsed in March 2023—roughly triple the number carried by comparable banks. The warnings existed. Examiners had written them down. Committees had reviewed them. And the bank failed anyway, in 36 hours, taking $209 billion in assets down with it.[1]

This figure isn’t really just about Silicon Valley Bank; it’s a broader story about how governance can falter right when it was meant to prevent failure. Across modern sectors like finance, healthcare, insurance, and tech—especially under heavy regulation—the structure is quite similar: a First Line managing risks, a Third Line (Internal Audit) independently evaluating effectiveness, and a Second Line acting as an oversight layer to challenge and ensure risk remains within boundaries before issues arise.

The uncomfortable pattern across nearly every major governance failure of the last fifteen years is not that the second line was absent. It was there, busy, and documented—and it still didn’t work.

That is the uncomfortable pattern across nearly every major governance failure of the last fifteen years: the second line of defense (2LOD) was rarely absent. It was there, it was busy, and it was thoroughly documented. JPMorgan’s Chief Investment Office had risk managers. Credit Suisse’s Prime Services division had a dedicated risk team. Wells Fargo had a corporate risk function, a legal department, and an audit group that all reviewed the Community Bank. Danske Bank’s Estonian branch had internal audit and a chief risk officer. In each case, the paperwork existed. The risk did not go away.

This raises the question at the center of this piece, and one that boards, regulators, and chief risk officers are increasingly asking out loud: is the modern second line of defense actually reducing risk—or is it primarily producing evidence that governance activities occurred? The two are not the same thing, and the gap between them is where some of the costliest failures in recent corporate history have lived.

What the Second Line Is Supposed to Do

The three-lines model that underpins risk governance at virtually every large regulated institution was formalized by the Institute of Internal Auditors in 2013 and substantially updated in 2020. The first line is operational management—the traders, lenders, engineers, and business unit leaders who own risk because they create it in the course of doing their jobs. The third line is internal audit, an independent function that reports to the board and periodically tests whether the first two lines are actually working. The second line sits in the middle: risk management, compliance, information security, and similar functions that provide, in the Institute’s own language, “complementary expertise, support, monitoring, and challenge” to the business.[2]

In U.S. banking specifically, this structure is not just best practice—it is regulatory expectation with teeth. The Office of the Comptroller of the Currency’s (OCC) 2014 heightened standards for large national banks explicitly require an independent risk management function, organizationally and financially separate from the business lines it oversees. The Federal Reserve’s 2011 guidance on model risk management, SR 11-7, assigns the second line an independent validation role specifically because business lines have an inherent incentive to trust their own models.[3]

Notably, when the Institute of Internal Auditors rewrote its guidance in 2020, it deliberately dropped the word “defense” from the model’s name, worried that the martial framing had encouraged organizations to treat risk management as purely defensive—blocking and reviewing—rather than as a function that helps an organization take the right risks well. That single word change is a useful preview of this piece’s argument: a second line built entirely around defense metrics—how many reviews were completed, how many policies exist, how many attestations were signed—can satisfy every requirement on paper while missing the actual point.[4]

Where the Model Breaks Down

Strip away the acronyms, and the recurring failure modes of the second line reduce to a short, uncomfortable list. Each one, on its own, sounds like a minor process gap. Together, and when combined with real money and real institutions, they have produced some of the largest corporate governance failures on record.

Documentation Instead of Risk Reduction

The clearest symptom is a second line that measures itself by volume: reviews completed, policies published, attestations collected, meetings held. Every one of those activities can be running at full capacity while the underlying risk grows untouched, because none of them require anyone to verify that a control actually works—only that someone said it does.

Self-Attestation Over Independent Verification

Much of traditional second-line practice depends on the first line telling the second line the truth: attestations, self-assessments, and point-in-time control tests that sample a narrow window and assume it represents the whole. When Danske Bank’s Estonian branch was later examined, the bank’s own lawyers conceded that “major deficiencies in controls and governance made it possible to use Danske Bank’s branch in Estonia for criminal activities such as money laundering,” and that internal reporting simply never reached the people positioned to stop it.[5]

Individual Exceptions Over Systemic Patterns

Second lines are often organized to catch one broken control at a time—a missed reconciliation, a late report, an expired certificate—rather than to notice that dozens of small, individually explainable exceptions are actually one large, systemic problem wearing different clothes.

Compliance Treated as a Proxy for Safety

Perhaps the most persistent conflation in second-line practice is the assumption that a control environment which satisfies a regulation is therefore a control environment that manages the underlying risk. The two frequently travel together. They are not the same claim, and treating them as interchangeable is exactly how organizations end up technically compliant and substantively exposed at once.

A Challenge Function That Doesn’t Actually Challenge

Effective second-line challenge requires two things that are hard to combine: enough independence to say no to a profitable business line, and enough technical and commercial fluency to know when “no” is actually warranted. Many second lines have one without the other—independent enough to be disliked, but not fluent enough in the actual business to be heeded, or so embedded in the business that independence quietly erodes.

Struggling to Govern What It Doesn’t Understand

Every one of the weaknesses above compounds sharply the moment the underlying risk is technical: artificial intelligence models, cloud migrations, third-party data pipelines, or novel cyber threats. A second line built to review loan files and sales scripts is not automatically equipped to evaluate a machine learning model’s training data lineage or a cloud vendor’s shared-responsibility boundary—and regulators are now saying so explicitly. NIST’s AI Risk Management Framework (RMF) and the broader push toward AI-specific governance exist precisely because traditional control catalogs were not written with adaptive, probabilistic systems in mind.[6]

Five Failures, One Pattern

These are not abstractions. They are the documented findings of regulators, board-appointed investigators, and congressional committees—and read together, they describe the same failure recurring in different industries, different countries, and different decades.

1. JPMorgan’s “London Whale” (2012)—When Risk Managers Don’t Know What the Business Is Doing

In 2012, JPMorgan Chase’s Chief Investment Office lost more than $6.2 billion on a series of synthetic credit derivative trades that came to be known as the “London Whale.” The U.S. Senate Permanent Subcommittee on Investigations spent nine months and reviewed more than 90,000 documents before concluding that the unit had mismarked its trading book to hide losses, disregarded multiple indicators of increasing risk, manipulated its own risk models, and evaded regulatory oversight.[7]

The Subcommittee’s report found that JPMorgan’s firm-wide risk managers—the second line—“knew little about” the trading strategy and had no role in approving the positions that produced the loss, even as the bank’s own public statements insisted the trades were consistent with firm-wide risk management. This was a second line that existed on the org chart and was functionally absent from the transaction that mattered most.[8]

2. Wells Fargo’s Sales Practices Scandal (2011–2016)—When Egos and Tenure Silence the Second Line

Between 2011 and 2016, Wells Fargo employees opened millions of unauthorized accounts to meet aggressive sales quotas, ultimately leading to the termination of roughly 5,300 employees and $185 million in regulatory penalties. When the bank’s independent directors released their own 110-page investigation in 2017, the findings went well beyond a rogue sales culture.[9]

The report found that Carrie Tolstedt, the long-tenured head of the Community Bank, and other Community Bank leaders “resisted and impeded scrutiny or oversight from corporate risk management and the Board,” and “minimized the scale and nature of problems” when they were forced to report them. Then-CEO John Stumpf, the report found, relied on “the Bank’s decades of success” and was “too slow to investigate or critically challenge” the sales model—a textbook description of tenure-driven bias, where years of past success become evidence against present-day concerns rather than a reason to look harder.[10]

Just as tellingly, the report found that Wells Fargo’s control functions were structurally weakened by internal politics: risk, legal, HR, and audit were “decentralized” and had “parallel units” embedded inside the Community Bank itself, reporting up through business-aligned structures that deferred to the business rather than challenging it. Audit reviewed the relevant controls and largely found them effective—but, the report notes pointedly, “it did not view its role to include analyzing more broadly the root cause of the improper conduct.” That is the governance-activity trap in a single sentence: the review happened, the box was checked, and the actual problem sailed through untouched.[11]

3. Credit Suisse and Archegos (2021)—When the Second Line Is Afraid to Say No

In March 2021, the collapse of Archegos Capital Management, a lightly regulated family office, cost Credit Suisse $5.5 billion—more than any other bank exposed to the same client. The board-commissioned investigation by Paul, Weiss found no fraud and no missing risk architecture. The controls existed. What failed was the willingness to use them.[12]

The investigation found a “persistent failure” to manage and remediate known risks connected to Archegos, and, more specifically, that Credit Suisse’s risk managers had intended to demand additional margin from Archegos to reflect its mounting credit risk—but were prevented from doing so because the business “deemed” it not to be in the bank’s commercial interest to upset the relationship. One outside review summarized the underlying dynamic bluntly: this was “a business more scared of losing a client than addressing the risks that client was bringing to the bank.” The report also found the Prime Services risk team itself was understaffed, had failed to replace departing senior risk staff, and lacked leadership experience—the second line, quite literally, hollowed out from within.[13]

4. Danske Bank Estonia (2007–2018)—When the Second Line Covers Its Own Mistakes

Danske Bank’s Estonian branch moved an estimated $230 billion in suspicious transactions, much of it linked to Russia, between 2007 and 2015—one of the largest money-laundering cases in European history. It might never have come to light if not for Howard Wilkinson, a British trader who filed four internal whistleblower reports to the bank’s audit unit and Copenhagen management between 2013 and 2014.[14]

Wilkinson later testified before the Danish and European Parliaments that the bank had “deliberately ignored” his warnings and that an Estonia branch executive told him the bank was “not the police.” An internal Danske audit team eventually validated the substance of his concerns, yet the bank still failed to take meaningful action until the money-laundering scandal became public in 2018—four years later. As Wilkinson departed the bank, he was reportedly presented with a nondisclosure agreement. This is the sharpest version of the pattern this piece was asked to examine directly: not a second line that failed to notice a problem, but one that noticed, confirmed it internally, and chose containment over correction—protecting the institution’s narrative rather than fixing the underlying failure.[15]

5. Silicon Valley Bank (2023)—When Periodic Reviews Can’t Keep Up With Real-Time Risk

SVB failed in 36 hours following a bank run, but the vulnerabilities behind it built for years. The Federal Reserve’s own review, led by Vice Chair for Supervision Michael Barr, is remarkable for how directly a regulator indicted its own supervisory process: SVB’s board and management “failed to manage their risks,” Federal Reserve supervisors “did not fully appreciate the extent of the vulnerabilities” as the bank grew, and—critically—even when supervisors did identify problems, they “did not take sufficient steps to ensure that Silicon Valley Bank fixed those problems quickly enough.”[16]

The report also found that SVB itself had changed its own risk-management assumptions specifically to reduce how its interest rate risk was measured, rather than managing the underlying exposure—a second-line control quietly redefined until it stopped producing uncomfortable answers. Barr’s report is also a rare admission that periodic, point-in-time supervisory cycles are structurally too slow for a risk that can move at deposit-run speed; a regulator reaching the same conclusion this piece reaches about the second line more broadly.[17]

What Regulators Learned—And Where Their Own Findings Converge

The most useful evidence that this is a systemic problem, not a string of unrelated scandals, comes from the regulators themselves. On April 28, 2023, the Federal Reserve and the Federal Deposit Insurance Corporation (FDIC) each released their own self-critical report on the same weekend of bank failures—an unusually candid coincidence that let the two reports be read side by side.

The Fed’s report on SVB, discussed above, found that supervisors identified real vulnerabilities but did not escalate forcefully enough once they had. The FDIC’s own report on Signature Bank reached a strikingly similar structural conclusion through a completely separate investigation: the bank’s failure was rooted in poor management, but the report also found that FDIC examiners had downgraded Signature’s liquidity rating as early as 2017 while its overall composite rating stayed at a healthy “2-Satisfactory” for six more years—a gap between what examiners were seeing and what the supervisory rating actually communicated.[18]

The U.S. Government Accountability Office (GAO) took a further step by reviewing both agencies together rather than separately. It concluded that this supports the main argument of this piece concerning federal banking regulation: the Federal Reserve and FDIC “identified numerous concerns at the banks as early as 2018, but did not issue enforcement actions.” Additionally, the GAO pointed out that the Federal Reserve’s “procedures for moving from a lower-level concern to an enforcement action often weren’t clear or specific.” This indicates that a regulator, assessing itself, independently recognizes the same core idea discussed here: identifying a risk is not the same as forcing a change. An institution can recognize risks on a large scale for years without reliably enforcing change.[19]

Read together with the NIST AI Risk Management Framework’s push for governance built around measurable, continuous risk assessment rather than static control catalogs, and the IIA’s 2020 shift away from purely defensive framing, a consistent regulatory direction emerges across otherwise unrelated bodies: less faith in point-in-time review, more emphasis on forcing identified risk into actual remediation, and explicit skepticism that documentation volume is a reliable proxy for safety. None of these bodies coordinated with each other. They arrived at overlapping conclusions anyway, because they were all looking at the same underlying failure pattern from different angles.[20],[21]

Figure 1. Most second-line functions do not lack activity—they sit in the high-activity, low-reduction quadrant, producing evidence of governance without changing risk outcomes.

The 2LOD governance trap and its four related boxes.

The Part Nobody Puts in the Org Chart: Tenure, Ego, and Internal Turf Wars

Every case above shares a dynamic that rarely appears in a governance framework diagram but shows up in nearly every post-mortem: the people closest to a mistake are often the ones best positioned to prevent its discovery, and organizational tenure tends to make that worse rather than better.

Long-tenured leaders accumulate something more dangerous than complacency—they accumulate authorship. A risk model, a sales program, a client relationship built over a decade is not just a business asset to the person who built it; it is proof of their own judgment. Wells Fargo’s Board Report describes exactly this pattern in Carrie Tolstedt, who had run the Community Bank for years and treated challenges to the sales model as challenges to her track record, not as useful information. John Stumpf’s decades at the company produced the same effect at the top: reliance on “decades of success” became a reason to discount new evidence rather than investigate it.[22]

Ego compounds this in a specific and predictable way inside the second line itself: once a risk function has signed off on something—approved a model, cleared a client, blessed a control—reversing that judgment later means admitting the earlier review was wrong. The Credit Suisse-Archegos investigation found that risk staff who wanted to tighten margin requirements were overruled by colleagues managing the client relationship, who prioritized the commercial relationship over the escalation. That is not a hypothetical about incentives; it is a documented instance of one part of the organization protecting a prior decision instead of correcting course.[23]

The most direct evidence of internal fighting to cover mistakes is Danske Bank. Wilkinson’s own account describes a bank that did not simply fail to notice a problem—it received internal confirmation that the problem was real, from its own audit function, and chose a non-disclosure agreement and years of silence over disclosure and remediation. That is not a control gap. It is a second line, or the executives who supervise it, actively managing the appearance of the problem rather than the problem itself—the containment instinct that shows up whenever an admission of error threatens a career, a bonus cycle, or a carefully maintained reputation.[24]

A second line that cannot survive telling the truth about its own prior mistakes will eventually stop looking for them.

None of this requires malice to be dangerous. Most of the people in these stories were not villains; they were professionals whose incentives, tenure, and self-image quietly bent the direction of ambiguous judgment calls toward “this is probably fine.” A modern second line has to be designed with the explicit assumption that this bending will happen—through rotation of long-tenured reviewers, external validation of internally cleared decisions, and protected channels for escalation that do not depend on the goodwill of the person whose earlier judgment is being questioned.

Governance Activity Is Not the Same as Risk Reduction

Every case study mentioned earlier successfully passed a compliance test before turning into a scandal. This is the key point repeatedly emphasized here: governance that merely shows evidence of compliance is different from governance that genuinely reduces risk. An organization can generate a lot of documentation proving compliance but still fall short in actually altering risk outcomes.

Evidence-of-compliance governance is legible, defensible in an exam, and relatively cheap to produce: a signed attestation, a completed checklist, a policy that has been “reviewed and approved.” Outcome-based governance is harder and more expensive: independently tested controls, risk metrics tied to actual loss experience, escalation paths that get used even when the news is bad. The first kind of governance protects the organization in an audit. The second kind protects the organization in a crisis. Wells Fargo, Credit Suisse, and Danske Bank all had abundant supplies of the first and a critical shortage of the second.

Figure 2. Modernizing the second line means shifting the underlying operating model, not just increasing the volume of existing activity.

Two columns showing the legacy model of checkbox compliance and the new model of continuous risk governance.

What a Modern Second Line Actually Looks Like

None of this argues for a weaker second line—every case study here shows the cost of that. It argues for a fundamentally different operating model, one that a growing body of regulatory guidance and industry practice is already pointing toward.

Risk-Based, Not Checklist-Based

Oversight intensity should scale with actual risk and complexity, not with how many items happen to be on a standard control list. A stable, well-understood process and a novel AI model deployed into a regulated decision workflow should never receive the same depth of review simply because both appear as line items on the same checklist.

Continuous Monitoring, Not Periodic Snapshots

The Barr report on SVB is itself an argument for this shift: point-in-time exams cannot keep pace with risks—interest rate exposure, deposit concentration, model drift—that can move materially between review cycles. Where technology allows it, continuous, automated monitoring should replace calendar-driven review as the default, with periodic deep-dives reserved for the risks continuous monitoring cannot yet see.

Evidence Over Attestation

Self-reported control effectiveness should be treated as a starting hypothesis, not a conclusion. Independent data validation—sampling actual transactions, actual model outputs, actual system logs—is more expensive than collecting a signature, and it is the only version of assurance that would have caught what self-attestation missed at Danske Bank.

Genuine Business and Technology Fluency

A second line cannot challenge what it does not understand. This means recruiting and developing risk professionals with real technical depth—in derivatives, in cloud architecture, in machine learning—rather than treating the second line as a generalist compliance career track. JPMorgan’s risk managers not knowing what the CIO’s synthetic credit portfolio actually did is the clearest cautionary tale on this point.

Escalation That Survives Internal Politics

Escalation paths need to be structurally protected from the relationship dynamics that killed escalation at Credit Suisse and Danske Bank—which means routing serious concerns to a level of the organization with no commercial stake in the outcome, and protecting the people who raise them, not just on paper but in how the organization actually treats them afterward.

Outcome-Based Metrics

A second line’s effectiveness should be measured by risk events avoided, losses prevented, and issues resolved before they compound—not by the number of reviews completed, policies published, or meetings held. Volume metrics are easy to game and easy to satisfy without changing anything; outcome metrics are harder to fake.

Real Oversight of AI, Cloud, and Third Parties

Emerging-technology governance needs its own competency track within the second line, built around frameworks purpose-designed for these risks—NIST’s AI Risk Management Framework, cloud shared-responsibility models, and structured third-party risk programs—rather than an attempt to stretch legacy control catalogs over technology they were never built to evaluate.[25]

Clear Accountability Between the First and Second Lines

Wells Fargo’s decentralized risk structure, with control functions embedded inside and reporting up through the business they were meant to oversee, shows what happens when the line between “owns the risk” and “challenges the risk” blurs. Modern governance requires those roles to remain organizationally and, where possible, financially distinct—precisely what the OCC’s heightened standards were written to enforce.[26]

Constructive Challenge, Not a Permanent Bottleneck

None of the above is a case for more friction everywhere. A second line that slows every decision equally will be resented, routed around, and eventually ignored—which is its own form of failure. The goal is targeted friction: fast, low-touch review for well-understood, lower-risk activity, and genuinely rigorous, well-resourced challenge concentrated on the decisions that could actually sink the institution.

Conclusion: Measuring the Right Thing

Return to Silicon Valley Bank’s 31 unaddressed supervisory warnings. Every one of them was, in a narrow sense, evidence that governance was happening: someone had identified a risk, written it down, and tracked it. And every one of them failed to change what actually happened to the bank. That is the second line’s central modern challenge, in miniature.

None of this is solvable by better metrics alone. Every case study in this piece also involved someone for whom the honest answer was personally expensive—a bonus, a reputation, a decade of authorship over a program now under question. A second line rebuilt around outcome-based measurement but layered on top of the same career incentives that rewarded Carrie Tolstedt’s silence and cost Howard Wilkinson his job will simply produce more sophisticated versions of the same evasions. The measurement has to change. So does the price of telling the truth.

It is also worth taking seriously what the regulators’ own convergence implies about where this is heading. The Federal Reserve, the FDIC, the GAO, NIST, and the IIA did not coordinate their findings—they arrived at the same conclusion independently, from different mandates, within the same few years. Convergence without coordination is usually a sign that a standard is hardening, not that a moment is passing. Institutions that treat this argument as a post-SVB overreaction, rather than the new baseline expectation, are likely to be rereading their own supervisory letters in a few years and wondering how they missed it.

The stakes of getting this right are also rising, not leveling off. Every failure examined here involved a risk that a sufficiently empowered reviewer could, in principle, still understand—a trading book, a sales incentive, a margin call. The AI models now moving into underwriting, claims, and credit decisions will not extend that same courtesy; their behavior can shift with a single retraining cycle in ways no annual attestation was ever built to catch. A second line that could not reliably catch a mismarked trading book will not reliably catch a model that has quietly drifted—not without first becoming the kind of second line this piece has been describing.

The organizations in this piece did not fail because nobody was watching. They failed because watching, on its own, was mistaken for managing. A modern second line has to be judged by a harder, more honest standard than whether the reviews got done: whether the risks that mattered actually got smaller. Everything else—the frameworks, the dashboards, the attestations—is only useful to the extent it serves that one outcome. Where it doesn’t, it is not governance. It is just paperwork with better branding.

Endnotes

[1]  Board of Governors of the Federal Reserve System, Review of the Federal Reserve’s Supervision and Regulation of Silicon Valley Bank (Washington, DC: Federal Reserve, April 28, 2023), https://www.federalreserve.gov/publications/files/svb-review-20230428.pdf; “Fed’s Barr: ‘Weaknesses in Supervision and Regulation Must Be Fixed,’” American Banker, April 28, 2023, https://www.americanbanker.com/news/feds-barr-weaknesses-in-supervision-and-regulation-must-be-fixed.

[2] The Institute of Internal Auditors, The IIA’s Three Lines Model: An Update of the Three Lines of Defense (Lake Mary, FL: IIA, July 2020), https://www.theiia.org/globalassets/documents/resources/the-iias-three-lines-model-an-update-of-the-three-lines-of-defense-july-2020/three-lines-model-updated-english.pdf.

[3]  Office of the Comptroller of the Currency, OCC Guidelines Establishing Heightened Standards for Certain Large Insured National Banks, Insured Federal Savings Associations, and Insured Federal Branches, 12 C.F.R. Part 30, Appendix D (2014); Board of Governors of the Federal Reserve System, “Supervisory Guidance on Model Risk Management,” SR Letter 11-7 (Washington, DC: Federal Reserve, April 4, 2011).

[4]  “IIA Unveils New Three Lines Model,” Radical Compliance, July 22, 2020, https://www.radicalcompliance.com/2020/07/22/iia-unveils-new-three-lines-model/.

[5]  “Howard Wilkinson,” Kohn, Kohn & Colapinto Whistleblower Case Archive, accessed August 2026, https://kkc.com/whistleblower-case-archive/howard-wilkinson/.

[6]  National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0) (Gaithersburg, MD: U.S. Department of Commerce, January 26, 2023), https://doi.org/10.6028/NIST.AI.100-1.

[7]  U.S. Senate Permanent Subcommittee on Investigations, Committee on Homeland Security and Governmental Affairs, JPMorgan Chase Whale Trades: A Case History of Derivatives Risks and Abuses (Washington, DC: U.S. Senate, March 15, 2013), https://www.hsgac.senate.gov/subcommittees/investigations/library/files/report-jpmorgan-chase-whale-trades-a-case-history-of-derivatives-risks-and-abuses-march-15-2013/.

[8]  JP Morgan Chase Whale Trades: A Case History of Derivatives Risks and Abuses, summarized in Demos, https://www.demos.org/research/jp-morgan-chase-whale-trades-case-history-derivatives-risks-and-abuses.

[9]  Independent Directors of the Board of Wells Fargo & Company, Sales Practices Investigation Report (San Francisco: Wells Fargo & Company, April 10, 2017), https://lowellmilkeninstitute.law.ucla.edu/wp-content/uploads/2018/01/WF-Board-Report.pdf.

[10]  Wells Fargo Newsroom, “Wells Fargo Board Releases Findings of Independent Investigation of Retail Banking Sales Practices and Related Matters,” press release, April 10, 2017, https://newsroom.wf.com/news-releases/news-details/2017/Wells-Fargo-Board-Releases-Findings-of-Independent-Investigation-of-Retail-Banking-Sales-Practices-and-Related-Matters/default.aspx.

[11]  “Summary of the Report of the Independent Directors of Wells Fargo & Company into Sales Practices,” Lexology, October 11, 2017, https://www.lexology.com/library/detail.aspx?g=9b82dbcc-146d-4921-847c-526ccbf505a2; Brad S. Karp, Roberto J. Gonzalez, and Vikas Desai, “Lessons Learned from the Wells Fargo Sales Practices Investigation Report,” Harvard Law School Forum on Corporate Governance, April 22, 2017, https://corpgov.law.harvard.edu/2017/04/22/lessons-learned-from-the-wells-fargo-sales-practices-investigation-report/.

[12]  Credit Suisse Group AG, Report of the Special Committee of the Board of Directors of Credit Suisse Group Regarding Archegos Capital Management, prepared by Paul, Weiss, Rifkind, Wharton & Garrison LLP (July 29, 2021), as reported in “Credit Suisse Publishes Independent Review of Archegos Losses,” Paul, Weiss news release, July 29, 2021, https://www.paulweiss.com/practices/litigation/internal-investigations/news/credit-suisse-publishes-independent-review-of-archegos-losses.

[13]  “Unpacking the Report on Credit Suisse’s Archegos Disaster,” Euromoney, July 29, 2021, https://www.euromoney.com/article/28usrfe6tdwq9fkpayosg/capital-markets/unpacking-the-report-on-credit-suisses-archegos-disaster/; “Credit Suisse and the Archegos Collapse – Lessons in Risk Management and Governance for All,” BDO, February 21, 2025, https://www.bdo.co.uk/en-gb/insights/industries/financial-services/credit-suisse-and-the-archegos-collapse-lessons-in-risk-management-and-governance.

[14]  “Whistleblower in Danish Banking Scandal: Bank Ignored Me,” Associated Press via Seattle Times, November 19, 2018, https://www.seattletimes.com/business/whistleblower-in-danish-banking-scandal-bank-ignored-me/; “Danske Bank Money Laundering Scandal – Tip of the Icebergs,” National Law Review, accessed August 2026, https://natlawreview.com/article/danske-bank-money-laundering-scandal-tip-icebergs.

[15]  “Howard Wilkinson,” Kohn, Kohn & Colapinto Whistleblower Case Archive, accessed August 2026, https://kkc.com/whistleblower-case-archive/howard-wilkinson/; “Thanks to Danske Bank Whistleblower, SEC Sets Aside $178 Million for Harmed Investors,” Whistleblower Blog, April 4, 2023, https://whistleblowersblog.org/corporate-whistleblowers/sec-whistleblowers/thanks-to-danske-bank-whistleblower-sec-sets-aside-178-million-for-harmed-investors/.

[16]  Board of Governors of the Federal Reserve System, Review of the Federal Reserve’s Supervision and Regulation of Silicon Valley Bank, i-iii; “Federal Reserve Board Announces the Results from the Review of the Supervision and Regulation of Silicon Valley Bank,” press release, April 28, 2023, https://www.federalreserve.gov/newsevents/pressreleases/bcreg20230428a.htm.

[17]  Board of Governors of the Federal Reserve System, Review of the Federal Reserve’s Supervision and Regulation of Silicon Valley Bank, 3.

[18]  Federal Deposit Insurance Corporation, FDIC’s Supervision of Signature Bank (Washington, DC: FDIC, April 28, 2023), https://www.fdic.gov/news/press-releases/2023/pr23033a.pdf; “FDIC Signature Bank Report Summary,” prepared for the U.S. House Committee on Financial Services, May 2, 2023, https://financialservices.house.gov/uploadedfiles/2023.05.02_-_fdic_signature_bank_report_summary_final.pdf.

[19]  U.S. Government Accountability Office, Bank Supervision: More Timely Escalation of Supervisory Action Needed, GAO-24-106974 (Washington, DC: GAO, 2024), https://www.gao.gov/products/gao-24-106974.

[20]  National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0) (Gaithersburg, MD: U.S. Department of Commerce, January 26, 2023), https://doi.org/10.6028/NIST.AI.100-1.

[21]  The Institute of Internal Auditors, The IIA’s Three Lines Model: An Update of the Three Lines of Defense (Lake Mary, FL: IIA, July 2020), https://www.theiia.org/globalassets/documents/resources/the-iias-three-lines-model-an-update-of-the-three-lines-of-defense-july-2020/three-lines-model-updated-english.pdf.

[22]  Independent Directors of the Board of Wells Fargo & Company, Sales Practices Investigation Report.

[23]  “Credit Suisse and the Archegos Collapse,” BDO; “Unpacking the Report on Credit Suisse’s Archegos Disaster,” Euromoney.

[24]  “Whistleblower in Danish Banking Scandal: Bank Ignored Me,” Seattle Times; “Howard Wilkinson,” Kohn, Kohn & Colapinto.

[25]  National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0).

[26]  Office of the Comptroller of the Currency, OCC Guidelines Establishing Heightened Standards, 12 C.F.R. Part 30, Appendix D; Independent Directors of the Board of Wells Fargo & Company, Sales Practices Investigation Report.

What the rise, fall, and rapid rebirth of eXch tells us about the real shape of crypto crime in 2026

Every few months, a crypto exchange gets “shut down.” Headlines run. LinkedIn fills with hot takes. And then, quietly, the money keeps moving. That’s the pattern I want to walk through here—not as a hypothetical, but as a documented case, built on the work of the two firms that actually trace this money for a living: TRM Labs and Chainalysis.

Across my career in technology governance, cyber risk, enterprise transformation, and technology leadership, this particular case study has become one of the clearest illustrations of a lesson every risk leader eventually learns the hard way: shutting down a bad actor is not the same as dismantling the capability behind it. The organization goes away. The infrastructure, the liquidity, and the operators very often do not.

The Exchange That Wouldn’t Stay Dead:

eXch was a no-questions-asked crypto swap service. No identity verification, no meaningful compliance program—and it marketed that absence as a feature, branding itself a “privacy project” rather than what regulators would call it: a gap in the system, wide open and waiting to be used.

That gap became national news in February 2025, when North Korea’s Lazarus Group pulled off the largest crypto theft in history, stealing roughly $1.4 to $1.5 billion in Ethereum from the Bybit exchange.1 Bybit and independent investigators—including Elliptic, TRM Labs, and researcher ZachXBT—all pointed to the same off-ramp: eXch allegedly helped launder more than $90 million of the stolen funds.2

eXch’s owner, publicly known only as “Johann Roberts,” denied it, then partially admitted it, then blamed a slow compliance data feed. For what it’s worth, I went looking for a verified identity behind that name while researching this piece. I couldn’t find one. Treat it as an alias until proven otherwise.

In April 2025, eXch announced it was shutting down—citing, of all things, a DOJ whistleblower and a “transatlantic law enforcement operation.” Here’s the part almost nobody covered: it didn’t actually stop. TRM Labs found that eXch pulled its public-facing website but kept serving business partners through an API, with the same laundering fingerprints continuing right past its own announced shutdown date.3

This Isn’t One Bad Exchange—It’s a Lineage:

If eXch feels like an isolated case, look at what happened to Garantex, the Russian exchange first sanctioned in 2022 for laundering funds tied to darknet markets and ransomware groups like Conti and Hydra. Law enforcement finally seized its infrastructure in March 2025, after the platform had processed an estimated $96 billion in transactions since 2019, a substantial share of it tied to ransomware, darknet-market, and other criminal activity.4

What happened next is the whole point of this article. Garantex didn’t disappear. It became Grinex—same liquidity, same users, same money, new name. Chainalysis and TRM then traced the same pattern into ABCeX and its rebrand AEXBit, which share identical backend infrastructure and hot wallets with their predecessors; into the A7/A7A5 ruble-backed payment network, which has moved more than $93.3 billion in on-chain volume and counting; and into Heleket, a “new” service that received its opening liquidity directly from Garantex’s own wallets.5

TRM’s own assessment, stated plainly in its 2026 crypto crime report, is that this wave of rebrands is likely coordinated—a deliberate attempt to keep Russia’s crypto liquidity flowing while insulating the actual operators from further sanctions.6 For what it’s worth, Grinex itself went dark in April 2026 after a $13.7 million cyberattack it blamed, without evidence, on Western intelligence agencies.7 I’d bet money there’s already a successor standing by.

The Bigger Story Nobody’s Talking About Enough:

Here’s what I think most crypto-crime coverage still misses: individual rogue exchanges, however dramatic the headline, are no longer the main event.

Both TRM and Chainalysis now point to something structurally different—Chinese-language money laundering networks, or CMLNs. In 2025 alone, these networks moved an estimated $16.1 billion, roughly $44 million a day, across nearly 1,800 active wallets. That’s not a typo: Chainalysis measured CMLN growth at roughly 7,325 times the growth rate of illicit inflows to centralized exchanges since 2020.8

The anchor of this ecosystem is Huione Group, a Cambodia-based conglomerate that processed more than $98 billion in total crypto inflows between August 2021 and January 2025, over $4 billion of it confirmed illicit. In October 2025, the U.S. Treasury’s FinCEN designated Huione under Section 311 of the USA PATRIOT Act as a primary money laundering concern. Huione is also directly tied to Prince Group, the Cambodia-based criminal network behind a sprawling web of scam compounds across Southeast Asia.9

Why does this matter more than another exchange takedown? Because CMLNs aren’t one company you can seize. They’re a marketplace—fragmentation services, OTC desks, and “guarantee” platforms like Huione and Xinbi that connect buyers and sellers of laundering capacity, often without the platform operators ever directly touching the illicit funds themselves. Sanction one vendor, and the rest of the marketplace barely notices.10

Ransomware Isn’t Slowing Down—It’s Diversifying:

Data-leak-site-claimed ransomware incidents grew 50 percent year-over-year in 2025, reaching an all-time high even as enforcement activity intensified.11 The Ransomware-as-a-Service market has also fragmented, with some trackers counting as many as 85 active independent extortion groups—a more decentralized field that’s harder to monitor collectively, even as individual groups’ laundering patterns become easier to fingerprint on-chain.12

Separately, broader Chainalysis research on illicit crypto flows (not specific to ransomware) points to a shift in final-stage laundering toward exchanges with little to no know your customer (KYC) verification, with no-KYC exchange usage up 82 percent and usage of “guarantee” aggregators such as Tudou Danbao up 87 percent.13 Whether North Korean state actors rely on these no-KYC exchanges less than independent cybercriminals do—running a more specialized pipeline through Chinese money-laundering networks and bridge protocols instead—is a plausible pattern given DPRK’s well-documented use of dedicated laundering infrastructure. But it isn’t a claim I found directly confirmed in the sources reviewed for this piece, so I’m flagging it as a reasonable hypothesis rather than an established fact.

Enforcement has also started targeting the infrastructure layer itself, not just individual exchanges. In February 2025, the U.S., U.K., and Australia jointly sanctioned Zservers, a Russian bulletproof-hosting provider tied to ransomware operations including LockBit; Chainalysis data shows Zservers funneled at least $5.2 million through high-risk channels, including the sanctioned exchange Garantex.14 OFAC separately sanctioned Aeza Group, another Russian bulletproof host, in July 2025—though, notably, that action does not appear to have included the U.K. and Australia as co-sanctioning parties.15

What This Actually Means:

If you take one thing from this, let it be this: the “shut it down” model of enforcement works—temporarily. eXch kept running through its own back door. Garantex became Grinex became ABCeX became AEXBit. The harder, more consequential fight is against the marketplace model itself—the CMLNs, the guarantee platforms, and the hosting infrastructure underneath all of it—which doesn’t have one throat to choke.

The good news, and it’s a real one, is that blockchain transparency remains investigators’ structural advantage. The same on-chain fingerprinting—shared wallets, co-spending patterns, infrastructure overlap—that unmasked ABCeX as a Garantex clone will eventually do the same to whatever comes after Grinex, and whatever comes after that.

This case study reflects the kind of governance-under-adversarial-pressure challenge I spend a lot of time researching and writing about: how do we design governance, oversight, and risk management frameworks for ecosystems that are deliberately engineered to evade them? Answering that will take a coordinated, multi-layered response—end-to-end mapping of cryptocurrency transaction chains, stronger Know Your Customer and Anti-Money Laundering controls, deeper multinational cooperation among regulators and law enforcement, more rigorous misuse-case modeling to anticipate adversarial behavior, and broader, faster identification and blacklisting of the high-risk exchanges, wallets, and tokens that keep facilitating illicit finance long after their predecessors are supposedly gone.

Endnotes:

1. TRM Labs, “2026 Crypto Crime Report” (TRM Labs, 2026), https://www.trmlabs.com/reports-and-whitepapers/2026-crypto-crime-report.

2. Decrypt, The Block, and CryptoRank.io, contemporaneous news coverage of the Bybit hack and eXch’s role in laundering stolen funds, February–March 2025.

3. TRM Labs, “eXch Remains Active Despite Shutdown: How the Bybit Hack-Linked Exchange Continues to Enable Laundering of CSAM Funds” (TRM Labs Blog, May 2, 2025), https://www.trmlabs.com/resources/blog.

4. Chainalysis, “OFAC Sanctions Tracker: How Sanctions Impact Crypto Crime” (Chainalysis Blog), https://www.chainalysis.com/blog/ofac-sanctions/.

5. TRM Labs, “2026 Crypto Crime Report.”

6. TRM Labs, “2026 Crypto Crime Report.”

7. TRM Labs, “2026 Crypto Crime Report.”

8. Chainalysis, “The Chinese-Language Underground Crypto Money Laundering Ecosystem” (Chainalysis Blog, January 27, 2026), https://www.chainalysis.com/blog/2026-crypto-money-laundering/.

9. Chainalysis, “Crypto Sanctions: 2026 Crypto Crime Report” (Chainalysis Blog, 2026), https://www.chainalysis.com/blog/crypto-sanctions-2026/.

10. Chainalysis, “The Chinese-Language Underground Crypto Money Laundering Ecosystem.”

11. Chainalysis, “Crypto Ransomware: 2026 Crypto Crime Report” (Chainalysis Blog, March 4, 2026), https://www.chainalysis.com/blog/crypto-ransomware-2026/.

12. Chainalysis, “Crypto Ransomware: 2026 Crypto Crime Report.”

13. Chainalysis, “2025 Crypto Theft Reaches $3.4 Billion” (Chainalysis Blog, December 18, 2025), https://www.chainalysis.com/blog/crypto-hacking-stolen-funds-2026/.

14. Chainalysis, “OFAC Sanctions Tracker.”

15. Chainalysis, “OFAC Sanctions Tracker.”

From Mythos to Mechanics: How Frontier AI Policy Shifts Are Rewriting Enterprise Governance

Fig. 1. Infographic Title: From Mythos to Mechanics, Generic/Rights Free, Jeremy Swenson, 2026.

The recent decision to lift restrictions on advanced model deployments from Anthropic represents more than a policy adjustment or regulatory softening. It signals a deeper transition in how frontier AI systems are being treated by governments, enterprises, and oversight bodies: not as static technologies that can be approved or denied once, but as dynamic systems whose behavior, risk profile, and operational impact evolve continuously over time. The significance of this shift is not fully captured in headlines focused on access restoration. Instead, it lies in the subtle but consequential rebalancing of responsibility—from centralized gatekeepers to distributed operators embedded inside enterprise systems.¹

This shift is unfolding alongside a broader geopolitical reclassification of AI systems as controlled strategic infrastructure. As reported by Forbes, the U.S. administration recently lifted export controls on Anthropic’s Mythos 5 and Fable 5 models following a period of heightened national security concern and temporary suspension of access.² Reuters similarly reports that this pattern reflects a new regulatory rhythm: rapid restriction, negotiated mitigation, and conditional restoration rather than permanent prohibition.³ These oscillations are not anomalies—they are becoming the governing structure itself.

At the same time, this policy volatility is occurring against a broader global consolidation of scientific consensus on AI risk. The International AI Safety Report 2026 emphasizes that AI capabilities are advancing faster than safety practices and institutional governance can reliably track.⁴ The report highlights that frontier systems are increasingly autonomous in workflow execution, capable of multi-step reasoning, and difficult to evaluate using static benchmarks alone.⁴ Importantly, it concludes that governance systems are now largely reactive rather than anticipatory, with safety controls lagging behind deployment realities.⁴

More critically, the report identifies a structural mismatch between capability growth and institutional oversight capacity. It notes that frontier AI systems are not improving linearly, but through discontinuous capability jumps driven by scaling, tool use, and inference-time computation.⁴ This creates evaluation blind spots where systems appear safe in testing environments but exhibit materially different behaviors once deployed.

At the core of this transition is a change in what “control” means. Earlier governance models around frontier AI were built on relatively familiar assumptions drawn from software regulation, export controls, and cloud security certification regimes. If a system passed evaluation thresholds, it could be deployed; if it failed, it was restricted or segmented. That logic worked reasonably well when system behavior was stable, deterministic, and tightly scoped. However, frontier AI systems increasingly violate those assumptions. Their outputs are probabilistic, their capabilities shift with prompting techniques, and their risk surfaces expand as they are embedded into broader enterprise ecosystems.⁴

The International AI Safety Report explicitly warns that pre-deployment evaluation alone is insufficient for safety assurance, particularly in systems with tool access, memory, or agentic capabilities.⁴ It recommends continuous post-deployment monitoring as a core governance requirement rather than an optional enhancement.

What emerges instead is a governance posture that resembles continuous assurance rather than static certification. Access becomes conditional, contextual, and dynamic. The report emphasizes the importance of real-world monitoring systems capable of detecting behavioral drift and emergent capabilities after deployment, reinforcing the idea that governance must move into runtime systems rather than remain in pre-release gates.⁴

As these systems return to broader availability, another structural shift becomes visible: the migration of governance responsibility away from regulators and model developers and into enterprise architecture itself. Historically, AI safety and capability constraints were enforced upstream. That separation is eroding rapidly.

Reuters reporting on export control reversals underscores how government decisions are now shaping model availability in near real time, creating a governance environment defined by rapid policy iteration rather than stable regulation.³ Meanwhile, the International AI Safety Report highlights that this instability is mirrored in deployment environments, where inconsistent governance maturity across organizations and jurisdictions creates asymmetric risk exposure.⁴

This downstream shift places new pressure on enterprise functions simultaneously. Cybersecurity teams must model AI behavior as part of threat landscapes. Third-party risk teams must evaluate emergent model behavior, not just vendor controls. Data governance teams must account for indirect leakage pathways through prompts and outputs. Product teams now actively shape risk through interface design, workflow orchestration, and agentic integration choices.

The International AI Safety Report reinforces this transformation by documenting how frontier AI systems are increasingly deployed in agentic configurations, where models execute multi-step tasks, use external tools, and operate with partial autonomy.⁴ These systems blur the line between software and actor, fundamentally altering traditional control assumptions.

Compounding this challenge is the fact that frameworks such as NIST AI RMF and ISO/IEC 42001 assume bounded, testable system behavior. The International AI Safety Report directly challenges this assumption, noting that emergent behaviors often appear only after real-world deployment under complex and shifting conditions.⁴

In cybersecurity contexts, this shift is already visible. The report documents growing evidence of AI systems being used for vulnerability discovery, phishing automation, and large-scale social engineering.⁴ These are not hypothetical risks—they are operational realities emerging in parallel with deployment expansion.

One of the most important but least discussed consequences of this shift is the transformation of AI systems into dynamic or “living” risk surfaces. Unlike traditional software, which changes primarily through version updates, AI systems can change behavior based on context, tool access, and input distribution.⁴ A retrieval-augmented system, for example, may introduce entirely different risk profiles than a base model operating in isolation.

The International AI Safety Report characterizes this as a form of non-stationary risk, where the system being evaluated is not stable over time.⁴ This fundamentally breaks traditional assumptions of static risk modeling. This introduces a shift in security thinking itself. Organizations must move from vulnerability-centric models to behavior-centric models. Weaknesses are no longer purely code-based—they are emergent, interaction-driven, and context-dependent.⁴

From a strategic perspective, the most important implication of expanded frontier model availability is not technical—it is competitive. Organizations that successfully integrate continuous AI governance into operational systems will deploy faster, scale broader, and take more strategic risk safely. Those that treat governance as a bottleneck will slow precisely when speed becomes advantage.

The International AI Safety Report explicitly identifies governance maturity and institutional readiness as key limiting factors in safe AI adoption at scale.⁴ This makes governance capability—not model access—the primary differentiator in enterprise AI maturity.

The next evolution of this landscape is the emergence of an AI control plane architecture: a unified layer that governs model access, routing, policy enforcement, behavioral monitoring, and auditability across environments. In this model, governance becomes infrastructure rather than documentation.

This represents a deeper shift in control theory itself. Static rules give way to continuous negotiation between capability and constraint. Periodic review gives way to continuous observation. Tools become ecosystems.

The lifting of restrictions on advanced models is therefore not an endpoint, but an early signal of a broader transition toward normalized frontier AI deployment under continuous governance conditions. The International AI Safety Report makes clear that this transition is already underway, driven by accelerating capabilities, uneven institutional readiness, and widening oversight gaps.⁴ The organizations that adapt early will not simply comply with this environment—they will define it.

Mitigation & Operational Readiness Playbook:

To translate the governance shift described in this analysis into actionable enterprise capability, organizations must move beyond fragmented controls and toward continuous, behavior-aware AI governance. The first priority is implementing continuous AI behavior monitoring. Rather than treating model evaluation as a pre-deployment checkpoint, enterprises need to track model outputs over time to detect drift, anomalies, and unexpected capability emergence. This effectively reframes AI telemetry as a core security signal, similar in importance to identity logs or network activity, rather than a secondary analytics layer.

In parallel, organizations must establish AI-specific threat modeling practices. Traditional cybersecurity frameworks are insufficient on their own because they assume deterministic system behavior. AI systems introduce new threat vectors such as prompt injection, tool misuse, data exfiltration through outputs, and unintended agentic behavior. These must be explicitly integrated into threat models, extending existing methodologies to account for probabilistic and context-sensitive system responses.

A critical structural requirement is the deployment of an AI control plane architecture. This layer should centralize governance across all models, vendors, and deployment environments. It should enforce consistent policy controls governing access, tool usage, and data exposure while enabling dynamic routing of model requests based on sensitivity, risk tier, and operational context. Without this unified control layer, organizations will struggle to maintain coherent governance across increasingly distributed AI systems.

Data boundary enforcement for large language model interactions also becomes essential. Sensitive information must be prevented from entering prompts unless properly classified and authorized, and all prompt and response flows should be logged to ensure auditability. In practice, this requires extending data loss prevention (DLP) concepts into generative AI pipelines, where the boundary between input, processing, and output is far more fluid than in traditional systems.

Organizations should also adopt post-deployment evaluation frameworks that move beyond static approval cycles. Instead of relying on one-time certification, AI systems must undergo continuous reassessment through red-teaming, adversarial testing, and behavior evaluation in production-like conditions. This allows organizations to identify emergent risks that only appear after models are exposed to real-world inputs, evolving workflows, and integrated toolchains.

Third-party risk management functions must also evolve. Vendor assessment can no longer focus solely on security posture, compliance checklists, or infrastructure controls. It must incorporate behavioral risk—how models actually perform once deployed in dynamic environments. This includes understanding update cycles, tool integrations, and the degree of transparency vendors provide around model behavior and safety limitations.

Agentic workflows represent another critical area of hardening. As models increasingly perform multi-step tasks and interact with external systems, organizations must enforce least-privilege principles on tool access and require human-in-the-loop controls for high-risk actions. These workflows should also be fully logged and treated as security-relevant events, enabling retrospective analysis of autonomous or semi-autonomous decision paths.

At a structural level, AI governance ownership must be elevated to the architectural tier of the enterprise. Responsibility should not be fragmented across cybersecurity, compliance, and product teams, but instead unified within enterprise architecture or security engineering functions that can enforce consistent governance patterns across systems. This alignment is necessary to avoid gaps created by siloed decision-making in highly interconnected AI environments.

Finally, organizations must develop dedicated AI incident response capabilities. These playbooks should define clear escalation paths for model misuse, anomalous behavior, or data leakage events involving AI systems. They should also include operational mechanisms for rapid rollback of model versions, disabling of tool integrations, and containment of affected workflows. In an environment where AI systems are continuously evolving, response speed becomes a critical determinant of organizational resilience.

Endnotes:

  1. Anthropic, frontier model deployment and safety policy communications, 2026.
  2. Siladitya Ray, “Trump Administration Lifts Export Controls on Anthropic’s Mythos 5 and Fable 5 AI Models,” Forbes, July 1, 2026, https://www.forbes.com/sites/siladityaray/2026/07/01/trump-administration-lifts-export-controls-on-anthropics-mythos-5-and-fable-5-ai-models/.
  3. Reuters, “U.S. Lifts Export Controls on Frontier AI Models Following Security Review,” June 2026.
  4. International AI Safety Report, International AI Safety Report 2026 (London: DSIT and international expert consortium, 2026), https://internationalaisafetyreport.org/.
  5. Siladitya Ray, Forbes reporting on U.S. frontier AI policy shift and export control reversal, 2026.

From Mythos to Fable: What Business Leaders Must Learn from the New AI Governance Crisis

Anthropic Claud Mythos InfoSec Infographic, generic rights-free, 2026.

The Mythos Moment Just Got Bigger

A few weeks ago, Anthropic’s Mythos model was being celebrated as a breakthrough in AI-enabled cybersecurity. Reports suggested it could identify software vulnerabilities at unprecedented speed, accelerate remediation efforts, and potentially transform how organizations secure critical infrastructure. Some observers described it as one of the most capable cyber-focused AI systems ever developed.¹

Today, the conversation looks very different. The White House has ordered Anthropic to suspend access to Mythos 5 and Fable 5 for foreign nationals, citing national security concerns. Reports indicate that government officials were concerned not only about potential jailbreak vulnerabilities but also about the possibility that a China-linked group may have accessed the models.² The administration reportedly fears that advanced frontier models could be reverse-engineered through model distillation techniques, allowing strategic competitors to replicate key capabilities.³

Whether those concerns ultimately prove justified is almost beside the point. For business leaders, the real lesson is not about Anthropic. It is about the future of AI itself. The Mythos controversy signals that AI governance is rapidly evolving from a technology management issue into a business resilience, geopolitical risk, and digital supply chain challenge.⁴

The New Reality: AI Is Becoming Strategic Infrastructure

For years, organizations treated cloud computing as utility infrastructure. Access was largely assumed. The same cloud services were available whether you were in Minneapolis, Mumbai, London, or Singapore. Artificial intelligence appeared to be following a similar trajectory.

That assumption may no longer hold. The government’s restrictions on Mythos and Fable represent one of the first major examples of an advanced AI model being treated more like sensitive defense technology than commercial software.⁵ In effect, policymakers are beginning to ask whether some AI systems should be governed similarly to advanced semiconductors, encryption technologies, or military capabilities.

If that trend continues, organizations may find that access to critical AI capabilities can be restricted, delayed, licensed, monitored, or even revoked based on national security considerations.⁶ That should concern every executive currently building long-term business strategies around AI-enabled operations.

Why Business Leaders Should Care

Many executives may be tempted to dismiss the Mythos controversy as a dispute between Anthropic and the federal government. That would be a mistake. The more important story is not whether Anthropic’s safeguards were sufficiently robust or whether a jailbreak vulnerability actually existed. The real story is that organizations are rapidly becoming dependent on AI systems they do not own, cannot fully inspect, and may not always be able to access.

Imagine investing millions of dollars to integrate a frontier AI model into cybersecurity operations, software development, customer service, fraud detection, or enterprise decision-making, only to discover that access has been restricted due to a government directive, geopolitical concerns, export controls, or actions taken by the model provider itself. What appeared to be a stable technology platform can quickly become a strategic dependency.⁷

This is precisely why the Mythos situation deserves attention from boards, executives, and risk leaders. The disruption was not caused by a system outage, ransomware attack, or cloud failure. Instead, it emerged from a combination of national security concerns, policy decisions, and uncertainty surrounding advanced AI capabilities. These are risks that many organizations have not yet incorporated into their enterprise risk management programs.⁸

Historically, leaders worried about disruptions involving suppliers, cloud providers, telecommunications carriers, or critical software vendors. Frontier AI models now belong in that same category. Organizations increasingly depend upon a relatively small number of providers for advanced AI capabilities, creating concentration risks that may become more significant as AI becomes embedded in core business processes.⁹

Endnotes

  1. Anthropic, Project Glasswing Technical Findings, June 2026.
  2. Terrence O’Brien, “China May Have Accessed Mythos,” The Verge, June 14, 2026.
  3. Ibid.
  4. Kristian McCann, “Why the US Restricted Anthropic’s Mythos and Fable and What It Means for AI Access,” June 15, 2026.
  5. Hadas Gold, “Anthropic Suspends All Access to Mythos Model After US Government Bans Foreign Nationals Use,” CNN, June 13, 2026.
  6. McCann, “Why the US Restricted Anthropic’s Mythos and Fable.”
  7. Gold, “Anthropic Suspends All Access to Mythos Model.”
  8. O’Brien, “China May Have Accessed Mythos”; Gold, “Anthropic Suspends All Access to Mythos Model.”
  9. McCann, “Why the US Restricted Anthropic’s Mythos and Fable.”

The Mythos Moment: Why AI Cyber Capabilities Just Crossed the Governance Rubicon

Fig. 1. How Mythos Evolved to Become a Recursive Threat, ChatGPT and Jeremy Swenson, 2026.

In April 2026, a quiet but profound shift occurred in cybersecurity—one that many organizations are still underestimating. Anthropic’s Claude Mythos Preview did not simply advance AI capability. It crossed a threshold. For the first time, a commercially developed model demonstrated the ability to autonomously discover and exploit software vulnerabilities at a near-expert level, including executing multi-step attack chains end-to-end.¹²

This is not incremental progress. It is a structural break. And with that break comes a new reality: the governance, security, and policy frameworks we have relied on are no longer theoretical exercises. They are operational requirements.

From Capability to Consequence—The End of the “Future Risk” Debate:

For years, discussions about AI-enabled cyber offense lived in the realm of hypotheticals—what could happen if models became sufficiently capable. That debate is now over. Mythos achieved a 73% success rate on expert-level capture-the-flag challenges and became the first AI system to complete a full 32-step enterprise network attack simulation.¹ What previously required elite human operators over many hours can now be partially automated.

At the same time, real-world testing has already shown that similar systems can uncover large volumes of previously unknown vulnerabilities. Reports indicate thousands of zero-day findings—including flaws that persisted undetected for decades—are now within reach of AI-assisted discovery.⁹ External validation reinforces this trajectory. A collaboration involving Mozilla used Mythos-like capabilities to identify hundreds of vulnerabilities in Firefox, demonstrating how quickly defensive gains—and offensive risks—can scale simultaneously. This dual-use dynamic is the defining characteristic of the Mythos moment: the same system that strengthens defense can accelerate exploitation.

The Government Contradiction—Risk, Reliance, and Reality:

What makes this moment even more consequential is not just the technology, but the policy response. In March 2026, the U.S. Department of Defense designated Anthropic as a supply chain risk after the company refused to allow unrestricted use of its models for autonomous weapons and surveillance applications.³ This effectively barred Anthropic from Pentagon contracts.

Yet within weeks, reporting confirmed that the National Security Agency—which operates within the same defense ecosystem—was actively using Mythos under controlled access.⁵⁶ At the same time, the Office of Management and Budget began negotiating a framework to deploy a modified version of the model across civilian agencies, including energy and financial regulators.⁷

This creates a striking contradiction:

  • One part of government labels the system a national security risk.
  • Another part actively deploys it.
  • A third is designing policy to scale its adoption.

This is not just bureaucratic inconsistency—it is a preview of how difficult governing frontier AI will be.

The Real Precedent—Governing AI as a Cyberweapon:

What is being negotiated right now matters far beyond Mythos itself. The White House–led framework under development is effectively the first attempt to govern an AI system with cyberweapon-level capabilities, not just data privacy or model safety.

Three emerging principles define this model:

1. Data Sovereignty Sensitive code and infrastructure data must remain within isolated government-controlled environments.

2. Model Integrity Inputs cannot be used to retrain or improve the underlying model, preventing unintended knowledge transfer.

3. Human-in-the-Loop Oversight No autonomous execution—human validation remains mandatory before action.

These are not minor guardrails. They represent the likely baseline for how governments—and eventually regulated industries—will manage high-capability AI systems. If history is any guide, these standards will propagate outward, much like FedRAMP reshaped cloud security procurement. Within 12–18 months, similar requirements are likely to appear in enterprise contracts, regulatory expectations, and audit frameworks.

The Industry Signal—This Is Already Scaling:

The private sector is not waiting. Through Project Glasswing, Anthropic has already deployed Mythos capabilities to a controlled group of major technology and infrastructure organizations, including cloud providers, semiconductor firms, and financial institutions.²

At the same time, companies like Microsoft are moving to integrate similar AI-driven vulnerability discovery into their secure development lifecycles, signaling that this capability will become embedded—not optional—in modern engineering practices. The implication is clear. AI-assisted vulnerability discovery is becoming a standard feature of cybersecurity—not an edge capability.

The Hard Truth—Containment Is Likely Temporary:

Perhaps the most important—and uncomfortable—reality is this:

Containment will not hold indefinitely. History shows that advanced AI capabilities diffuse rapidly. Model architectures leak, competitors replicate breakthroughs, and open-weight alternatives emerge. Even today, non-frontier models can replicate meaningful portions of Mythos-like capability at far lower cost and with fewer restrictions.¹⁴ That means the current environment—where only a limited set of organizations have access—is a temporary window. Organizations that treat this as a policy issue rather than an operational priority are making a critical mistake.

What This Means for Enterprise Leaders:

The Mythos precedent is not a niche technical development. It is a strategic inflection point. Three implications stand out:

1. The Attack Surface Is No Longer Static:

AI compresses the timeline between vulnerability discovery and exploitation from weeks or months to hours. Legacy assumptions—especially around “safe” unpatched systems—are no longer valid.

2. Patch Velocity Becomes a Board-Level Issue:

Organizations with slow remediation cycles are structurally exposed. If critical vulnerabilities can be identified and weaponized faster, governance processes must accelerate accordingly.

3. Defense Must Become Structural, Not Reactive:

Emerging approaches like confidential computing—hardware-isolated execution environments—offer a path to reducing the impact of exploits regardless of discovery speed.

In other words, the goal shifts from “find and fix everything” to “limit what can be compromised at runtime.”

The Strategic Window—Act Before the Curve Flattens:

There is still a narrow window of advantage. Today, frontier capabilities are relatively concentrated. Tomorrow, they will not be. Organizations that move now—by modernizing vulnerability management, accelerating patch cycles, and adopting structural defenses—can get ahead of the curve. Those who wait for regulatory clarity or broader market adoption will likely find themselves reacting under pressure.

Final Thoughts—How to Mitigate These Risks Now:

Here are the most practical, high-impact actions organizations can take right now to mitigate risks associated with advanced AI systems, data exposure, and model misuse—especially in light of incidents like large-scale leaks or “model mythos” exposures:

1) Lock Down Data at the Source:

The most immediate risk reducer is controlling what goes into AI systems in the first place.

  • Classify and tier data (public, internal, confidential, restricted).
  • Prohibit sensitive data (e.g., IP, credentials, client info) from being entered into external AI tools.
  • Implement data loss prevention (DLP) policies across endpoints, SaaS, and APIs.
  • Tokenize or anonymize sensitive datasets before AI usage.

2) Enforce Strong Access Controls:

AI systems often inherit weak identity governance from the broader environment.

  • Apply least privilege access to AI tools, datasets, and model pipelines.
  • Require multi-factor authentication (MFA) everywhere AI is accessed.
  • Monitor and restrict API key usage (rotate keys frequently).
  • Segment environments (dev/test/prod) to prevent lateral movement.

3) Introduce AI-Specific Governance:

Traditional IT governance is not sufficient for AI risk.

  • Stand up a lightweight AI governance council (security, legal, data, business).
  • Define acceptable use policies for generative AI tools.
  • Maintain an AI system inventory (models, vendors, datasets, use cases).
  • Require risk assessments before deploying AI into production.

4) Monitor for Data Leakage and Model Abuse:

You can’t protect what you don’t observe.

  • Log all prompts, outputs, and API interactions (where legally permissible).
  • Deploy behavioral analytics to detect unusual model usage patterns.
  • Scan outputs for sensitive data leakage (prompt injection, exfiltration attempts).
  • Red-team models with adversarial testing scenarios.

5) Harden Third-Party and Vendor Risk:

Many AI risks enter through vendors, not internal builds.

  • Conduct AI-focused vendor due diligence (data handling, training sources, retention policies).
  • Require contractual clauses on: Data ownership Model training boundaries Breach notification timelines.
  • Prefer vendors offering private model instances or zero data retention.

6) Implement Prompt and Output Controls:

The interface layer is a major attack surface.

  • Use prompt filtering and sanitization to block injection attempts.
  • Apply output guardrails to prevent harmful or sensitive responses.
  • Restrict high-risk capabilities (e.g., code execution, system access).
  • Use retrieval-augmented generation (RAG) with vetted internal sources only.

7) Train Employees (Fast, Not Perfect):

Human behavior is still the biggest variable.

  • Roll out short, targeted training on: Safe AI usage, Data handling do’s and don’ts, Prompt injection awareness.
  • Provide approved AI tools so employees don’t default to shadow AI.
  • Reinforce “don’t paste what you wouldn’t email externally”.

8) Prepare for Incident Response:

Assume exposure will happen—speed matters.

  • Update incident response plans to include AI-specific scenarios.
  • Define playbooks for: Data leakage via prompts, Model compromise or abuse, Third-party AI breaches.
  • Run tabletop exercises simulating AI-related incidents.

9) Control Model Inputs and Training Data:

What shapes the model shapes the risk.

  • Vet training datasets for: Sensitive information, Copyright/IP exposure, Bias and integrity issues.
  • Maintain data provenance tracking.
  • Avoid uncontrolled fine-tuning on raw internal data.

10) Start Small with Secure Architectures:

Don’t boil the ocean—secure what’s already in motion.

  • Use private or on-prem AI deployments for sensitive workloads.
  • Isolate AI systems within secure cloud environments.
  • Gate external model access through controlled middleware or APIs.
  • Adopt a “human-in-the-loop” approach for high-risk decisions.

Endnotes:

  1. UK AI Security Institute, “Our Evaluation of Claude Mythos Preview’s Cyber Capabilities,” April 2026.
  2. Anthropic, “Project Glasswing: Securing Critical Software for the AI Era,” April 2026.
  3. CNBC, “Judge Presses DOD on Why Anthropic Was Blacklisted,” March 24, 2026.
  4. CNBC, “Anthropic Loses Appeals Court Bid to Temporarily Block Pentagon Blacklisting,” April 8, 2026.
  5. TechCrunch, “NSA Spies Are Reportedly Using Anthropic’s Mythos,” April 20, 2026.
  6. Axios, “NSA Using Anthropic’s Mythos Despite Defense Department Blacklist,” April 19, 2026.
  7. CSO Online, “White House Moves to Give Federal Agencies Access to Anthropic’s Claude Mythos,” April 2026.
  8. Fortune, “Anthropic Acknowledges Testing New AI Model,” March 26, 2026.
  9. TechCrunch, “Anthropic Debuts Preview of Powerful New AI Model Mythos,” April 7, 2026.
  10. Axios, “Anthropic to Have Peace Talks at White House,” April 17, 2026.
  11. CNBC, “Trump Says He Had ‘No Idea’ About White House Meeting,” April 17, 2026.
  12. Washington Post, “Anthropic CEO Visits White House Amid Hacking Fears,” April 17, 2026.
  13. Council on Foreign Relations, “Six Reasons Claude Mythos Is an Inflection Point,” April 2026.
  14. Evron, Mogull, Lee et al., “The AI Vulnerability Storm: Building a Mythos-Ready Security Program,” CSA/SANS/OWASP, April 2026.

Russia’s Sanctions-Busting Cryptocurrency Empire: Architecture, Actors, and the Future of Financial Conflict

Fig. 1. Russia’s Sanctions-busting Cryptocurrency Empire Infographic, Jeremy Swenson via ChatGPT, 2026.


I. Origins of a Parallel Financial System:

The roots of Russia’s sanctions-busting cryptocurrency ecosystem can be traced to the intersection of geopolitical pressure and technological opportunity. While Russia experimented with cryptocurrency policy ambiguity throughout the 2010s, it was the aftermath of the 2022 invasion of Ukraine—and the subsequent exclusion from key parts of the global financial system, including SWIFT—that triggered a structural change. Lacking dollar liquidity and limited by Western banking restrictions, Russian policymakers and aligned financial actors started rapidly developing alternative methods for cross-border settlement (1).

Early efforts were fragmented, consisting of informal networks of exchanges, darknet markets, and capital flight channels. Platforms such as Garantex, founded in 2019, became foundational nodes in this system, allowing users to convert rubles into stablecoins and move funds internationally while avoiding traditional compliance mechanisms (6). Despite sanctions imposed by the U.S. Treasury in 2022, these platforms adapted rapidly, shifting wallets, rebranding, and integrating with crypto mixers to obscure transaction flows (1).

By 2024, Russia had formally embraced cryptocurrency for international trade, legalizing its use in cross-border transactions while maintaining domestic restrictions. This dual posture—restrict internally, exploit externally—laid the groundwork for a state-tolerated, if not state-enabled, shadow financial architecture that would mature rapidly in the years that followed (9).

II. The Rise of A7A5 and the Industrialization of Evasion:

The emergence of the ruble-backed stablecoin A7A5 marked a turning point from opportunistic evasion to industrial-scale financial engineering. Developed through networks linked to sanctioned Russian financial institutions and offshore intermediaries, A7A5 was designed explicitly to bypass Western oversight by enabling direct conversion from rubles into crypto assets and then into globally usable currencies (6).

Unlike decentralized cryptocurrencies such as Bitcoin, A7A5 represents a hybrid model: centralized issuance combined with decentralized transaction pathways. This design allows Russian actors to maintain monetary control while leveraging blockchain’s opacity and global reach. Within its first year, the token processed tens of billions of dollars in transactions, with some estimates approaching $100 billion in cumulative volume—evidence of rapid adoption across trade networks and sanctions-affected industries (4).

Crucially, this system extended beyond simple financial transfers. It became embedded in supply chain logistics, enabling the procurement of dual-use goods—technology with both civilian and military applications—through intermediaries in regions such as Central Asia and the Middle East. Crypto-enabled payments allowed these transactions to bypass traditional banking scrutiny, effectively creating a parallel trade infrastructure insulated from Western enforcement mechanisms (3).

III. Decentralization as Strategy, Not Ideology:

In Western culture, decentralization is often seen as a libertarian ideal—an escape from centralized power. However, in the Russian sanctions-evasion model, decentralization is not about ideology but strategy. It is used selectively to reduce visibility, make enforcement harder, and spread operational risk.

This system operates as a layered network rather than a single platform. Exchanges such as Bitpapa and others flagged by blockchain intelligence firms function alongside mixers, peer-to-peer marketplaces, and offshore entities, creating a fluid ecosystem in which assets can be rapidly converted, transferred, and obfuscated (7).

Moreover, decentralization enhances resilience. When Western authorities sanction one node—such as Garantex—activity shifts to successor platforms or newly created entities, often staffed by the same personnel. This phenomenon mirrors adaptive systems: disruption leads not to collapse but to evolution. The result is a sanctions-resistant architecture that thrives on redundancy and ambiguity.

Academic research supports this point by showing that sanctions enforcement in crypto is structurally reactive, while illicit actors are fast and adaptive. Studies find that once wallets or platforms are sanctioned, actors quickly shift funds to new addresses, exchanges, or networks—often within hours—well before regulators can complete attribution and enforcement cycles (12). Because blockchain systems allow unlimited address creation and operate across jurisdictions, enforcement actions tend to disrupt specific nodes rather than the broader network. As a result, the research consistently demonstrates that sanctions evasion persists not despite enforcement, but because the system’s design enables rapid migration and continuity.

IV. The Ransomware Nexus: Criminal Infrastructure and State Alignment

At the heart of Russia’s crypto ecosystem lies a symbiotic relationship between cybercriminal groups and financial infrastructure. Ransomware organizations such as REvil and Ryuk-linked networks have long relied on cryptocurrency to receive and launder payments, targeting Western corporations, critical infrastructure, and supply chains (2).

The connection between these groups and sanctioned exchanges is well-documented. Platforms like Garantex have been identified as facilitating transactions tied to ransomware proceeds, effectively serving as financial clearinghouses for cybercrime (5). This relationship extends beyond mere tolerance. Investigations such as Operation Destabilise have uncovered networks in which cryptocurrency exchanges, money laundering operations, and state-linked actors intersect. In some cases, these networks have been used not only for financial gain but also to support espionage activities and strategic objectives aligned with Russian interests (11).

The implication is clear: ransomware is not simply criminal activity but a component of a broader hybrid warfare strategy. By targeting Western institutions and funneling proceeds through crypto networks, these groups generate revenue, disrupt adversaries, and reinforce Russia’s alternative financial ecosystem.

V. Extraction from the West: Mechanisms of Digital Theft:

The Russian crypto-sanctions ecosystem extracts value from the West through multiple channels, blending cybercrime, financial engineering, and trade manipulation. Ransomware attacks represent the most visible vector, with payments often demanded in cryptocurrency and subsequently laundered through exchanges and mixers (2).

However, a less visible but equally significant mechanism is trade-based money laundering facilitated by crypto. Russian entities purchase restricted goods through intermediaries, paying in stablecoins that are difficult to trace. These goods are then re-exported into Russia, effectively bypassing export controls (3).

Additionally, capital flight and asset concealment play a major role. Wealthy individuals and sanctioned entities move funds into crypto assets to protect them from seizure, leveraging decentralized wallets and offshore exchanges. The cumulative effect is a steady outflow of value from regulated Western systems into a shadow economy that operates beyond their reach.

By 2025, illicit cryptocurrency flows had surged dramatically, with tens of billions of dollars linked to sanctions evasion and state-aligned networks (10).

VII. Conclusion: The Future of Financial Warfare:

Russia’s sanctions-busting cryptocurrency empire represents a new phase in the evolution of financial conflict—not simply a workaround, but a scalable model for a decentralized, state-influenced financial system operating beyond traditional controls. What began as a reaction to Western sanctions has matured into a resilient ecosystem that blends state policy, criminal enterprise, and technological innovation. Its strength lies in its hybridity: centralized where control is necessary, decentralized where opacity provides advantage.

For the West, this presents a fundamental challenge. Traditional tools—sanctions, asset freezes, and banking restrictions—are increasingly limited in a world where adversaries can operate outside the formal financial system. Countering this shift requires more than incremental reform; it demands a transition from static enforcement to dynamic, intelligence-driven financial defense.

A central component of this approach is the expansion of blockchain analytics and real-time monitoring. On-chain intelligence has proven effective in tracing illicit flows and identifying high-risk actors, but its true value emerges when integrated into coordinated international enforcement frameworks. Moving beyond periodic sanctions designations toward continuously updated, intelligence-led responses will be critical to keeping pace with adaptive networks (7).

Equally important is targeting the infrastructure that enables liquidity. Cryptocurrency ecosystems depend on exchanges, stablecoin issuers, and fiat on-ramps and off-ramps to function. Coordinated regulation and enforcement against these access points—particularly across jurisdictions that facilitate intermediary flows—can significantly constrain the usability of sanctions-evading assets. While measures such as wallet blacklisting and exchange sanctions have had impact, they must evolve from reactive tools into part of a broader, proactive strategy (1).

At the same time, deterrence must be redefined. Financial penalties alone are insufficient against actors who operate in decentralized and jurisdictionally fragmented environments. Effective deterrence will require a combination of cyber operations, asset seizures, and coordinated disruption of ransomware and illicit financial infrastructure. Public-private collaboration will be essential, as much of the expertise and visibility into these networks resides within the private sector.

Beyond enforcement, the West must also compete. Developing secure, efficient, and transparent alternatives—such as regulated digital payment systems, central bank digital currencies, and compliant stablecoin frameworks—can reduce the relative attractiveness of shadow financial networks. If legitimate systems offer greater speed, cost efficiency, and accessibility, the incentive to rely on illicit alternatives diminishes.

Finally, this issue must be understood in its broader geopolitical context. Russia’s crypto ecosystem is not an isolated case but part of a wider movement toward financial fragmentation, in which states seek parallel systems to reduce dependence on Western institutions. Addressing this trend will require sustained international coordination, including strategic engagement with non-Western jurisdictions that play intermediary roles in these networks (4).

In this evolving landscape, success will not be measured by the elimination of illicit systems, but by the ability to constrain, outpace, and adapt to them. The future of financial warfare will belong to those who can align technological capability with strategic coherence—building financial architectures that are not only secure, but resilient against continuous disruption.

Bibliography:

  1. U.S. Department of the Treasury. “Treasury Sanctions Cryptocurrency Exchange and Network.” https://home.treasury.gov/news/press-releases/sb0225
  2. Chainalysis. Crypto Crime Report 2026. https://www.chainalysis.com
  3. Royal United Services Institute (RUSI). “The Shadow Crypto Economy Feeding Russia’s War Machine.” https://www.rusi.org
  4. Center for European Policy Analysis (CEPA). “A Crypto River Runs Through Russia.” https://cepa.org
  5. BankInfoSecurity. “U.S. Sanctions Crypto Exchange Tied to Russian Ransomware.” https://www.bankinfosecurity.com
  6. TRM Labs. “Garantex, Grinex, and the A7A5 Token.” https://www.trmlabs.com
  7. Elliptic. “Russia-Linked Crypto Platforms’ Ongoing Sanctions Evasion.” https://www.elliptic.co
  8. Reuters. “Sanctioned Russian Crypto Exchange Suspends Services.” https://www.reuters.com
  9. Business Insider. “Russia’s Crypto Shadow Economy.” https://www.businessinsider.com
  10. Financial Times. “Illicit Crypto Flows Surge to Record Levels.” https://www.ft.com
  11. National Crime Agency. “Operation Destabilise.” https://www.nationalcrimeagency.gov.uk
  12. Zola, Francesco et al. “Assessing the Impact of Sanctions in the Crypto Ecosystem.” https://arxiv.org/abs/2409.10031

Crypto, Conflict, and Capital Flight: What Iran’s On-Chain Shock Signals for Middle East Economics and U.S. Markets

In late February 2026, shortly after coordinated U.S.–Israeli airstrikes struck targets in Tehran, blockchain analytics firms observed an abrupt spike in cryptocurrency withdrawals from Iran’s largest digital asset exchange. Within minutes of the strikes, Nobitex reportedly experienced a roughly 700 percent surge in withdrawals, with millions of dollars in crypto leaving the platform in a compressed time window.¹ This episode, while modest in absolute global market terms, offers a revealing case study in how digital assets function during geopolitical stress—and what that may signal for Middle East economics and U.S. financial markets over the next year.

A Rapid Withdrawal Shock:

Reporting indicates that nearly $3 million exited Nobitex in a single hour following the strikes, with approximately $10 million leaving Iranian exchanges over several days.² Such flows are small relative to global crypto trading volumes but significant within the Iranian financial context, where capital controls, sanctions, and currency instability already shape economic behavior.

Iran’s domestic currency, the rial, has faced long-standing pressure from inflation, sanctions, and restricted access to global banking networks. In that environment, cryptocurrencies—particularly Bitcoin and dollar-denominated stablecoins—have increasingly served as alternative stores of value and channels for cross-border transfers.³ The surge in withdrawals appears consistent with crisis-driven capital preservation behavior rather than speculative trading alone.

Crypto as a Financial “Pressure Valve”:

The events underscore crypto’s evolving role as a decentralized financial “pressure valve” in sanctioned or conflict-affected economies. When traditional banking rails are constrained or politically vulnerable, digital assets offer relative portability and censorship resistance.¹

Internet blackouts and temporary exchange disruptions complicate interpretation. Outages can cluster transactions when connectivity resumes, making withdrawal spikes appear sharper than underlying demand alone would suggest.³ Nonetheless, the pattern aligns with prior episodes in emerging markets where digital assets gained traction during currency stress.

The lesson is not that crypto replaces sovereign financial systems, but that it increasingly supplements them under strain.

Economic Implications for the Middle East (Next 12 Months):

Looking forward, several dynamics are likely to shape regional economics:

1. Expanded Informal Dollarization via Digital Assets. Sanctioned or financially constrained economies may see broader retail and institutional adoption of dollar-linked stablecoins as parallel monetary tools.

2. Heightened Regulatory and Surveillance Pressure. As crypto flows intersect with sanctions regimes, U.S. and allied regulators are likely to intensify scrutiny of exchanges, custodians, and cross-border blockchain activity.¹

3. Persistent Capital Flight Incentives. Geopolitical volatility increases incentives for households and firms to diversify outside domestic banking systems.

4. Infrastructure Fragility Risks. Internet shutdowns and exchange outages remain structural vulnerabilities in crisis environments.³

Collectively, these forces suggest that digital asset adoption in parts of the Middle East will continue—not as ideological endorsement of crypto, but as pragmatic economic hedging.

What This Means for U.S. Markets:

For U.S. investors and policymakers, the implications extend beyond regional headlines.

Oil and Energy Sensitivity. Any escalation involving Iran carries oil supply risk implications. Even absent sustained disruption, perceived risk premiums can lift energy prices.

Safe-Haven Flows and Dollar Strength. Periods of geopolitical tension historically reinforce demand for U.S. Treasuries and dollar-denominated assets. Concurrently, Bitcoin and gold often experience volatility tied to risk sentiment shifts.⁴

Regulatory Spillover. If crypto is increasingly viewed as a sanctions-adjacent vector, U.S. enforcement posture may tighten, affecting exchanges and institutional investors.

Systemic Interconnectedness. Crypto is no longer a siloed asset class. It is embedded within global liquidity networks. Geopolitical events can trigger rapid on-chain responses that ripple into equities, commodities, and foreign exchange markets.

Forecast—A Converging Risk Landscape:

Over the next year, expect three converging trends:

  1. Greater integration between geopolitical risk modeling and digital asset analytics.
  2. Increased compliance burdens on global crypto infrastructure providers.
  3. Continued volatility transmission across oil, crypto, emerging market currencies, and U.S. equities during regional escalations.

The Iranian withdrawal spike may have involved only millions of dollars—but its significance lies in what it signals: digital capital now moves at the speed of conflict.

For U.S. markets, that means geopolitical shocks increasingly transmit through hybrid financial rails—traditional and decentralized alike. Outside of economic considerations, peace is desirable for the benefit of all.

Bibliography:

  1. Yahoo Finance. “Millions of Dollars in Crypto Left Iranian Exchanges After Airstrikes.” February 2026.
  2. Economic Times. “Why Did Iran’s Largest Crypto Exchange See a 700% Withdrawal Spike Minutes After US–Israel Airstrikes Hit Tehran?” February 2026.
  3. Bitget News. “Iranian Crypto Exchange Records Surge in Withdrawals Following Tehran Strikes.” February 2026.
  4. Forbes. “Iran War, an Oil Crisis, a Crypto Stress Test.” March 2026.

Apple’s Carrier-Level Location Privacy: Strategy, Law, and the Future of Data Control

Fig. 1. Apple’s Carrier-Level Location Privacy Infographic. Jeremy Swenson and Open AI Chat GPT. 2026.

In January 2026, Apple quietly introduced a new privacy control in iOS 26.3 that allows users to limit the precision of location data shared with cellular carriers. While the feature’s initial rollout was narrow—restricted to select devices and carriers—it represents a significant shift in how location data is governed at the network level, with implications for legal investigations, platform competition, and data marketing strategies.1

Unlike app-level location permissions, which have been a focal point of mobile privacy debates for more than a decade, this control targets a less visible layer of the data stack: the information that cellular networks inherently collect as devices connect to towers. By allowing users to reduce carrier access to neighborhood-level rather than precise location data, Apple is challenging long-standing assumptions about the inevitability of carrier-side surveillance.

How the Feature Works—and Why It Matters

The new “Limit Precise Location” setting is found within Cellular Data Options on supported devices running iOS 26.3. When enabled, it reduces the granularity of location data available to participating carriers without degrading network performance or interfering with emergency services.2 Apple has emphasized that precise location data remains available to emergency responders and to apps that users have explicitly authorized, underscoring that the control is designed to limit passive collection rather than eliminate functionality.

At launch, the feature applies only to devices equipped with Apple’s newer C-series modems and is supported by a limited number of carriers, including Boost Mobile in the United States and select providers in Europe and Asia.2 This constrained availability reflects Apple’s vertically integrated approach to privacy: by controlling hardware, operating system, and key software layers, Apple can implement privacy protections that are difficult to standardize across more fragmented ecosystems.

Legal Investigation and Carrier Data: A Shifting Boundary

Carrier-level location data has long been a cornerstone of law-enforcement investigations. Historical cell-tower records can be used to infer a person’s movements, corroborate timelines, or establish proximity to crime scenes. As a result, carriers are frequent recipients of subpoenas and lawful data requests.

By limiting the precision of location data available at the carrier level, Apple’s new feature introduces friction into this investigative model. While it does not prevent lawful access to available data, it may reduce the specificity of records in cases where users have enabled the setting. This development raises important legal questions: if a platform offers a user-controlled mechanism that technically limits data collection, what obligations do carriers retain to preserve or disclose information that no longer exists in high-resolution form?

Security researchers and privacy advocates have framed the feature as a defensive response to the growing misuse of carrier data, including cases where location information has been sold, leaked, or exploited by criminal actors.3 From this perspective, the control is less about obstructing legitimate investigations and more about narrowing the attack surface of sensitive personal data.

Platform Strategy: Apple Versus Android

The contrast with Android is instructive. Android has made substantial progress in recent years with fine-grained app permissions, background location alerts, and transparency dashboards. However, it does not currently offer a system-level control that restricts the precision of location data shared directly with carriers.

This difference reflects deeper architectural realities. Android’s ecosystem spans multiple hardware manufacturers, modem vendors, and carrier customizations, making uniform carrier-level privacy controls difficult to deploy. Apple’s ability to design proprietary modems and tightly integrate them with iOS enables a level of privacy enforcement that is harder to replicate in a more open, modular platform.

From a strategic standpoint, this gives Apple a competitive narrative advantage: privacy not merely as policy, but as product design. While Android remains dominant globally in market share, Apple’s approach positions privacy as a premium feature tied to hardware, reinforcing brand trust among users who are increasingly sensitive to data misuse.

Privacy, Data Marketing, and Consumer Trust

Location data is among the most valuable assets in the data economy. It fuels targeted advertising, behavioral analytics, and predictive modeling across industries. Limiting carrier-level access does not eliminate these practices, but it does alter where and how data is collected.

Apple has been careful to frame this feature as part of a broader philosophy of data minimization rather than an absolute shield. App-level data collection, Wi-Fi triangulation, Bluetooth beacons, and other signals can still reveal detailed location information when users grant permission. The new control instead constrains a historically opaque channel of data flow that users rarely considered or understood.1

For consumers, this reinforces a key reality of modern privacy: meaningful control requires layered defenses. Carrier-level protections, app permissions, and informed usage patterns must work together. For data marketers and brokers, the shift signals a gradual tightening of default access to passive location data, encouraging greater reliance on consent-driven and aggregated sources.

Conclusion: Implications and Best Practices

Apple’s decision to limit precise location data shared with carriers marks an incremental but meaningful evolution in mobile privacy architecture. It highlights the growing tension between user autonomy, lawful access, and commercial data practices, while underscoring the strategic power of vertically integrated platforms.

Looking ahead, several implications stand out:

  1. Legal frameworks may need to adapt to scenarios where high-resolution location data is no longer uniformly available at the carrier level.
  2. Platform competition will increasingly hinge on architectural control, not just policy promises.
  3. Data markets will continue shifting toward explicit consent and diversified data sources as passive collection channels narrow.

Best practices for consumers remain straightforward but essential:

  • Regularly review system-level and app-level privacy settings.
  • Understand the scope and limits of each control.
  • Grant precise location access only when it is necessary for functionality.
  • Stay informed about how platforms and carriers handle personal data.

Ultimately, Apple’s new feature does not end location tracking, nor does it resolve every privacy concern. What it does accomplish is more subtle—and more consequential: it redraws the boundary of what is considered acceptable default data collection in the mobile ecosystem, setting a precedent that others will be pressured to follow.

Endnotes

  1. Apple Inc., “Limit precise location from cellular networks,” Apple Support, accessed January 2026, https://support.apple.com/en-euro/126101.
  2. Chance Miller, “iOS 26.3 Adds New Feature to Limit Location Data Shared With Your Carrier,” 9to5Mac, January 26, 2026, https://9to5mac.com/2026/01/26/ios-26-3-adds-new-feature-to-limit-location-data-shared-with-your-carrier/.
  3. Suzanne Smalley, “New Apple Feature Will Block Cell Networks From Capturing Precise Location Data,” The Record from Recorded Future News, January 29, 2026, https://therecord.media/new-apple-feature-block-location-data-cell-networks.

Why Being Respected Matters More Than Being Nice in Leadership

In leadership, the tension between being respected and being merely nice has been debated for centuries. Niceness is often equated with politeness, affability, and the desire to avoid conflict. Respect, on the other hand, is grounded in trust, competence, and integrity. While niceness may win temporary approval, respect creates lasting influence. Leaders who prioritize being respected over being liked not only drive stronger performance but also safeguard their organizations against complacency and poor decision-making. A change agent leader cannot be overly nice, or he or she will be trampled on.

Fig. 1. Jeremy Swenson, Pink Suit With Yellow Background, 2025, Jeremy Swenson.

Fig. 1. Jeremy Swenson, Ink Suit Yellow Background, 2025.

The Pitfalls of “Niceness”:

Niceness can be an appealing trait, especially in team settings where harmony is valued. However, as a leadership strategy, niceness carries inherent risks. When leaders prioritize being liked, they may avoid difficult conversations, tolerate poor performance, or bend organizational rules to keep others happy. Over time, this erodes accountability. Research in organizational psychology demonstrates that leaders who are overly agreeable may sacrifice effectiveness, as employees perceive them as weak or inconsistent (Judge, Bono, Ilies, & Gerhardt, 2002).

Margaret Thatcher, the former Prime Minister of the United Kingdom, captured this dilemma bluntly: “If you set out to be liked, you will accomplish nothing” (Thatcher, 1993, p. 147). Niceness often becomes a form of self-preservation—leaders seek short-term harmony at the cost of long-term impact. While being liked may feel rewarding in the moment, it does not inspire confidence or loyalty when difficult decisions must be made. An overly nice person would likely give undue favor to people close to them and thus would not encourage growth or innovation.

Why Respect Endures:

Respect is a far more enduring quality. It is not rooted in popularity but in consistency, fairness, and competence. Respected leaders earn trust by setting clear expectations, making principled decisions, and holding themselves and others accountable. Respect does not preclude kindness; rather, it frames kindness in a way that maintains boundaries and integrity.

The late Maya Angelou (1993) famously observed: “People will forget what you said, people will forget what you did, but people will never forget how you made them feel” (p. 21). In a leadership context, being respected makes people feel valued, secure, and motivated because they know their leader will not waiver under pressure or abandon fairness for personal popularity. Respect builds psychological safety, which modern research identifies as one of the strongest predictors of high-performing teams (Edmondson, 2019).

Moreover, people are more likely to trust those who build respect than politeness. Respect crosses all demographics while what is nice in one culture may not be nice in another culture. In other words, respect is less subjective and thus more powerful. Respect means you mean what you say and enforce it over time, across cultures, and no matter what. Niceness signals your pliable and not confident in your approach as to who or what is right.

Lessons from Business Leadership:

Business history is filled with examples that highlight the difference between respected leaders and merely nice ones.

  • Steve Jobs (Apple): Jobs was not widely regarded as “nice.” His demanding nature often clashed with employees. However, he was deeply respected for his vision, creativity, and relentless pursuit of excellence. Walter Isaacson (2011) documented how Jobs inspired loyalty and innovation because employees trusted his uncompromising standards, even if they did not always appreciate his methods.
  • Indra Nooyi (PepsiCo): Nooyi combined respect with empathy. She was known for her warmth and for writing personal letters to employees’ families, yet she also set bold strategic goals and held teams accountable for results. Her leadership illustrates that respect does not exclude kindness but rather enhances it when boundaries and accountability remain intact (Nooyi & Mirza, 2021).
  • Colin Powell (U.S. Army General): Powell (1995) explained that respect is inseparable from accountability: “The day soldiers stop bringing you their problems is the day you have stopped leading them” (p. 54). For Powell, respect came not from being “nice” but from being competent, decisive, and trustworthy in the face of pressure.

These examples highlight that respected leaders may not always win popularity contests, but they leave legacies of trust and performance.

Respect, Boundaries, and Authority:

A crucial distinction between respect and niceness lies in boundaries. Nice leaders often allow others to cross their boundaries in order to avoid discomfort. Respected leaders, by contrast, maintain clear boundaries, which prevents exploitation and reinforces authority. As Maxwell (1998) argued, leadership is fundamentally about influence, and influence requires credibility. A leader without respect may have a title, but not authority.

In practice, this means making unpopular but necessary decisions—layoffs during a downturn, holding a top performer accountable for misconduct, or refusing to compromise ethics for profit. These choices rarely make a leader “liked” in the moment, but they generate long-term respect and loyalty. Employees may not always agree, but they admire the leader’s consistency and courage. This is especially true in contexts that require tough change management, such as mergers, new products, entering new countries, and adopting new technologies. This is where a strong respected visionary leader beats nice person every time.

Conclusion:

In the final analysis, it is far better for leaders to be respected than to be merely nice. Niceness without boundaries leads to exploitation and mediocrity. Respect, however, fosters trust, accountability, and sustainable success. Leaders who cultivate respect create organizations that withstand challenges, adapt to change, and achieve long-term goals.

As Thatcher, Angelou, Jobs, Nooyi, and Powell all remind us in different ways, leadership is not about avoiding conflict or pleasing others—it is about earning trust through integrity, competence, and courage. Respect lasts; niceness fades. In business and leadership, respect is not just preferable—it is essential.

A respected leader will not be taken advantage of. His or her management structure will be less likely to be challenged, making operations run more smoothly. Those around such a leader will be more inspired to follow the tough decisions they make and will feel relief knowing they did not have to shoulder those burdens themselves, yet can remain confident in the respected leader who did. That leader is not doubted. With the right experience and training, you can be that leader.

References:

Angelou, M. (1993). Wouldn’t take nothing for my journey now. Bantam Books.

Edmondson, A. C. (2019). The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth. Wiley.

Isaacson, W. (2011). Steve Jobs. Simon & Schuster.

Judge, T. A., Bono, J. E., Ilies, R., & Gerhardt, M. W. (2002). Personality and leadership: A qualitative and quantitative review. Journal of Applied Psychology, 87(4), 765–780. https://doi.org/10.1037/0021-9010.87.4.765

Maxwell, J. C. (1998). The 21 irrefutable laws of leadership. Thomas Nelson.

Nooyi, I., & Mirza, R. (2021). My life in full: Work, family, and our future. Portfolio.

Powell, C. (1995). My American journey. Random House.

Thatcher, M. (1993). The Downing Street years. HarperCollins.

About the Author:

Jeremy Swenson is a disruptive-thinking security entrepreneur, futurist/researcher, and senior management tech risk consultant. Over 17 years, he has held progressive roles at many banks, insurance companies, retailers, healthcare organizations, and even government entities. Organizations appreciate his talent for bridging gaps, uncovering hidden risk management solutions, and simultaneously enhancing processes. He is a frequent speaker, podcaster, and a published writer – CISA Magazine and the ISSA Journal, among others. He holds a certificate in Media Technology from Oxford University’s Media Policy Summer Institute, an MBA from Saint Mary’s University of MN, an MSST (Master of Science in Security Technologies) degree from the University of Minnesota, and a BA in political science from the University of Wisconsin Eau Claire. He is an alum of the Cyber Security Summit Think Tank, the Federal Reserve Secure Payment Task Force, the Crystal, Robbinsdale and New Hope Citizens Police Academy, and the Minneapolis FBI Citizens Academy. He also has certifications from Intel and the Department of Homeland Security.

🛡️ Cyberattack on St. Paul Disrupts Systems, Triggers National Guard Response: A Wake-Up Call for City Infrastructure and Public-Private Security

Fig. 1. St. Paul Cyber Attack, St. Paul, 2025.

A major cyberattack brought critical systems across the City of St. Paul to a halt this week, prompting Governor Tim Walz to take the rare step of activating the Minnesota National Guard’s 177th Cyber Protection Team through Executive Order 24-25. The breach, which has yet to be fully disclosed in technical detail, forced the shutdown of municipal networks, libraries, payment systems, and internal applications—raising alarms about the fragility of local government infrastructure in the digital age.

This crisis has not only impacted operations but also exposed deeper vulnerabilities—from disruption of city services to potential legal and evidentiary breakdowns, especially concerning the chain of custody for digital evidence and sensitive case management platforms used by law enforcement and legal teams.

“The cyberattack… has resulted in a disruption of city services and operations, and the city has requested assistance from the State of Minnesota in the form of technical expertise and personnel,” Gov. Walz stated in the executive order. “The incident poses a threat to the delivery of critical government services.” (Walz, 2025)

Legal and Infrastructure Ramifications:

One often overlooked consequence of cyberattacks on public systems is the risk to legal integrity. City governments often store digital evidence for court cases, police body cam footage, and case records within networked systems. When such systems are compromised or taken offline, the chain of custody—a legal requirement for maintaining the integrity of evidence—may be broken. This could lead to dismissed charges, delayed court proceedings, or contested verdicts.

Beyond the courts, St. Paul’s systems underpin essential infrastructure. From 911 backend operations to building permits, utility management, and emergency communications, these disruptions ripple into residents’ lives and civic trust. Any delay in fire dispatch systems, real-time weather alerts, or even payroll processing for emergency responders can escalate into broader crisis.

Why Public-Private Partnerships Are Essential:

The attack illustrates the need for stronger collaboration between public entities and private cybersecurity firms. Municipalities often operate with limited budgets, aging infrastructure, and insufficient security staff. In contrast, private-sector vendors—ranging from cloud security providers to endpoint monitoring specialists—offer scalable defenses and expertise that cities can’t always sustain in-house.

Governor Walz’s executive order underscores this reality, stating:

“Cooperation between the Minnesota Department of Information Technology Services (MNIT), the National Guard, and other partners is necessary to protect public assets and respond to cybersecurity threats.” (Walz, 2025)

This partnership must also extend beyond technical vendors. Insurance carriers, legal risk consultants, and incident response firms should be part of proactive city planning, not just post-breach triage.

The Human Factor: Employee Training Matters:

While technical systems are critical, human error remains the top vector for cyberattacks, especially through phishing and social engineering. A well-crafted phishing email clicked by a single city employee can introduce malware into core systems.

St. Paul’s situation shows how cybersecurity education is no longer optional. Ongoing staff training—including:

  • Simulated phishing attacks
  • Clear escalation protocols
  • “Stop and verify” culture for email attachments and access requests

…is essential. Cities should treat their staff as the first line of defense, not just passive users.

The Road Ahead: What Cities Must Do Now:

The cyberattack on St. Paul should serve as a regional and national inflection point. Other cities must take this as a cue to reassess their cyber posture through the following:

Strategic Priorities:

  1. Zero Trust Implementation Limit internal access and require constant authentication, even for trusted users.
  2. Third-Party Risk Audits Review vendors, contractors, and outsourced services for security gaps.
  3. Resilient Backup and Recovery Ensure data is stored offsite and tested regularly for recovery readiness.
  4. Legal and Digital Forensics Planning Build frameworks for protecting the chain of custody in case of breach.
  5. Integrated Public-Private Playbooks Define shared roles between city staff, Guard units, and private partners in cyber response drills.
  6. Community Transparency Proactively inform the public about risks, responses, and what’s being done to rebuild digital trust.

Final Thoughts:

The breach in St. Paul is not just a local IT issue—it is a civic security event that affects courts, emergency services, legal integrity, and public confidence. Governor Walz’s activation of the National Guard is a bold signal that digital defense is now a matter of public safety.

“Immediate action is necessary to provide technical support and ensure continuity of operations,” reads Executive Order 24-25 (Walz, 2025).

Moving forward, public-private partnerships, cybersecurity training, and legal readiness must become foundational to how cities govern in the digital era. The stakes are no longer theoretical—they are real, operational, and deeply human.

References:

  1. FOX 9. (2025, July 29). Gov. Walz activates National Guard after cyberattack on city of St. Paul. https://www.fox9.com/news/gov-walz-activates-national-guard-after-cyberattack-st-paul
  2. KSTP. (2025, July 29). City of St. Paul experiencing unplanned technology disruptions. https://kstp.com/kstp-news/top-news/city-of-st-paul-experiencing-unplanned-technology-disruptions/
  3. League of Minnesota Cities. (2024, October). Cybersecurity Incident Reporting Requirements for Cities. https://www.lmc.org/news-publications/news/all/fonl-cybersecurity-incident-reporting-requirements/
  4. Reddit. (2025, July 29). Minnesota National Guard activated after city cyberattack [Discussion threads]. https://www.reddit.com/r/minnesota
  5. Walz, T. (2025, July 29). Executive Order 24-25: Activating the Minnesota National Guard Cyber Protection Team. Office of the Governor, State of Minnesota. https://mn.gov/governor/assets/EO-24-25_tcm1055-621842.pdf

About the Author:

Jeremy Swenson is a disruptive-thinking security entrepreneur, futurist/researcher, and senior management tech risk consultant. Over 17 years, he has held progressive roles at many banks, insurance companies, retailers, healthcare organizations, and even government entities. Organizations appreciate his talent for bridging gaps, uncovering hidden risk management solutions, and simultaneously enhancing processes. He is a frequent speaker, podcaster, and a published writer – CISA Magazine and the ISSA Journal, among others. He holds a certificate in Media Technology from Oxford University’s Media Policy Summer Institute, an MBA from Saint Mary’s University of MN, an MSST (Master of Science in Security Technologies) degree from the University of Minnesota, and a BA in political science from the University of Wisconsin Eau Claire. He is an alum of the Cyber Security Summit Think Tank , the Federal Reserve Secure Payment Task Force, the Crystal, Robbinsdale and New Hope Citizens Police Academy, and the Minneapolis FBI Citizens Academy. He also has certifications from Intel and the Department of Homeland Security.