Blog
/
/
May 22, 2020

Securing AWS Cloud Environments

Discover how self-learning AI in AWS environments detects and beats threats early with enterprise-wide analysis.
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
Andrew Tsonchev
VP, Security & AI Strategy, Field CISO
Default blog image
22
May 2020

Cloud platforms transform the way we build digital infrastructure, allowing us to create incredibly innovative environments for business – but often, it’s at the cost of visibility and control.

With complex hybrid and multi-cloud infrastructures becoming an essential part of increasingly diverse digital estates, the journey to the cloud has fundamentally reshaped the traditional paradigm of the network perimeter, while expanding the attack surface at an alarming rate. Meanwhile, traditional security controls still only offer point solutions that rely on retrospective rules and threat signatures and fail to stop novel and advanced attacks.

To shoulder the weight of shared responsibility for cloud security, organizations require the approach offered by Darktrace DETECT & RESPOND. With Self-Learning AI, DETECT continuously learns what normal ‘patterns of life’ look like for every user, device, virtual machine, and container across an organization. By actively developing a bespoke understanding of ‘self,’ the DETECT can identify the subtle anomalies that point to an advanced attack, without any pre-defined assumptions of ‘good’ or ‘bad' and RESPOND can autonomously interfere to stop emerging threats without disrupting business operations.

As more and more businesses turn to AWS to leverage the benefits of cloud infrastructure, gaining visibility and security for AWS-hosted data and applications is absolutely crucial. The advent of AWS VPC traffic mirroring has allowed Darktrace to shine a light on blind spots in our customers’ AWS environments, ensuring that our Cyber AI security platform can stop any type of threat that emerges. With the AI-powered security securing your AWS environment, you can embrace all the benefits of the cloud with confidence.

Self-learning Cyber AI with granular, real-time visibility

VPC traffic mirroring gives our Self-Learning AI access to granular packet data, allowing DETECT to extract hundreds of features from the raw data and build rich behavioral models for our customers’ AWS cloud environments. This real-time visibility to the underlying fabric of AWS environments provided by VPC traffic mirroring helps Darktrace Cyber AI learn ‘on the job,’ continuously adapting as your business evolves. Darktrace provides the only security solution that learns in real time, a critical feature given the speed and scale of development in the cloud.

Unified control: Correlating patterns across infrastructure

Taking a fundamentally unique approach, DETECT actively correlates activity across AWS and beyond – whether your digital ecosystem includes other cloud environments, SaaS applications, or any range of on- and off-premise infrastructure. From a threat detection perspective, this is crucial, as security events detected in one part of an organization are often part of a broader security incident. This ensures that threats in the cloud are not siloed from monitoring of the rest of the infrastructure, nor are the implications for cloud security ignored when intrusions occur elsewhere in the network.

Neutralizing sophisticated and novel attacks

Legacy security controls miss novel and advanced attacks targeting cloud infrastructure. With VPC traffic mirroring supporting Darktrace Cyber AI’s understanding of an organization’s AWS environment, any slight changes from normal behavior that may indicate a potential threat can be detected immediately. This allows the DETECT to catch the full range of cloud-based attacks, from zero-day malware, to stealthy insider threats.

“Darktrace represents a new frontier in AI-based cyber defense. Our team now has complete real-time coverage across our SaaS applications and cloud containers.”

— CIO, City of Las Vegas

How it works: Using VPC traffic mirroring to analyze AWS traffic

For customers leveraging AWS within an IaaS model, Darktrace uses VPC traffic mirroring to collect metadata from mirrored VPC packets in a Darktrace probe known as a ‘vSensor’. The vSensor captures real-time traffic and selectively forwards relevant metadata to a Darktrace cloud instance or on-premise probe. From here, DETECT correlates VPC traffic with cloud, email, network, and SaaS traffic across a customer’s hybrid and multi-cloud infrastructure for analysis.

By utilizing VPC traffic mirroring in this way, the Immune System can perform deep packet inspection on traffic in the customer’s AWS cloud environment, up to and including the application layer. Hundreds of features are extracted from the raw data, ranging from high-level metrics of data flow quantities, to peer relationship meta-data, to specific application layer events. These features allow Darktrace Cyber AI to build rich behavioral models that let it understand normal patterns of life for the organization and detect malicious activity. It is important that Darktrace is able to construct these metrics from the raw data rather than relying on flow logs alone, as flow logs don't provide the required level of granularity or real-time events within connections.

For non-Nitro AWS instances, we deploy lightweight agents known as ‘OS-Sensors’ that feed relevant traffic to a local vSensor and, in turn, to a Darktrace cloud instance or on-premise probe. Once configured, OS-Sensors can easily be scaled as new instances are spun up. Darktrace also offers a specialized OS-Sensor that provides coverage in containerized systems like Docker and Kubernetes.

Richer context with AWS CloudTrail logs

In addition to analyzing data with VPC traffic mirroring, the DETECT also monitors management and data events within AWS. It does so via HTTP requests for logfiles generated by AWS CloudTrail, which monitors events from all AWS services, including:

  • EC2
  • IAM
  • S3
  • VPC
  • Lambda

Different event types produced via CloudTrail are organized by Darktrace into categories based on the action type and the AWS services that generate it. These different categories show up as metrics in the DETECT user interface, the Threat Visualizer. This information is used to provide even richer context in connection with mirrored traffic in VPCs, as well as all cloud, network, email, and SaaS traffic across a customer’s entire digital environment.

Darktrace deployment scenarios for AWS customers

For IaaS environments, Darktrace deploys a vSensor in each cloud environment. Within AWS environments, the vSensor captures real-time traffic with AWS VPC traffic mirroring. The receiving vSensor processes the data and feeds it back to the cloud-based Darktrace instance. AWS customers additionally have the option of deploying a ‘Darktrace Security Module’ to monitor IaaS management and data events at the API level, such as logins, editing virtual servers, or creating new access credentials.

Figure 1: A cloud-only deployment scenario — Darktrace manages a master cloud probe which receives traffic from sensors and connectors in IaaS and/or SaaS environments.

For hybrid IaaS deployments, Darktrace will similarly deploy vSensors, and OS-Sensors as appropriate. Cloud traffic and event data from AWS and any other cloud environments is then fed to a Darktrace probe in the cloud or on-premise network. For the latter scenario, Darktrace will deploy a physical appliance that ingests real-time network traffic via a SPAN port or network tap, allowing it to correlate patterns across the entire digital ecosystem.

Figure 2: A hybrid cloud deployment scenario, with multi-cloud infrastructure across AWS, Azure and GCP

For hybrid SaaS deployments, Darktrace will deploy provider-specific Darktrace Security Modules on either a physical or cloud-based Darktrace probe, in addition to any other relevant vSensors and OS-Sensors in place. SaaS data is then analyzed and correlated with traffic and user behaviors across AWS, other cloud environments, and any on- and off- premise cyber-physical infrastructure.

Figure 3: A hybrid SaaS deployment scenario

Defense against the full range of threats in the cloud

With the deep insight and powerful reaction capabilities of Cyber AI, Darktrace DETECT & RESPOND are the only proven technologies to stop the full range of cyber-threats in the cloud, including:

  • Critical misconfigurations
  • Insider threat
  • Compromised credentials
  • Novel and advanced malware
  • Password brute-force attacks
  • Data exfiltration
  • Lateral movement
  • Man-in-the-middle attacks
  • Crypto-jacking
  • Violations of policy

Case Studies

Crypto mining malware inadvertently installed

Darktrace detected a mistake from a junior DevOps engineer in a multinational organization with workloads across AWS and Azure and leveraging containerized systems like Docker and Kubernetes. The engineer accidentally downloaded an update that included a crypto miner, which led to an infection across multiple cloud production systems.

After the initial infection, the malware started beaconing out to an external command and control server, which was immediately picked up by Darktrace. With the external connection established and the attack mission instructions delivered, the crypto malware infection was then able to rapidly spread across the organization’s expansive cloud infrastructure at machine speed, infecting 20 cloud servers in under 15 seconds.

Extensive visibility into the organization’s AWS environment via VPC traffic mirroring was a key factor allowing Darktrace Cyber AI to identify the scale of the attack. With the dynamic and unified view across the company’s sprawling hybrid and multi-cloud infrastructure provided by Darktrace, the company’s security team was able to contain the attack within minutes, rather than hours or days. Even though the attack moved at machine speed, by leveraging solutions like VPC traffic mirroring to continuously analyze behavior in the cloud, Darktrace caught the threat at an early enough stage – well before the costs could start to mount.

Developer misuse of AWS cloud infrastructure

At an insurance group, a DevOps Engineer was attempting to build a parallel back-up infrastructure within AWS to replicate the organization’s data center production systems. The technical implementation was perfect, and the back-up systems were created – however, the cost of running the system would have been several million dollars per year.

The DevOps Engineer was unaware of the costs associated with the project and kept management in the dark. The cloud infrastructure was launched, and the costs started rising. Yet with real-time access to the company’s AWS environment provided by VPC traffic mirroring, Darktrace’s Cyber AI was immediately alerted to this unusual behavior, allowing the security team to take preventative action immediately.

With Darktrace Cyber AI, embrace the benefits of AWS

As organizations increasingly turn to the cloud and the threat surface continues to expand, security teams need self-learning AI on their side to gain the strongest insights, illuminate every blind spot, and stop all attacks.

By providing an enterprise-wide Cyber AI platform, Darktrace helps teams overcome the traditional security challenge of manually piecing together incidents across disparate corners of an organization. The unified visibility and control offered by Darktrace PREVENT, DETECTRESPOND, & HEAL reduces the complexity and dashboard fatigue that many teams continue to struggle with, while the system’s multi-dimensional insight enhances its decision-making and threat confidence. Darktrace further augments this process with the Immune System’s AI Analyst capability, which takes the additional step of automatically investigating threats detected by Darktrace and producing concise, AI-generated reports that communicate the full scope of an incident.

With the granular, real-time visibility of VPC traffic mirroring Darktrace, you can be certain your AWS cloud environments are always protected.

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
Andrew Tsonchev
VP, Security & AI Strategy, Field CISO

More in this series

No items found.

Blog

/

AI

/

July 7, 2026

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

ai security stackDefault blog imageDefault blog image

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.  

Continue reading
About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO

Blog

/

AI

/

July 6, 2026

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

ai security nistDefault blog imageDefault blog image

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

Continue reading
About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
Your data. Our AI.
Elevate your network security with Darktrace AI