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May 19, 2023

Darktrace Stops Large-Scale Account Hijack

Learn how Darktrace detected and stopped a large-scale account hijack that led to a phishing attack. Protect your business with these insights.
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
Zoe Tilsiter
Cyber Analyst
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19
May 2023

Introduction 

As malicious actors across the threat landscape continue to take advantage of the widespread adoption of Software-as-a-Service (SaaS) platforms and multi-factor authentication (MFA) services to gain unauthorized access to organizations’ networks, it is crucial to have appropriate security tools in place to defend against account compromise at the earliest stage.

One method frequently employed by attackers is account takeover. Account takeovers occur when a threat actor exploits credentials to login to a SaaS account, often from an unusual location where the genuine actor does not usually login from. 

Access to these accounts can be caused by harvesting credentials through phishing emails and password spray attacks, or by exploiting insecure cloud safety practices such as not having MFA enabled on user accounts, requiring only user credentials for authentication. Once the integrity of the account is compromised, the threat actor can conduct further activity, such as delivering malware, reading and exfiltrating sensitive data, and sending out phishing emails to harvest further internal and external user credentials, repeating the attack cycle [1,2]. 

In early 2023, Darktrace detected a large-scale account takeover and phishing attack on the network of a customer in the education sector that affected hundreds of accounts and resulted in thousands of emails being forwarded outside of the network. The exceptional degree of visibility provided by Darktrace DETECT™ allowed for the detection of adversarial activity at every stage of the kill chain, and direct support from the Darktrace Analyst team via the Ask the Expert (ATE) service ensured the customer was fully informed and equipped to implement remedial action. 

Details of Attack Chain

Darktrace observed the same pattern of activity on all hijacked accounts on the customer’s network; login from unfamiliar locations, enablement of a mail forwarding rule that forwards all incoming emails to malicious email addresses, and the sending of phishing emails followed by their deletion. 

Figure 1: Timeline of attack on hijacked SaaS accounts.

Initial Access

Darktrace DETECT first detected anomalous SaaS activity on the customer environment on January 14, 2023, and then again on February 3, when multiple SaaS accounts were observed logging in from atypical locations with rare IP addresses and geographically impossible travel timings, or logging in whilst the account owner was active elsewhere. Subsequent investigation using open-source intelligence (OSINT) sources revealed one of the IP addressed had recently been associated with brute-force or password spray attempt.

This pattern of unusual login behavior persisted throughout the timeframe of the attack, with more unique accounts generating model breaches each day for similarly anomalous logins. As MFA authentication was not enforced for these user logins, the initial intrusion process was enabled by requiring only credentials for authentication.

Sending Emails 

The compromised accounts were also seen sending out emails with the subject ‘Email HELP DESK’ to external and internal recipients. This was likely represented a threat actor employing social engineering tactics to gain the trust of the recipient by posing as an internal help desk.

Mail Forwarding

Following the successful logins, compromised accounts began creating email rules to forward mail to external email addresses, some of which were associated with domains that had hits for malicious activity according to OSINT sources [3].

  • chotunai[.]com
  • bymercy[.]com
  • breazeim[.]com
  • brandoza[.]com

Forwarding mail is a commonly observed tactic during SaaS compromises to control lines of communication. Malicious actors often attempt to insert themselves into ongoing correspondence for illicit purposes, such as exfiltrating sensitive information, gaining persistent access to the compromised email or redirecting invoice payments. 

Email Deletions

Shortly after the mail forwarding activity, compromised accounts were detected performing anomalous email deletions en masse. Further investigation revealed that these accounts had previously sent a large volume of phishing emails and this mass deletion likely represented an attempt to conceal these activities by deleting them from their outboxes.

On February 10, the customer applied a mass password reset on all accounts that Darktrace had identified as compromised and provisioned, privileged accounts with MFA. They have indicated that those measures successfully halted the compromise, addressing the initial point of entry.  

Darktrace Coverage

Using its Self-Learning AI, Darktrace effectively demonstrated its ability to detect unusual SaaS activity that could indicate that an account has been hijacked by malicious actors. Rather than relying on a traditional rules and signature-based approach, Darktrace models develop an understanding of the network itself and can instantly recognize when a compromised deviates from its expected pattern of life.

Figure 2: Detection of unusual SaaS activity on hijacked SaaS account.

Initial Access

Initial access was detected by the following models:

  • Security Integration / High Severity Integration Detection  
  • SaaS / Unusual Activity / Activity from Multiple Unusual IPs 
  • SaaS / Access / Unusual External Source for SaaS Credential Use 
  • SaaS / Compromise / Login From Rare Endpoint While User Is Active 

Initial access was also detected by the following Cyber AI Analyst Incidents:

  • Possible Hijack of Office365 Account 

The model breaches and AI Analyst incidents detected logins from 100% rare external IP addresses in conjunction with a lack of MFA usage, as depicted in Figure 3.

Figure 3: Breach log showing initial detection of a SaaS login from a 100% rare IP where MFA was not used.
Figure 4: Initial detection of unusual SaaS activity visualized in Darktrace's SaaS console.

Mail Forwarding

Mail forwarding was detected by the following models:

  • SaaS / Admin / Mail Forwarding Enabled 

Compromised accounts were largely detected configuring mail forwarding rules to external email addresses, ostensibly to establish persistence on the network and exfiltrate sensitive correspondence.

Figure 5: The enablement of mail forwarding was detected as 100% new or uncommon for the account in question.

Mass Email Deletion

Mass email deletion was detected by the following models:

  • SaaS / Compromise / Suspicious Login and Mass Email Deletes 
  • SaaS / Resource / Mass Email Deletes from Rare Location 
Figure 6: Compromised account deleting phishing emails it had previously sent from the outbox.

Darktrace detected accounts performing highly anomalous mass email deletions from rare locations. The actors deleted the email “Email HELP DESK” which was later confirmed as being the primary phishing email used in the attack. Deletions were observed on compromised accounts’ outboxes, presumably to conceal the malicious activity.

Darktrace also detected this linked pattern of activity in sequential models such as: 

  • SaaS / Compromise / Unusual Login, Sent Mail, Deleted Sent
  • SaaS / Compromise / Suspicious Login and Mass Email Deletes 

Ask the Expert

The customer used the ATE service to request more technical information and support concerning the attack. Darktrace’s 24/7 team of analysts were able to offer expert assistance and further details to assist in the subsequent investigations and remediation steps. 

Further Detection and Response  

Unfortunately, the customer did not have Darktrace/Email™ enabled at the time of the attack. Darktrace/Email has visibility over inbound and outbound mail-flow which provides an oversight on potential data loss incidents. In this case, Darktrace DETECT/Email would have been able to provide full visibility over the phishing emails sent by the compromised accounts, as well as the attackers attempts to spoof an internal helpdesk. Further to this, the new Analysis Outlook integration helps employees understand why an email is suspicious and enables them report emails directly to the security team, which helps to continuously build user awareness of phishing attacks. 

Darktrace/Email also enhances Darktrace/Network™ detections by triggering ‘Email Nexus’ models within Darktrace/Network, where malicious activity is detected across the digital estate, correlating moving from SaaS compromised logins to mass email spam being sent out by compromised users

Figure 7: Email Nexus models within the Darktrace/Network enhanced by Darktrace/Email

Darktrace RESPOND™ was not enabled on the customer environment at the time of the attack; if it were, Darktrace would have been able to autonomously take action against the SaaS model breaches detecting across multiple of the kill chain. RESPOND would have disabled the hijacked accounts or force them to log out for a period of time, whilst also disabling the inbox rules that had been established by malicious actors. This would have given the customer’s security team valuable time to analyze the incident and mitigate the situation, preventing the attack from escalating any further. 

Conclusion

Ultimately, Darktrace demonstrated its unparalleled visibility over customer networks which allowed for the detection of this large-scale targeted SaaS account takeover, and the subsequent phishing attack. It underscores the importance of defense in depth; critically, MFA was not enforced for this environment which likely made the targeted organization far more susceptible to compromise via credential theft. The phishing activity detected by Darktrace following this account compromise also highlights the need for email protection in any security stack. 

Darktrace’s visibility meant allowed it to detect the attack at a high degree of granularity, including the account logins, email forwarding rule creations, outbound mail, and the mass deletions of phishing emails. Darktrace’s anomaly-based detection means it does not have to rely on signatures, rules or known indicators of compromise (IoCs) when identifying an emerging threat, instead placing the emphasis on recognizing a user’s deviation from its normal behavior.

However, without the presence of an autonomous response technology able to instantly intervene and stop ongoing attacks, organizations will always be reacting to attacks once the damage is done. Darktrace RESPOND is uniquely placed to take action against suspicious activity as soon as it is detected, preventing attacks from escalating and saving customers from significant disruption to their business.

Credit to: Zoe Tilsiter, Cyber Analyst, Gernice Lee, Cyber Analyst.

Appendices

Models Breached

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Admin / Mail Forwarding Enabled

SaaS / Compliance / Microsoft Cloud App Security Alert Detected

SaaS / Compromise / SaaS Anomaly Following Anomalous Login 

SaaS / Compromise / Unusual Login, Sent Mail, Deleted Sent

SaaS / Compromise / Suspicious Login and Mass Email Deletes 

SaaS / Resource / Mass Email Deletes from Rare Location

SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

SaaS / Unusual Activity / Activity from Multiple Unusual IPs

SaaS / Unusual Activity / Multiple Unusual SaaS Activities 

Security Integration / Low Severity Integration Detection

Security Integration / High Severity Integration Detection

List of IoCs

brandoza[.]com - domain - probable domain of forwarded email address

breazeim[.]com - domain - probable domain of forwarded email address

bymercy[.]com - domain - probable domain of forwarded email address

chotunai[.]com - domain - probable domain of forwarded email address

MITRE ATT&CK Mapping

Tactic: INITIAL ACCESS, PERSISTENCE, PRIVILEGE ESCILATION, DEFENSE EVASION

Technique: T1078.004 – Cloud Accounts

Tactic: COLLECTION

Technique: T1114- Email Collection

Tactic:COLLECTION

Technique: T1114.003- Email Forwarding Rule

Tactic: IMPACT

Technique: T1485- Data Destruction

Tactic: DEFENSE EVASION

Technique: T1578.003 – Delete Cloud Instance

References

[1] Darktrace, 2022, Cloud Application Security_ Protect your SaaS with Self-Learning AI.pdf

[2] https://www.cloudflare.com/en-gb/learning/access-management/account-takeover/ 

[3] https://www.virustotal.com/gui/domain/chotunai.com 

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
Zoe Tilsiter
Cyber 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
Your data. Our AI.
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