Category Archives: CIO and CISO

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.

Interview and Update on Ransomware Leader LockbitSupp

#lockbit #ransomware #cybersecurity #fraud #cyberextortion

Fig. 1. Dmitry Yuryevich Khoroshev, aka LockBitSupp.[1]

Law enforcement agencies spanning the United States, United Kingdom, and Australia have collectively pinpointed Russian national Dmitry Yuryevich Khoroshev as the suspected architect behind the infamous LockBit ransomware crime gang, operating under the moniker LockBitSupp. The government asserts LockBit victims span a wide array of entities, including individuals, small businesses, multinational corporations, hospitals, schools, nonprofit organizations, critical infrastructure, and government and law enforcement agencies. They are responsible for draining an estimated $500 million from its victims over an extensive hacking spree including:[2]

1)       148 built attacks.

2)       119 engaged in negotiations with victims, meaning they definitely deployed attacks.

3)       Of the 119 who began negotiations, there are 39 who appear not to have ever received a ransom payment.

4)       75 did not engage in any negotiation, so also appear not to have received any ransom payments.

The group has long evaded identification, with LockBitSupp shrouded in online anonymity due to multiple VPNs, VMs, and fake pass-through names and entities. He was so bold that he even offered a $10 million reward to anyone that could reveal his identity.[3]

This revelation comes in the wake of a substantial operation by UK law enforcement, which infiltrated LockBit’s systems, executed multiple arrests, dismantled its infrastructure, and intercepted internal communications, effectively reducing LockBit’s criminal operations — but not stopping or deterring them. This was dubbed Operation Cronos and initiated in February 2024.[4]

Details disclosed by the United States Office of Foreign Assets Control (OFAC) reveal Khoroshev, aged 31 and residing in Russia, is under sanction, with his designation including various email and cryptocurrency addresses, alongside details from his Russian passport. Furthermore, the United States has filed a comprehensive indictment against him.[5] He also faces 26 criminal charges, including extortion and hacking, carrying a cumulative maximum penalty of 185 years in prison. The Justice Department has also issued a $10 million bounty for information leading to his arrest.

‘”This identification and charging of Khoroshev mark a significant milestone,” remarked Principal Deputy Assistant Attorney General Nicole Argentieri in a statement on Tuesday. “Through the meticulous efforts of our investigators and prosecutors, we have unveiled the individual behind LockBitSupp.”’[6]

According to the indictment, Khoroshev is alleged to have served as the developer and administrator of the LockBit ransomware group from its inception in September 2019 through May 2024, typically receiving a 20 percent share of each ransom payment extorted from LockBit victims.

Federal authorities utilized LockBit’s existing victim shaming website layout to disseminate press releases and provide free decryption tools. Following the FBI’s intervention, LockBitSupp reassured partners and affiliates via Russian cybercrime forums that the ransomware operation remained fully operational. Additional darknet websites were launched, promising the release of data stolen from several LockBit victims prior to the FBI’s intervention.

Fig. 2. Lockbit Victim Shaming Portal With FBI Takeover.[7]

Despite LockBitSupp’s claims of invincibility, law enforcement efforts have made strides. The group’s modus operandi included “double extortion,” demanding separate ransom payments for both unlocking hijacked systems and promising to delete stolen data. However, the Justice Department asserts LockBit never followed through on deleting victim data, regardless of ransom payments made — all the more reason why you should not pay or trust these types.

Khoroshev marks the sixth individual indicted as an active member of LockBit. Among those indicted are Russian nationals Artur Sungatov and Ivan Gennadievich Kondratyev, alias “Bassterlord,” charged with deploying LockBit against targets in various industries across multiple countries.[8]

Lastly, leading threat intel consultancy Recorded Future facilitated an interview with LockbitSupp over an encrypted app via the dark web, where he said they got the wrong guy, among other things. [9] The interview is linked here thanks to hard work of The Record from Recorded Future News and Dmitry Smilyanets!

Disclaimer:

All citations and statements are from publicly available reports. No private info was disclosed in this article. Feedback is welcome. Attempts to retaliate against or censor my research and/or writing will be reported (you will be blocked). This was drafted with the current info, and future info could change things.

About the Author:

Jeremy Swenson is a disruptive-thinking security entrepreneur, futurist/researcher, and senior management tech risk consultant. He is a frequent speaker, published writer, podcaster, and even does some pro bono consulting in these areas. He holds an MBA from St. 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 Federal Reserve Secure Payment Task Force, the Crystal, Robbinsdale and New Hope Citizens Police Academy, and the Minneapolis FBI Citizens Academy.

References:

[1] Goodin, Dan. “Ransomware mastermind LockBitSupp reveled in his anonymity—now he’s been ID’d.” Ars Technical. 05/07/24. https://arstechnica.com/security/2024/05/the-mastermind-of-the-prolific-ransomware-group-lockbit-has-finally-been-unmasked/

[2] National Crime Agency (NCA). “LockBit leader unmasked and sanctioned.” Viewed 05/10/24. https://www.nationalcrimeagency.gov.uk/news/lockbit-leader-unmasked-and-sanctioned

[3] Burgess, Matt. “The Alleged LockBit Ransomware Mastermind Has Been Identified.” Wired. 05/07/24. https://www.wired.com/story/lockbitsupp-lockbit-ransomware/

[4] Boyton, Christopher. “Unveiling the Fallout: Operation Cronos’ Impact on LockBit Following Landmark Disruption.” Trend Micro. 04/03/24. https://www.trendmicro.com/en_us/research/24/d/operation-cronos-aftermath.html

[5] US Attorneys Office: NJ. “U.S. Charges Russian National with Developing and Operating Lockbit Ransomware.” 05/07/24. https://www.justice.gov/usao-nj/pr/us-charges-russian-national-developing-and-operating-lockbit-ransomware

[6] Sean Powers, Sean; Abdul-Malik, Jade; Temple Raston, Dina. “In interview, LockbitSupp says authorities outed the wrong guy.” The Record by Recorded Future. 05/09/24. https://therecord.media/lockbitsupp-interview-ransomware-cybercrime-lockbit  

[7] Boyton, Christopher. “Unveiling the Fallout: Operation Cronos’ Impact on LockBit Following Landmark Disruption.” Trend Micro. 04/03/24. https://www.trendmicro.com/en_us/research/24/d/operation-cronos-aftermath.html

[8] FlashPoint. “Indictment-USA-v.-Ivan-Kondratyev.” 05/17/22. https://flashpoint.io/wp-content/uploads/Indictment-USA-v.-Ivan-Kondratyev.pdf

[9] Sean Powers, Sean; Abdul-Malik, Jade; Temple Raston, Dina. “In interview, LockbitSupp says authorities outed the wrong guy.” The Record by Recorded Future. 05/09/24. https://therecord.media/lockbitsupp-interview-ransomware-cybercrime-lockbit

Top Pros and Cons of Disruptive Artificial Intelligence (AI) in InfoSec

Fig. 1. Swenson, Jeremy, Stock; AI and InfoSec Trade-offs. 2024.

Disruptive technology refers to innovations or advancements that significantly alter the existing market landscape by displacing established technologies, products, or services, often leading to the transformation of entire industries. These innovations introduce novel approaches, functionalities, or business models that challenge traditional practices, creating a substantial impact on how businesses operate (ChatGPT, 2024). Disruptive technologies typically emerge rapidly, offering unique solutions that are more efficient, cost-effective, or user-friendly than their predecessors.

The disruptive nature of these technologies often leads to a shift in market dynamics, digital cameras or smartphones for example. These with new entrants or previously marginalized players gain prominence while established entities may face challenges in adapting to the transformative changes (ChatGPT, 2024). Examples of disruptive technologies include the advent of the internet, mobile technology, and artificial intelligence (AI), each reshaping industries and societal norms. Here are four of the leading AI tools:

1.       OpenAI’s GPT:

OpenAI’s GPT (Generative Pre-trained Transformer) models, including GPT-3 and GPT-2, are predecessors to ChatGPT. These models are known for their large-scale language understanding and generation capabilities. GPT-3, in particular, is one of the most advanced language models, featuring 175 billion parameters.

2.       Microsoft’s DialoGPT:

DialoGPT is a conversational AI model developed by Microsoft. It is an extension of the GPT architecture but fine-tuned specifically for engaging in multi-turn conversations. DialoGPT exhibits improved dialogue coherence and contextual understanding, making it a competitor in the chatbot space.

3.       Facebook’s BlenderBot:

BlenderBot is a conversational AI model developed by Facebook. It aims to address the challenges of maintaining coherent and contextually relevant conversations. BlenderBot is trained using a diverse range of conversations and exhibits improved performance in generating human-like responses in chat-based interactions.

4.       Rasa:

Rasa is an open-source conversational AI platform that focuses on building chatbots and voice assistants. Unlike some other models that are pre-trained on large datasets, Rasa allows developers to train models specific to their use cases and customize the behavior of the chatbot. It is known for its flexibility and control over the conversation flow.

Here is a list of the pros and cons of AI-based infosec capabilities.

Pros of AI in InfoSec:

1. Improved Threat Detection:

AI enables quicker and more accurate detection of cybersecurity threats by analyzing vast amounts of data in real-time and identifying patterns indicative of malicious activities. Security orchestration, automation, and response (SOAR) platforms leverage AI to analyze and respond to security incidents, allowing security teams to automate routine tasks and respond more rapidly to emerging threats. Microsoft Sentinel, Rapid7 InsightConnect, and FortiSOAR are just a few of the current examples

2. Behavioral Analysis:

AI can perform behavioral analysis to identify anomalies in user behavior or network activities, helping detect insider threats or sophisticated attacks that may go unnoticed by traditional security measures. Behavioral biometrics, such as analyzing typing patterns, mouse movements and ram usage, can add an extra layer of security by recognizing the unique behavior of legitimate users. Systems that use AI to analyze user behavior can detect and flag suspicious activity, such as an unauthorized user attempting to access an account or escalate a privilege.

3. Enhanced Phishing Detection:

AI algorithms can analyze email patterns and content to identify and block phishing attempts more effectively, reducing the likelihood of successful social engineering attacks.

4. Automation of Routine Tasks:

AI can automate repetitive and routine tasks, allowing cybersecurity professionals to focus on more complex issues. This helps enhance efficiency and reduces the risk of human error.

5. Adaptive Defense Systems:

AI-powered security systems can adapt to evolving threats by continuously learning and updating their defense mechanisms. This adaptability is crucial in the dynamic landscape of cybersecurity.

6. Quick Response to Incidents:

AI facilitates rapid response to security incidents by providing real-time analysis and alerts. This speed is essential in preventing or mitigating the impact of cyberattacks.

Cons of AI in InfoSec:

1. Sophistication of Attacks:

As AI is integrated into cybersecurity defenses, attackers may also leverage AI to create more sophisticated and adaptive threats, leading to a continuous escalation in the complexity of cyberattacks.

2. Ethical Concerns:

The use of AI in cybersecurity raises ethical considerations, such as privacy issues, potential misuse of AI for surveillance, and the need for transparency in how AI systems operate.

3. Cost and Resource Intensive:

Implementing and maintaining AI-powered security systems can be resource-intensive, both in terms of financial investment and skilled personnel required for development, implementation, and ongoing management.

4. False Positives and Negatives:

AI systems are not infallible and may produce false positives (incorrectly flagging normal behavior as malicious) or false negatives (failing to detect actual threats). This poses challenges in maintaining a balance between security and user convenience.

5. Lack of Human Understanding:

AI lacks contextual understanding and human intuition, which may result in misinterpretation of certain situations or the inability to recognize subtle indicators of a potential threat. This is where QA and governance come in case something goes wrong.

6. Dependency on Training Data:

AI models rely on training data, and if the data used is biased or incomplete, it can lead to biased or inaccurate outcomes. Ensuring diverse and representative training data is crucial to the effectiveness of AI in InfoSec.

About the author:

Jeremy Swenson is a disruptive-thinking security entrepreneur, futurist / researcher, and senior management tech risk consultant. He is a frequent speaker, published writer, podcaster, and even does some pro bono consulting in these areas. He holds an MBA from St. 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 Federal Reserve Secure Payment Task Force, the Crystal, Robbinsdale and New Hope Citizens Police Academy, and the Minneapolis FBI Citizens Academy.

Top 10 Ways SMBs Can Mitigate Cyber Risks and Threats in 2023.

Fig. 1. Stock Virus Infographic, 2023.

#smbinfosec #cyberrisk #techrisk #techinnovation #infosec #infosec #cloudcomputing 
#cyberdefense #disinformation #cio #ciso #cto #tech #ransomwareattack #123backup

1) Educate Employees About Cyber Threats and Hold Them Accountable:

Educate your employees about online threats and how to protect your business’s data, including safe use of social networking sites. Depending on the nature of your business, employees might be introducing competitors to sensitive details about your firm’s internal business. Employees should be informed about how to post online in a way that does not reveal any trade secrets to the public or competing businesses. Use games with training and hold everyone accountable to security policies and procedures. This needs to be embedded in the culture of your company. Register for free DHS cyber training here and/or use the free DHS SMB cyber resource toolkit. Most importantly, sign up for DHS CISA e-mail alerts specific to your company and industry needs and review the alerts – Sign up here. Use the free DHS developed CSET (Cybersecurity Evaluation Tool) to assess your security posture – High, Med, or Low. CSET is downloadable here.

2) Protect Against Viruses, Spyware, and Other Malicious Code:

Make sure each of your business’s computers are equipped with antivirus software and antispyware and updated regularly. Such software is readily available online from a variety of vendors. All software vendors regularly provide patches and updates to their products to correct security problems and improve functionality. Configure all software to install updates automatically. Especially watch out for freeware that contains malvertising. Make sure submission forms can block spam and can block code execution (cross-side scripting attacks).

3) Secure Your Networks:

Safeguard your Internet connection by using a firewall and encrypting information. If you have a Wi-Fi network, make sure it is secure and hidden – not publicly broadcasted. To hide your Wi-Fi network, set up your wireless access point or router so it does not broadcast the network name, known as the Service Set Identifier (SSID). Also, have a secure strong password to protect access to the router. (xbeithyg18695843%&*&RELxu75IGO) — example. Lastly, use a VPN (virtual private network) to encrypt data in transit, especially when working from home.

4) Control Physical Access to Computers and Network Components:

Prevent access or use of business computers by unauthorized individuals. Laptops can be particularly easy targets for theft or can be lost, so lock them up when unattended. Make sure a separate user account is created for each employee and require strong passwords. Administrative privileges should only be given to trusted IT staff and key personnel — with approval records.

5) Create A Mobile Device Protection Plan:

Require users to password-protect their devices, encrypt their data, and install security apps to prevent criminals from stealing information while the phone is on public networks. Use a containerization application to separate personal data from company data. Be sure to set reporting procedures for lost or stolen equipment.

6) Establish Security Practices and Policies to Protect Sensitive Information:

Establish policies on how employees should handle and protect personally identifiable information and other sensitive data. Clearly outline the consequences of violating your business’s cybersecurity policies and who is accountable. Base your security strategy significantly on the NIST Cybersecurity Framework 1.1: Identify, Detect Defend, Respond, and Recover — a respected standard that easy to understand (Fig. 1). The NIST Cybersecurity Framework Small Business Resources are linked here.

Fig. 2. NIST CSF Domains and Sub Areas, NIST, 2022.

7) Employ Best Practices on Payment Cards:

Work with your banks or card processors to ensure the most trusted and validated tools and anti-fraud services are being used. You may also have additional security obligations related to agreements with your bank or processor. Isolate payment systems from other, less secure programs and do not use the same computer to process payments and surf the internet. Outsource some or all of it and know where your risk responsibility ends.

8) Make Backup Copies of Important Business Data and Use Encryption When Possible:

Regularly backup the data on all computers. Critical data includes word processing documents, electronic spreadsheets, databases, financial files, human resources files, and accounts receivable/payable files. Back up data automatically if possible, or at least weekly, and store the copies either offsite or on the cloud. Having all key files backed up via the 3-2-1 rule — three copies of files in two different media forms with one offsite — thus reducing ransomware attack damage.

9) Use A Password Management Tool and Strong Passwords:

Another way to stay safe is by setting passwords that are longer, complex, and thus hard to guess. Additionally, they can be stored and encrypted for safekeeping using a well-regarded password vault and management tool. This tool can also help you to set strong passwords and can auto-fill them with each login — if you select that option. Yet using just the password vaulting tool is all that is recommended. Doing these two things makes it difficult for hackers to steal passwords or access your accounts.

10) Use Only Whitelisted Sites Not Blacklisted Ones or Ones Found Via the Dark Web:

Use only approved whitelisted platforms and sites that do not expose you to data leakages or intrusion on your privacy. Whitelisting is the practice of explicitly allowing some identified websites access to a particular privilege, service, or access. Backlisting is blocking certain sites or privileges. If a site does not assure your privacy, do not even sign up let alone participate.

 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 orgs, and even governments including being a member of the Federal Reserve Secure Payment Task Force. Organizations relish in his ability to bridge gaps and flesh out hidden risk management solutions while at the same time improving processes. He is a frequent speaker, published writer, podcaster, and even does some pro bono consulting in these areas. As a futurist, his writings on digital currency, the Target data breach, and Google combining Google + video chat with Google Hangouts video chat have been validated by many. He holds an MBA from St. 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.