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:
Anthropic, frontier model deployment and safety policy communications, 2026.
Reuters, “U.S. Lifts Export Controls on Frontier AI Models Following Security Review,” June 2026.
International AI Safety Report, International AI Safety Report 2026 (London: DSIT and international expert consortium, 2026), https://internationalaisafetyreport.org/.
Siladitya Ray, Forbes reporting on U.S. frontier AI policy shift and export control reversal, 2026.
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
Anthropic, Project Glasswing Technical Findings, June 2026.
Terrence O’Brien, “China May Have Accessed Mythos,” The Verge, June 14, 2026.
Ibid.
Kristian McCann, “Why the US Restricted Anthropic’s Mythos and Fable and What It Means for AI Access,” June 15, 2026.
Hadas Gold, “Anthropic Suspends All Access to Mythos Model After US Government Bans Foreign Nationals Use,” CNN, June 13, 2026.
McCann, “Why the US Restricted Anthropic’s Mythos and Fable.”
Gold, “Anthropic Suspends All Access to Mythos Model.”
O’Brien, “China May Have Accessed Mythos”; Gold, “Anthropic Suspends All Access to Mythos Model.”
McCann, “Why the US Restricted Anthropic’s Mythos and Fable.”
Fig. 1. Quantum ChatGPT Growth Plus NIST AI Risk Management Framework Mashup [1], [2], [3].
Summary:
This year is unique since policy makers and business leaders grew concerned with artificial intelligence (AI) ethics, disinformation morphed, AI had hyper growth including connections to increased crypto money laundering via splitting / mixing. Impressively, AI cyber tools become more capable in the areas of zero-trust orchestration, cloud security posture management (CSPM), threat response via improved machine learning, quantum-safe cryptography ripened, authentication made real time monitoring advancements, while some hype remains. Moreover, the mass resignation / gig economy (remote work) remained a large part of the catalyst for all of these trends.
Introduction:
Every year we like to research and comment on the most impactful security technology and business happenings from the prior year. This year is unique since policy makers and business leaders grew concerned with artificial intelligence (AI) ethics [4], disinformation morphed, AI had hyper growth [5], crypto money laundering via splitting / mixing grew [6], AI cyber tools became more capable – while the mass resignation / gig economy remained a large part of the catalyst for all of these trends. By August 2023 ChatGPT reached 1.43 billion website visits per month and about 180.5 million registered users [7]. This even attracted many non-technical naysayers. Impressively, the platform was only nine months old then and just turned a year old in November [8]. These numbers for AI tools like ChatGPT are going to continue to grow in many sectors at exponential rates. As a result, the below trends and considerations are likely to significantly impact government, education, high-tech, startups, and large enterprises in big and small ways, albeit with some surprises.
1. The Complex Ethics of Artificial Intelligence (AI) Swarms Policy Makers and Industry Resulting in New Frameworks:
The ethical use of artificial intelligence (AI) as a conceptual and increasingly practical dilemma has gained a lot of media attention and research in the last few years by those in philosophy (ethics, privacy), politics (public policy), academia (concepts and principles), and economics (trade policy and patents) – all who have weighed in heavily. As a result, we find this space is beginning to mature. Sovereign nations (The USA, EU, and elsewhere globally) have developed and socialized ethical policies and frameworks [9], [10]. While major corporations motivated by profit are all devising their own ethical vehicles and structures – often taking a legalistic view first [11]. Moreover, The World Economic Forum (WEF) has weighed in on this matter in collaboration with PricewaterhouseCoopers (PWC) [12]. All of this contributes to the accelerated pace of maturity of this area in general. The result is the establishment of shared conceptual viewpoints, early-stage security frameworks, accepted policies, guidelines, and governance structures to support the evolution of artificial intelligence (AI) in ethical ways.
For example, the Department of Defense (DOD) has formally adopted five principles for the ethical development of artificial intelligence capabilities as follows [13]:
Responsible
Equitable
Traceable
Reliable
Governable
Traceable and governable seem to be the most clear and important principles, while equitable and responsible seem gray at best and they could be deemphasized in a heightened war time context. The latter two echo the corporate social responsibility (CSR) efforts found more often in the private sector.
The WEF via PWC has issued its Nine AI Ethical Principles for organizations to follow [14], and The Office of the Director of National Intelligence (ODNI) has released their Framework for AI Ethics [15]. Importantly, The National Institute For Standards in Technology (NIST) has released their AI Risk Management Framework as outlined in Fig. 2. and 3. They also released a playbook to support its implementation and have hosted several working sessions discussing it with industry which we attended virtually [16]. It seems the mapping aspect could take you down many AI rabbit holes, some unforeseen – inferring complex risk. Mapping also impacts how you measure and manage. None of this is fully clear and much of it will change as ethical AI governance matures.
Fig. 3. NIST AI Risk Management Framework: Actors Across AI Lifecycle Stages (AI RMF) 1.0 [18].
The actors in Fig. 3. cover a wide swath of spaces where artificial intelligence (AI) plays, and appropriately so as AI is considered a GPT (general purpose technology) like electricity, rubber, and the like – where it can be applied ubiquitously in our lives [19]. This infers cognitive technology, digital reality, ambient experiences, autonomous vehicles and drones, quantum computing, distributed ledgers, and robotics to name a few. These were all prior to the emergence of generative AI on the scene which will likely put these vehicles to the test much earlier than expected. Yet all of these can be mapped across the AI lifecycle stages in Fig. 3. to clarify the activities, actors, dimensions, and if it gets to build, then more scrutiny will need to be applied.
Scrutiny can come in the form of DevSecOps but that is extremely hard to do with such exponentially massive AI code datasets required by the learning models, at least at this point. Moreover, we are not sure if any AI ethics framework does justice to quality assurance (QA) and secure coding best practices much at this point. However, the above two NIST figures at least clarify relationships, flows, inputs and outputs, but all of this will need to be greatly customized to an organization to have any teeth. We imagine those use cases will come out of future NIST working sessions with industry.
Lastly, the most crucial factor in AI ethics governance is what Fig. 3. calls “People and Planet”. This is because the people and planet can experience the negative aspects of AI in ways the designers did not imagine, and that feedback is valuable to product governance to prevent bigger AI disasters. For example, AI taking control of the air traffic control system and causing reroutes or accidents, or AI malware spreading faster than antivirus products can defend it creating a cyber pandemic. Thus, making sure bias is reduced and safety increased (DOD five AI principles) is key but certainly not easy or clear.
2. ChatGPT and Other Artificial Intelligence (AI) Tools Have Huge Security Risks:
It is fair to start off discussing the risks posed by ChatGPT and related tools to balance out all the positive feature coverage in the media and popular culture in recent months. First of all, with artificial intelligence (AI), every cyber threat actor has a new tool to better send spam, steal data, spread malware, build misinformation mills, grow botnets, launder cryptocurrency through shady exchanges [20], create fake profiles on multiple platforms, create fake romance chatbots, and to build the most complex self-replicating malware that will be akin to zero-day exploits much of the time.
One commentator described it this way in his well circulated LinkedIn article, “It can potentially be a formidable social engineering and phishing weapon where non-native speakers can create flawlessly written phishing emails. Also, it will be much simpler for all scammers to mimic their intended victim’s tone, word choice, and writing style, making it more difficult than ever for recipients to tell the difference between a genuine and fraudulent email” [21]. Think of MailChimp on steroids with a sophisticated AI team crafting millions and billions of phishing e-mails / texts customized to impressively realistic details including phone calls with fake voices that mimic your loved ones building fake corroboration [22].
SAP’s Head of Cybersecurity Market Strategy, Gabriele Fiata, took the words out of our mouths when he described it this way, “The threat landscape surrounding artificial intelligence (AI) is expanding at an alarming rate. Between January to February 2023, Darktrace researchers have observed a 135% increase in “novel social engineering” attacks, corresponding with the widespread adoption of ChatGPT” [23]. This is just the beginning. More malware as a service propagation, fake bank sites, travel scams, and fake IT support centers will multiply to scam and extort the weak including, elders, schools, local government, and small businesses. Then there is the increased likelihood that antivirus and data loss prevention (DLP) tools will become less effective as AI morphs. Lastly, cyber criminals can and will use generative AI for advanced evidence tampering by creating fake content to confuse or dirty the chain of custody, lessen reliability, or outright frame the wrong actor – while the government is confused and behind the tech sector. It is truly a digital arms race.
In the next section we will discuss the possibilities of how artificial intelligence (AI) can enhance information security increasing compliance, reducing risk, enabling new features of great value, and enabling application orchestration for threat visibility.
3. The Zero-Trust Security Model Becomes More Orchestrated via Artificial Intelligence (AI):
The zero-trust model assumes that no user or system, even those within the corporate network, should be trusted by default. Access controls are strictly enforced, and continuous verification is performed to ensure the legitimacy of users and devices. Zero-trust moves organizations to a need-to-know-only access mindset (least privilege) with inherent deny rules, all the while assuming you are compromised. This infers single sign-on at the personal device level and improved multifactor authentication. It also infers better role-based access controls (RBAC), firewalled networks, improved need-to-know policies, effective whitelisting and blacklisting of applications, group membership reviews, and state of the art privileged access management (PAM) tools. Password check out and vaulting tools like CyberArk will improve to better inform toxic combination monitoring and reporting. There is still work in selecting / building the right tech components that fit into (not work against) the infrastructure orchestra stack. However, we believe rapid build and deploy AI based custom middleware can alleviate security orchestration mismatches in many cases easily. All of this is likely to better automate and orchestrate zero-trust abilities so that one part does not hinder another part via complexity fog.
4. Artificial Intelligence (AI) Powered Threat Detection Has Improved Analytics:
Artificial intelligence (AI) is increasingly being used to enhance threat detection capabilities. Machine learning algorithms analyze vast amounts of data to identify patterns indicative of potential security threats. This enables quicker and more accurate identification of malicious activities. Security information and event management (SIEM) systems enhanced with improved machine learning algorithms can detect anomalies in network traffic, application logs, and data flow – helping organizations identify potential security incidents faster.
There will be reduced false positives which has been a sustained issue in the past with large overconfident companies repeatedly wasting millions of dollars per year fine tuning useless data security lakes (we have seen this) that mostly produce garbage anomaly detection reports [25], [26]. Literally the kind good artificial intelligence (AI) laughs at – we are getting there. All the while, the technology vendors try to solve this via better SIEM functionality for an increased price at present. Yet we expect prices to drop really low as the automation matures.
With improved natural language processing (NLP) techniques, artificial intelligence (AI) systems can analyze unstructured data sources, such as social media feeds, photos, videos, and news articles – to assemble useful threat intelligence. This ability to process and understand textual data empowers organizations to stay informed about indicators of compromise (IOCs) and new attack tactics. Vendors that provide these services include Dark Trace, IBM, CrowdStrike, and many startups will likely join soon. This space is wide open and the biases of the past need to be forgotten if we want innovation. Young fresh minds who know web 3.0 are valuable here. Thus, in the future more companies will likely not have to buy but rather can build their own customized threat detection tools informed by advancements in AI platform technology.
5. Quantum-Safe Cryptography Ripens:
Quantum computing is a quickly evolving technology that uses the laws of quantum mechanics to solve problems too complex for traditional computers, like superposition and quantum interference [27]. Some cases where quantum computers can provide a speed boost include simulation of physical systems, machine learning (ML), optimization, and more. Traditional cryptographic algorithms could be vulnerable because they were built and coded with weaker technologies that have solvable patterns, at least in many cases. “Industry experts generally agree that within 7-10 years, a large-scale quantum computer may exist that can run Shor’s algorithm and break current public-key cryptography causing widespread vulnerabilities” [28]. Quantum-safe or quantum-resistant cryptography is designed to withstand attacks from quantum computers, often artificial intelligence (AI) assisted – ensuring the long-term security of sensitive data. For example, AI can help enhance post-quantum cryptographic algorithms such as lattice-based cryptography or hash-based cryptography to secure communications [29]. Lattice-based cryptography is a cryptographic system based on the mathematical concept of a lattice. In a lattice, lines connect points to form a geometric structure or grid (Fig. 5).
This geometric lattice structure encodes and decodes messages. Although it looks finite, the grid is not finite in any way. Rather, it represents a pattern that continues into the infinite (Fig. 6).
Lattice based cryptography benefits sensitive and highly targeted assets like large data centers, utilities, banks, hospitals, and government infrastructure generally. In other words, there will likely be mass adoption of quantum computing based encryption for better security. Lastly, we used ChatGPT as an assistant to compile the below specific benefits of quantum cryptography albeit with some manual corrections [32]:
Detection of Eavesdropping: Quantum key distribution protocols can detect the presence of an eavesdropper by the disturbance introduced during the quantum measurement process, providing a level of security beyond traditional cryptography.
Quantum-Safe Against Future Computers: Quantum computers have the potential to break many traditional cryptographic systems. Quantum cryptography is considered quantum-safe, as it relies on the fundamental principles of quantum mechanics rather than mathematical complexity.
Near Unconditional Security: Quantum cryptography provides near unconditional security based on the principles of quantum mechanics. Any attempt to intercept or measure the quantum state will disturb the system, and this disturbance can be detected. Note that ChatGPT wrongly said “unconditional Security” and we corrected to “near unconditional security” as that is more realistic.
Artificial intelligence (AI) is used not only for threat detection but also in automating response actions [33]. This can include automatically isolating compromised systems, blocking malicious internet protocol (IP) addresses, closing firewalls, or orchestrating a coordinated response to a cyber incident – all for less money. 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. Basically, AI tools will help SOAR tools mature so security operations centers (SOCs) can catch the low hanging fruit; thus, they will have more time for analysis of more complex threats. These AI tools will employ the observe, orient, decide, act (OODA) Loop methodology [34]. This will allow them to stay up to date, customized, and informed of many zero-day exploits. At the same time, threat actors will constantly try to avert this with the same AI but with no governance.
As organizations increasingly migrate to cloud environments, ensuring the security of cloud assets becomes key. Vendors like Microsoft, Oracle, and Amazon Web Services (AWS) lead this space; yet large organizations have their own clouds for control as well. Cloud security posture management (CSPM) tools help organizations manage and secure their cloud infrastructure by continuously monitoring configurations and detecting misconfigurations that could lead to vulnerabilities [35]. These tools automatically assess cloud configurations for compliance with security best practices. This includes ensuring that only necessary ports are open, and that encryption is properly configured. “Keeping data safe in the cloud requires a layered defense that gives organizations clear visibility into the state of their data. This includes enabling organizations to monitor how each storage bucket is configured across all their storage services to ensure their data is not inadvertently exposed to unauthorized applications or users” [36]. This has considerations at both the cloud user and provider level especially considering artificial intelligence (AI) applications can be built and run inside the cloud for a variety of reasons. Importantly, these build designs often use approved plug ins from different vendors making it all the more complex.
Artificial intelligence (AI) is being utilized to strengthen user authentication methods. 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 [37]. Two factor authentication remains the bare standard with many leading identity and access management (IAM) application makers including Okta, SailPoint, and Google experimenting with AI for improved analytics and functionality. Both two factor and multifactor authentication benefit from AI advancements with machine learning via real time access rights reassignment and improved role groupings [38]. However, multifactor remains stronger at this point because it includes something you are, biometrics. The jury is out on which method will remain the security leader because biometrics can be faked by AI [39]. Importantly, AI tools can remove fake accounts or orphaned accounts much more quickly, reducing risk. However, it likely will not get it right 100% of the time so there is a slight inconvenience.
Conclusion and Recommendations:
Artificial intelligence (AI) remains a leading catalyst for digital transformation in tech automation, identity and access management (IAM), big data analytics, technology orchestration, and collaboration tools. AI based quantum computing serves to bolster encryption when old methods are replaced. All of the government actions to incubate ethics in AI are a good start and the NIST AI Risk Management Framework (AI RMF) 1.0 is long overdue. It will likely be tweaked based on private sector feedback. However, adding the DOD five principles for the ethical development of AI to the NIST AI RMF could derive better synergies. This approach should be used by the private sector and academia in customized ways. AI product ethical deviations should be thought of as quality control and compliance issues and remediated immediately.
Organizations should consider forming an AI governance committee to make sure this unique risk is not overlooked or overly merged with traditional web / IT risk. ChatGPT is a good encyclopedia and a cool Boolean search tool, yet it got some things wrong about quantum computing in this article for which we cited and corrected. The Simplified AI text to graphics generator was cool and useful but it needed some manual edits as well. Both of these generative AI tools will likely get better with time.
Artificial intelligence (AI) will spur many mobile malware and ransomware variants faster than Apple and Google can block them. This in conjunction with the fact that people more often have no mobile antivirus on their smart phone even if they have it on their personal and work computers, and a culture of happy go lucky application downloading makes it all the worse. As a result, more breaches should be expected via smart phones / watches / eyeglasses from AI enabled threats.
Therefore, education and awareness around the review and removal of non-essential mobile applications is a top priority. Especially for mobile devices used separately or jointly for work purposes. Containerization is required via a mobile device management (MDM) tool such as JAMF, Hexnode, VMWare, or Citrix Endpoint Management. A bring your own device (BYOD) policy needs to be written, followed, and updated often informed by need-to-know and role-based access (RBAC) principles. This requires a better understanding of geolocation, QR code scanning, couponing, digital signage, in-text ads, micropayments, Bluetooth, geofencing, e-readers, HTML5, etc. Organizations should consider forming a mobile ecosystem security committee to make sure this unique risk is not overlooked or overly merged with traditional web / IT risk. Mapping the mobile ecosystem components in detail is a must including the AI touch points.
The growth and acceptability of mass work from home (WFH) combined with the mass resignation / gig economy remind employers that great pay and culture alone are not enough to keep top talent. At this point AI only takes away some simple jobs but creates AI support jobs, yet the percents of this are not clear this early. Signing bonuses and personalized treatment are likely needed for those with top talent. We no longer have the same office and thus less badge access is needed. Single sign-on (SSO) will likely expand to personal devices (BYOD) and smart phones / watches / eyeglasses. Geolocation-based authentication is here to stay with double biometrics, likely fingerprint, eye scan, typing patterns, and facial recognition. The security perimeter remains more defined by data analytics than physical / digital boundaries, and we should dashboard this with machine learning tools as the use cases evolve.
Cloud infrastructure will continue to grow fast creating perimeter and compliance complexity / fog. Organizations should preconfigure artificial intelligence (AI) based cloud-scale options and spend more on cloud-trained staff. They should also make sure that they are selecting more than two or three cloud providers, all separate from one another. This helps staff get cross-trained on different cloud platforms and plug in applications. It also mitigates risk and makes vendors bid more competitively. There is huge potential for AI synergies with Cloud Security Posture Management (CSPM) tools, and threat response tools – experimentation will likely yield future dividends. Organization should not be passive and stuck in old paradigms. The older generations should seek to learn from the younger generations without bias. Also, comprehensive logging is a must for AI tools.
In regard to cryptocurrency, non-fungible tokens (NFTs), initial coin offerings (ICOs), and related exchanges – artificial intelligence (AI) will be used by crypto scammers and those seeking to launder money. Watch out for scammers who make big claims without details, no white papers or filings, or explanations at all. No matter what the investment, find out how it works and ask questions about where your money is going. Honest investment managers and advisors want to share that information and will back it up with details in many documents and filings [40]. Moreover, better blacklisting by crypto exchanges and banks is needed to stop these illicit transactions erroring far on the side of compliance. This requires us to pay more attention to knowing and monitoring our own social media baselines – emerging AI data analytics can help here. If you are for and use crypto mixer and / or splitter services then you run the risk of having your digital assets mixed with dirty digital assets, you have high fees, you have zero customer service, no regulatory protection, no decent Terms of Service and / or Privacy Policy if any, and you have no guarantee that it will even work the way you think it will.
As security professionals, we are patriots and defenders of wherever we live and work. We need to know what our social media baseline is across platforms. IT and security professionals need to realize that alleviating disinformation is about security before politics. We should not be afraid to talk about this because if we are, then our organizations will stay weak and outdated and we will be plied by the same artificial intelligence (AI) generated political bias that we fear confronting. More social media training is needed as many security professionals still think it is mostly an external marketing thing.
It’s best to assume AI tools are reading all social media posts and all other available articles, including this article which we entered into ChatGPT for feedback. It was slightly helpful pointing out other considerations. Public-to-private partnerships (InfraGard) need to improve and application to application permissions need to be more scrutinized. Everyone does not need to be a journalist, but everyone can have the common sense to identify AI / malware-inspired fake news. We must report undue AI bias in big tech from an IT, compliance, media, and a security perspective. We must also resist the temptation to jump on the AI hype bandwagon but rather should evaluate each tool and use case based on the real-world business outcomes for the foreseeable future.
About the Authors:
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.
Matthew Versaggi is a senior leader in artificial intelligence with large company healthcare experience who has seen hundreds of use-cases. He is a distinguished engineer, built an organization’s “College of Artificial Intelligence”, introduced and matured both cognitive AI technology and quantum computing, has been awarded multiple patents, is an experienced public speaker, entrepreneur, strategist and mentor, and has international business experience. He has an MBA in international business and economics and a MS in artificial intelligence from DePaul University, has a BS in finance and MIS and a BA in computer science from Alfred University. Lastly, he has nearly a dozen professional certificates in AI that are split between the AI, technology, and business strategy.
[37] Muneer, Salman Muneer, Muhammad Bux Alvi, and Amina Farrakh; “Cyber Security Event Detection Using Machine Learning Technique.” International Journal of Computational and Innovative Sciences. Vol. 2, no (2): pg. 42-46. 2023: https://ijcis.com/index.php/IJCIS/article/view/65.
[38] Azhar, Ishaq; “Identity Management Capability Powered by Artificial Intelligence to Transform the Way User Access Privileges Are Managed, Monitored and Controlled.” International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Vol. 9, Issue 1: pg. 4719-4723. January 2021: https://ssrn.com/abstract=3905119
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.
Fig. 1. Former OpenAI CEO Sam Altman and Microsoft CEO Satya Nadella. Getty Images, 2023.
#chatGPT #Microsoft #openai #boardgovernance
Update: Sam Altman is returning to OpenAI as CEO, ending days of drama and negotiations with the help of heavy investor Microsoft and Silicon Valley insiders (Bloomberg, 11/22/23). In sum, there were more issues without Sam than with him and the board realized that pretty fast. So now some board members have to be shown the door.
Some may view a fired executive like Sam Altman as damaged goods but we all know that corporate boards get these things wrong all the time, and it’s more about office politics and cliques than substantive performance.
The board described their decision as a “deliberative review process which concluded that he was not consistently candid in his communications with the board, hindering its ability to exercise its responsibilities. The board no longer has confidence in his ability to continue leading OpenAI.” Yet the board’s statement makes little sense and is out of context for an emerging technology at a time such as this.
As a result of this nonsensical firing, there was likely no job interview when Sam Altman joined Microsoft. He was already validated as a thought leader in the tech and generative AI community, so it was hardly needed. Microsoft CEO Satya Nadella was a fan and already invested billions into OpenAI. He saw the open opportunity and took it fast before another tech company could. The same thing happened when Oracle CEO Larry Ellison hired Mark Hurd in 2010 after HP fired him and the results were great.
This begs the question of how valuable are job interviews in the area of emerging tech or for people with visible achievements. What is the H.R. screener or some tech director in a fiefdom going to ask you? They would hardly understand the likely answers in a meaningful way anyway. I know many tech and business leaders who have wasted time in dumb interviews in contexts such as these and it is a poor reflection of the companies setting them up this way.
In other words, plenty of people will not want to work for OpenAI because of how Altman was publicly treated while Microsoft looks more inclusive and forward-thinking. So I am sure many people will leave OpenAI to follow Altman at Microsoft and that is really how OpenAI shot themselves in the foot especially considering Microsoft’s size.
Any failings and risks designed into ChatGPT are as much the problem of OpenAIs as it is every other company working in this vastly unknown and emerging area of tech. To blame that on Altman in this context seems unreasonable and thus he is a fall guy.
There are good and bad things with AI just like with any technology, yet the good far outweighs the bad in this context. Microsoft knows that there are problems in AI in cyber security, fraud, IP theft, and more. The bigger and more capable their AI team the better they can address these issues, now with Altman’s help.
Now, of course, Altman has to be evaluated on his performance at Microsoft making sure AI stays viable and within the approved guardrails, and hopefully innovates a few solutions to make society better. Yet the free market of other tech companies and regulators also have that responsibility.
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.
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