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July 17, 2024

What you need to know about the new SEC Cybersecurity rules

In July 2023, the U.S. Securities and Exchange Commission (SEC) adopted new rules concerning cybersecurity incidents and disclosures. This blog describes the new rules and demonstrates how Darktrace can help organizations achieve compliance with these standards.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Kendra Gonzalez Duran
Principal Analyst
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17
Jul 2024

What is new in 2023 to SEC cybersecurity rules?

Form 8-K Item 1.05: Requiring the timely disclosure of material cybersecurity incidents.

Regulation S-K item 106: requiring registrants’ annual reports on Form 10-K to address cybersecurity risk management, strategy, and governance processes.

Comparable disclosures are required for reporting foreign private issuers on Forms 6-K and 20-F respectively.

What is Form 8-K Item 1.05 SEC cybersecurity rules?

Form 8-K Item 1.05 requires the following to be reported within four business days from when an incident is determined to be “material” (1), unless extensions are granted by the SEC under certain qualifying conditions:

“If the registrant experiences a cybersecurity incident that is determined by the registrant to be material, describe the material aspects of the nature, scope, and timing of the incident, and the material impact or reasonably likely material impact on the registrant, including its financial condition and results of operations.” (2, 3)

How does the SEC define cybersecurity incident?

Cybersecurity incident defined by the SEC means an unauthorized occurrence, or a series of related unauthorized occurrences, on or conducted through a registrant’s information systems that jeopardizes the confidentiality, integrity, or availability of a registrant’s information systems or any information residing therein. (4)

How can Darktrace assist in the process of disclosing incidents to the SEC?

Accelerate reporting

Darktrace’s Cyber AI Analyst generates automated reports that synthesize discrete data points potentially indicative of cybersecurity threats, forming reports that provide an overview of the evolution and impact of a threat.

Thus, when a potential threat is identified by Darktrace, AI Analyst can quickly compile information that organizations might include in their disclosure of an occurrence they determined to be material, including the following: incident timelines, incident events, incident summary, related model breaches, investigation process (i.e., how Darktrace’s AI conducted the investigation), linked incident events, and incident details. The figure below illustrates how Darktrace compiles and presents incident information and insights in the UI.

Overview of information provided in an ‘AI Analyst Report’ that could be relevant to registrants reporting a material cybersecurity incident to the SEC
Figure 1: Overview of information provided in an ‘AI Analyst Report’ that could be relevant to registrants reporting a material cybersecurity incident to the SEC

It should be noted that Instruction 4 to the new Form 8-K Item 1.05 specifies the “registrant need not disclose specific or technical information about its planned response to the incident or its cybersecurity systems, related networks and devices, or potential system vulnerabilities in such detail as would impede the registrant’s response or remediation of the incident” (5).

As such, the incident report generated by Darktrace may provide more information, including technical details, than is needed for the 8-K disclosure. In general, users should take appropriate measures to ensure that the information they provide in SEC reports meets the requirements outlined by the relevant regulations. Darktrace cannot recommend that an incident should be reported, nor report an incident itself.

Determine if a cybersecurity incident is material

Item 1.05 requires registrants to determine for themselves whether cybersecurity incidents qualify as ‘material’. This involves considerations such as ‘the nature scope and timing of the incident, and the material impact or reasonably likely material impact on the registrant, including its financial condition and results of operations.’

While it is up to the registrant to determine, consistent with existing legal standards, the materiality of an incident, Darktrace’s solution can provide relevant information which might aid in this evaluation. Darktrace’s Threat Visualizer user interface provides a 3-D visualization of an organization’s digital environment, allowing users to assess the likely degree to which an attack may have spread throughout their digital environment. Darktrace Cyber AI Analyst identifies connections among discrete occurrences of threatening activity, which can help registrants quickly assess the ‘scope and timing of an incident'.

Furthermore, in order to establish materiality it would be useful to understand how an attack might extend across recipients and environments. In the image below, Darktrace/Email identifies how a user was impacted across different platforms. In this example, Darktrace/Email identified an attacker that deployed a dual channel social engineering attack via both email and a SaaS platform in an effort to acquire login credentials. In this case, the attacker useding a legitimate SharePoint link that only reveals itself to be malicious upon click. Once the attacker gained the credentials, it proceeded to change email rules to obfuscate its activity.

Darktrace/Email presents this information in one location, making such investigations easier for the end user.

Darktrace/Email indicating a threat across SaaS and email
Figure 2: Darktrace/Email indicating a threat across SaaS and email

What is regulation S-K item 106 of the SEC cybersecurity rules?

The new rules add Item 106 to Regulation S-K requiring registrants to disclose certain information regarding their risk management, strategy, and governance relating to cybersecurity in their annual reports on Form 10-K. The new rules add Item 16K to Form 20-F to require comparable disclosure by [foreign private issuers] in their annual reports on Form 20-F. (6)

SEC cybersecurity rules: Risk management

Specifically, with respect to risk management, Item 106(b) and Item 16K(b) require registrants to describe their processes, if any, for assessing, identifying, and managing material risks from cybersecurity threats, as well as whether any risks from cybersecurity threats, including as a result of any previous cybersecurity incidents, have materially affected or are reasonably likely to materially affect them. The new rules include a non-exclusive list of disclosure items registrants should provide based on their facts and circumstances. (6)

SEC cybersecurity rules: Governance

With respect to governance, Item 106 and Item 16K require registrants to describe the board of directors’ oversight of risks from cybersecurity threats (including identifying any board committee or subcommittee responsible for such oversight) and management’s role in assessing and managing material risks from cybersecurity threats. (6)

How can Darktrace solutions aid in disclosing their risk management, strategy, and governance related to cybersecurity?

Impact scores

Darktrace End-to-End (E2E) leverages AI to understand the complex relationships across users and devices to model possible attack paths, giving security teams a contextual understanding of risk across their digital environments beyond isolated CVEs or CVSS scores. Additionally, teams can prioritize risk management actions to increase their cyber resilience through the E2E Advisory dashboard.

Attack paths consider:

  • Potential damages: Both the potential consequences if a given device was compromised and its immediate implications on other devices.
  • Exposure: Devices' level of interactivity and accessibility. For example, how many emails does a user get via mailing lists and from what kind of sources?
  • Impact: Where a user or asset sits in terms of the IT or business hierarchy and how they communicate with each other. Darktrace can simulate a range of possible outcomes for an uncertain event.
  • Weakness: A device’s patch latency and difficulty, a composite metric that looks at attacker MITRE methods and our own scores to determine how hard each stage of compromise is to achieve.

Because the SEC cybersecurity rules require “oversight of risks from cybersecurity threats” and “management’s role in assessing and managing material risks from cybersecurity threats” (6), the scores generated by Darktrace E2E can aid end-user’s ability to identify risks facing their organization and assign responsibilities to address those risks.

E2E attack paths leverage a deep understanding of a customer’ digital environment and highlight potential attack routes that an attacker could leverage to reach critical assets or entities. Difficulty scores (see Figure 5) allow security teams to measure potential damage, exposure, and impact of an attack on a specific asset or entity.

An example of an attack path in a digital environment
Figure 3: An example of an attack path in a digital environment

Automatic executive threat reports

Darktrace’s solution automatically produces Executive Threat Reports that present a simple visual overview of model breaches (i.e., indicators of unusual and threatening behaviors) and activity in the network environment. Reports can be customized to include extra details or restricted to high level information.

These reports can be generated on a weekly, quarterly, and yearly basis, and can be documented by registrants in relation to Item 106(b) to document parts of their efforts toward assessing, identifying, and managing material risks from cybersecurity threats.

Moreover, Cyber AI Analyst incident reports (described above) can be leveraged to document key details concerning significant previous incidents identified by the Darktrace solution that the registrant determined to be ‘material’.

While the disclosures required by Item 106(c) relate to the governance processes by which the board of directors, the management, and other responsible bodies within an organization oversee risks resulting from cybersecurity threats, the information provided by Darktrace’s Executive Threat Reports and Cyber AI Analyst incident reports can also help relevant stakeholders communicate more effectively regarding the threat landscape and previous incidents.

DISCLAIMER

The material above is provided for informational purposes only. This summary does not constitute legal or compliance advice, recommendations, or guidance. Darktrace encourages you to verify the contents of this summary with your own advisors.

References

  1. Note that the rule does not set forth any specific timeline between the incident and the materiality determination, but the materiality determination should be made without unreasonable delay.
  2. https://www.sec.gov/files/form8-k.pdf
  3. https://www.sec.gov/news/press-release/2023-139
  4. https://www.ecfr.gov/current/title-17/chapter-II/part-229
  5. https://www.sec.gov/files/form8-k.pdf
  6. https://www.sec.gov/corpfin/secg-cybersecurity
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Kendra Gonzalez Duran
Principal Analyst

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July 7, 2026

Securing AI: Analysis of the Complete Security Stack with Governance and Controls

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Why traditional cybersecurity approaches are not enough for AI

AI adoption outpaces most security programs’ ability to adapt.  That gap is now one of the most consequential sources of cyber risk facing enterprises. As organizations embed generative and agentic AI into development workflows, business operations, and security tooling itself, the question is no longer whether AI will introduce risk. The question is whether organizations understand where that risk actually lives and how to manage it operationally.  

Two recent pieces of guidance underscore this shift:

  1. The upcoming Cybersecurity Framework Profile for AI from NIST
  1. The Five Eyes government guidance on the careful adoption of agentic AI services

Taken together, they point to a critical conclusion. AI security cannot be reduced to model hardening or prompt filtering. It requires a defense in depth strategy that treats AI as both a new attack surface and a force multiplier for defense, while accounting for how AI fundamentally changes scale, speed, and autonomy.  

Recent threat research suggests that today's cyber risk is driven less by initial compromise and more by an adversary's ability to blend into normal operations over time. AI systems create the same exposure in a new form: more autonomy, more scale, and more opportunities for risky behavior to blend into normal operations.

How NIST defines the three core pillars of AI security

The NIST profile organizes AI risk across three inseparable focus areas that span all cybersecurity functions, Secure, Defend and Thwart. These areas are not sequential. They exist simultaneously and must be addressed together.

Secure

This treats AI as an attack surface. It includes models, prompts, agents, pipelines, training and inference data, retrieval augmented generation corpora, and the AI supply chain itself. AI systems are opaque, probabilistic, and non-deterministic by design. Some vulnerabilities are inherent in how models are trained or how data is sourced. Traditional patching does not fully mitigate these risks. This is also where many enterprises are weakest today and, critically, where many security programs stop.  

Defend

This is AI as a defensive force multiplier. AI can improve detection speed, scale, correlation, and response, but only if the right models are used and operationalized correctly. Machine-speed behavior-based detection, response and containment becomes critical in defending non-deterministic systems. Accuracy, explainability, governance, testing, validation, and integration into SOC workflows matter as much as capability. Without those controls, hallucination risk, over automation, and misplaced trust become security risks themselves.  

Thwart

This treats AI as an adversarial accelerant. Threat actors are already using AI to generate targeted social engineering attacks, deepfakes, malware, and autonomous attack agents. Asymmetric warfare is highlighting faster vulnerability discovery and exploitation with a lag on patch development, testing and deployment.  

How this looks in practice

Darktrace researchers observed scaled, automated exploitation of the React2Shell vulnerability within days of disclosure. A vulnerable cloud asset was exploited in under 120 seconds of being deployed. Darktrace research team observed an AI/LLM-generated malware sample used in exploitation activity tied to React2Shell. The significance isn't novelty. It is that AI lowers the barrier to producing usable offensive tooling and compresses the time between experimentation and deployment.  

Tactics are getting more and more creative in order to string together steps of an attack kill chain. This creates a dependency on behavior-based detection, autonomous investigation, autonomous containment, training, resilience investment, and recovery planning across the entire enterprise.

Why agentic AI fundamentally changes enterprise cyber risk

The Five Eyes guidance on agentic AI highlights material changes to the cyber risk profile of an organization. Unlike generative AI systems that produce content for human consumption, agentic AI systems reason, plan, and act autonomously across tools, data, and environments. That autonomy, combined with access to real systems, amplifies the impact of traditional cyber failures and introduces new system level risks that are difficult to predict, observe, and contain.  

Risk in agentic systems does not live in the model alone. It emerges from interactions between models, prompts, memory, tools, APIs, identities, privileges, inter-agent trust relationships, and human assumptions baked into design. Vulnerabilities are often introduced through data, connectors, natural language interfaces, protocols, and drift by design.

In supply-chain incidents, attackers did not need sophisticated exploits to scale impact. They abused trusted systems built for automation and implicit access. Agentic AI inherits that model. Once a system can act across tools, data, and workflows, compromise propagates through trust relationships that were never designed for machine autonomy.

The major agentic AI risk classes include the following:  

  • The identity control for non-human identities or autonomous agents makes it difficult to mitigate over-permissioning, limiting access, scope, and duration, as well as access hygiene
  • Agents are frequently over permissioned
  • Compromised tools inherit agent authority
  • Static secrets enable impersonation
  • Implicit trust between agents enables lateral movement

Design and configuration risks compound this, including privileges evaluated once at startup, poor segmentation, unvetted third party tools, reused authorization decisions outside their original context, and guardrail limitations.  

Behavioral risk  

Agents can optimize for goals in unsafe ways, misinterpret ambiguous intent, chain actions into unintended sequences, change behavior during evaluation, and exhibit deceptive or sycophantic responses.  

Structural risk  

Structural risk follows from agentic systems that are tightly coupled, multicomponent ecosystems. Failures can propagate across agents. Hallucinations cascade downstream. Resource exhaustion becomes systemic. Tool misuse enables indirect prompt injection and command execution. Rogue agents can poison peer agents through trust relationships.  

Accountability

Accountability becomes unclear as autonomy increases. Autonomous agents assume human identity permissions, and humans should have clear ownership of these agents, but they don’t, and this model is flawed. Decision paths are opaque and non-deterministic. Logs are fragmented and difficult to interpret. Reproducing an incident will be impossible without explicit design for observability and forensics. An agent compromise is functionally an insider threat, often with better access and fewer behavioral constraints than a human.  

What does defense in depth look like for AI?

Agentic AI runs on software, networks, identities, and data. It must be governed using the same foundational principles that have proven resilient under uncertainty, including secure by design, defense in depth, zero trust, least privilege, continuous monitoring, behavior-based advanced threat detection and containment, and incident response and recovery.

Core components to a Defense in depth Strategy for Securing the use of AI:

  • Strong, precise identity control plane to include an identity per agent (cryptographic, non‑shared)
    • Privilege monitoring and just‑in‑time access
  • Data Governance
  • Secure‑by‑default configurations
    • Security Posture Management  
    • Zero Trust principles  
  • Strong guardrails, deny‑by‑default policies, and isolation
  • Explicit instruction hierarchies and controlled context
  • Behavioral-based detection across entire enterprise to include inputs, tools, and outputs as well as AI used on the endpoint, across the network, cloud, SaaS, email, and OT
    • Runtime anomaly detection and goal‑drift detection
    • Autonomous containment to mitigate risk and minimize damage
  • Hard boundaries on autonomy and delegation
  • Testing, Evaluation, Validation and Verification  
    • Determine when autonomous action and when human in the loop
    • Adversarial training and agent‑specific testing
    • Simulation, red teaming, and chaos testing
  • Kill‑switches, rollback, and containment mechanisms
    • Forensics data captures, interpretability, autonomous containment, and remediation/recovery plans  

Until standards, tooling, and assurance methods mature, organizations should assume agentic AI systems will behave unexpectedly and design deployments around resilience, behavior-based detection, reversibility, and containment, not efficiency.

How security leaders should prepare for enterprise AI adoption

AI security is not model security alone. Data, pipelines, identities, and agents are first class assets. Many AI attacks succeed through standard cyber failures amplified by AI. Identity, data, and supply chain risk dominate. Behavior-based detection and response are critical, not optional. Logging, provenance, versioning, and forensics data capture of detections are mandatory because you cannot investigate or recover from AI incidents without them.  

Risk will often be visible in behavior before it is clearly defined in policy or guidance. The same pattern has been seen in pre-CVE disclosure detection, where abnormal activity appears before the industry has named or described the vulnerability. AI systems introduce that uncertainty by design.

Security leaders should prioritize controls before AI is fully deployed, avoid generic AI security checklists, integrate AI risk into existing cyber programs, and mitigate the risk of non-deterministic technology with continuous oversight, monitoring, behavior analytics, anomaly detection, autonomous investigation, and autonomous containment.

Visibility has a different connotation with AI. Previously, audit logging worked for software/people, but with Generative AI-based systems, interpretability and explainability is difficult to understand, you cannot "undo" what has been done, or see the logic or control a chain of events. This is why behavioral-based detections and containment becomes critical.  

What capabilities should every AI security program include?

If an organization asked “what must be in place before scaling AI?”:

  1. AI Risk board and approval workflow
  1. IAM + PAM for all AI services and agents
  1. AI asset inventory
  1. Prompt/output DLP with sanctioned AI access – This is not just pre- and post- filters, but behavior-based detections of semantic interface as well as behavior-based analysis of output with associated risk context.  
  1. Shadow AI identification
  1. Secure MLOps – This is an entire paper itself
  1. Runtime guardrails and tool restrictions
    • Including AI Gateway/SASE/Zero trust/
  1. Runtime security with behavior-based detections
    • Complete visibility, monitoring, behavior analytics, anomaly detection, risk/intent/context evaluation of anomalies, autonomous investigation and autonomous containment of all AI assets across endpoint, network, SaaS, SASE, cloud, OT, email, and messaging platforms
  1. Secure data pipelines and data governance
  1. SOC workflow changes from malicious classification workflows to behavior-based detection workflows
  1. Remediation plans for AI-related incidents  

Layered Governance and Security Stack for Securing AI  

The following outline considers governance and security tools that should be considered, well-integrated, deployed, tested, operationalized and embedded within security workflows. These tools and controls map to NIST’s CMF for AI.  

These considerations do not need to be implemented in order. Runtime Detect and Respond will help mitigate risk while Governance, Visibility, and Identity mature.

Category Tooling Controls
Governance & Visibility
  • AI asset inventory / AI CMDB
  • Shadow AI discovery
  • SaaS discovery
  • AI usage on non-endpoint managed systems via network or cloud telemetry
  • MCP server/client usage via protocols
  • Browser telemetry
  • Gateway or SASE telemetry
  • Establish a risk board to set up controls
  • Mandatory registration of AI systems
  • Owner, data classification, intended use, and risk tier
  • Supplier disclosure requirements
  • Risk mitigation plan for AI adoption, innovation, or development
Identity, Access & Agent Control

Non-human autonomous agents should not have the full permissions associated with a human user.

  • IAM with workload identities
  • PAM for AI service accounts
  • Secrets management with short-lived tokens
  • Zero Trust principles
  • Identity, permission, and token hygiene
  • Unique identities per model, agent, and pipeline
  • Least privilege for tools, data, and APIs
  • Explicit approval for autonomous actions
Data Security & Privacy
  • Data classification and labeling
  • Enterprise DLP across endpoint, email, network, cloud, and SaaS
  • Forensics data capture after risky detections
  • Prompt-level DLP through behavior-based semantic analysis with risk and intent context
  • Input/interface analysis for risky data requests
  • Output analysis for sensitive data
  • Data integrity evaluation
  • Retention and redaction policies for prompts and responses
Secure MLOps / LLMOps
  • Secure CI/CD with AI-specific gates
  • Model registries with approval workflows
  • Dependency, container, and artifact scanning
  • SBOM/AIBOM generation
  • IaC security scanning
  • Security posture management
  • Misconfiguration identification
  • Hardening recommendations
  • Signed models and prompts
  • Versioned datasets, configurations, logging, and controls
  • Securing data pipelines
  • Controlled promotion
  • Quality assurance
  • Adversarial testing
Runtime Security

Securing runtime goes beyond guardrails and model firewalls to include behavior-based detections, response, and containment.

  • Detection, monitoring, and SOC integration
  • Centralized visibility into prompts, outputs, and tool calls
  • AI-specific detections
  • Behavior-based detection for AI usage patterns
  • Model drift and behavior monitoring
  • Autonomous containment
  • Behavior-based detection of model inputs and outputs
  • Prompt injection detection
  • Model manipulation, including jailbreaking, poisoning, and related attacks
  • Sensitive data access attempts
  • Behavior-based detection across low-code agents, high-code agents, MCP clients and servers, endpoint, network, cloud, email, SaaS, SASE, IoT, and OT
  • Policy enforcement between users, models, tools, agents, SaaS models/tools, and MCP servers/clients
  • Risk, intent, and context evaluation for detections and response actions
Response & Recovery
  • Autonomous containment
  • AI-assisted playbooks
  • Forensics data capture for AI-related events
  • Model rollback mechanisms
  • Backup and restore for models and datasets
  • Kill switch for agents
  • Autonomous response to agents performing risky behaviors
  • Model and dataset rollback
  • Remediation plans
  • Tabletop exercises
  • Supplier coordination plans
  • Post-incident AI performance validation

AI security requires continuous visibility and behavioral detection

AI changes how fast systems move, how decisions are made, and how risk propagates. It does not change the fundamentals of security. Organizations that succeed will be the ones that apply those fundamentals rigorously, assume failure, and build systems that can detect, contain, and recover when AI behaves in ways they did not anticipate. Security is not what AI is allowed to do. It is whether the organization can understand, trust, and control what AI actually does in practice.  

Take this guidance to understand different initiatives that organizations should be considering. Securing AI is the most critical component to AI safety. As organizations invest more in AI adoption, they should be investing in security in order to mitigate the risk of AI adoption. Organizations should be evaluating their governance and security stack to include well-integrated tools that are deployed, tested, operationalized and embedded within security workflows. While organizations mature in governance, visibility and identity access management, they should be investing in behavior-based detection and autonomous containment to mitigate AI risk.  

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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
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