How to Build an AI Security Runbook for SOC Teams

What an AI Security Runbook Covers

An AI security runbook is a structured, repeatable procedure that defines how a SOC detects, triages, contains, remediates, and reports security incidents involving AI agents. Unlike a traditional application or infrastructure runbook, it must account for incident types unique to agent architectures: prompt injection, unauthorized tool-call execution, agent credential misuse, and unexpected data access. These categories are documented across current voluntary frameworks. OWASP's Top 10 for LLM Applications and its Agentic AI guidance identify prompt injection, excessive agency, and unchecked tool invocation as primary risk classes, while MITRE ATLAS catalogs adversary techniques such as ML supply chain compromise and exfiltration via model output.

No binding regulatory standard mandates a specific runbook structure for AI agent incidents. NIST SP 800-61 Rev.3 provides a lifecycle backbone (preparation, detection and analysis, containment, eradication and recovery, and post-incident activity) that a SOC can extend with AI-specific detection criteria rather than building a parallel process. NIST's AI RMF adds governance functions (Govern, Map, Measure, Manage) that map to runbook ownership and escalation authority, and the Generative AI Profile (NIST AI 600-1) extends this with generative AI risks including data leakage and value-chain integration risks tied to agent tool use.

Building a runbook on this foundation means defining detection criteria and escalation paths per incident type, specifying the runtime signals required for triage, and embedding audit requirements that produce a traceable record of agent identity, permission state, and tool-call history for every incident.

Map Detection Signals to Agent Architecture Elements

Effective triage depends on signals that reflect how agents actually act, not only on final outputs. Map logging and detection to these four architecture elements:

1. Agent identity

   Track each agent as an identity distinct from the human or service account it acts on behalf of, since actions execute through delegated credentials that can be misused independently of the underlying account.

2. Permission scope

   Log the permission scope evaluated at the moment of each action, not only the agent's assigned role, since scope can change dynamically within a session.

3. Tool-call history

   Record the requested action, parameters, and outcome of every tool invocation. Detecting tool-call abuse or excessive agency depends on this history, not on final output alone.

4. Runtime policy enforcement point

   A control point positioned between agent decision-making and tool execution gives the SOC a place to block unauthorized actions before they execute, rather than alerting only after the fact.

Detection Criteria and Evidence Requirements

Generic anomaly thresholds do not map cleanly across AI incident types. Define separate detection logic for each category, and ensure the evidence base needed for triage is already in the log stream.

Detection criteria by incident type

• Prompt injection (direct or indirect): Inspect user prompts and ingested content, including retrieved documents, web pages, and tool outputs, for instructions that diverge from the agent's assigned task scope. Direct and indirect sources require separate detection logic.
• Tool-call abuse or excessive agency: Compare each invocation's parameters and target against the permission scope evaluated at call time, and flag calls exceeding that scope or invoking high-impact actions without a human-in-the-loop checkpoint.
• Agent credential misuse: Correlate agent service accounts and API tokens as distinct identities within IAM and SIEM systems, and flag usage patterns inconsistent with the agent's normal task cadence or originating context.
• Unauthorized data access: Cross-reference data accessed during a session against the task's defined scope and the agent's permission grants at the time of access.

Evidence to capture for every incident

• Agent identity and the human or service account it acted on behalf of
• Permission scope in effect at the time of each action
• Full tool-call history, including parameters and outcomes
• Decision context or reasoning trace supporting the action, where available
• Model and plugin or component provenance for supply chain review

Containment and Remediation for Agent Incidents

Containment for AI agent incidents differs from traditional host or network isolation. Because agent actions execute through delegated credentials and tool integrations, containment more often means revoking or scoping down agent credentials and tool permissions rather than quarantining a device or network segment. A runtime policy enforcement point positioned between agent reasoning and tool execution allows a SOC to block a specific action, or a class of actions, without disabling the agent entirely, which matters when the same agent supports unrelated production workflows.

Escalation paths differ as well. Incidents involving excessive agency or tool misuse should route to whoever holds authority over agent permission configuration, which may not sit within a traditional SOC tier. Supply chain and plugin provenance issues, cataloged separately by OWASP and reflected in MITRE ATLAS techniques such as ML supply chain compromise, extend incident scope beyond runtime behavior into model and component sourcing, and remediation may require coordination with teams managing model and plugin procurement rather than SOC action alone.

Remediation should confirm that revoked or scoped permissions do not silently reset to a broader grant, and that any policy exception created to unblock a legitimate workflow is logged and time-bound. Runtime governance layers that support instant credential or permission scoping, without disrupting unrelated agent workflows, are what make this containment step executable rather than theoretical during a live incident.

Building and Maintaining the Runbook

• Start from an existing lifecycle: Extend NIST SP 800-61 Rev.3's four stages with AI-specific detection criteria rather than authoring a parallel process.
• Define detection criteria per incident type: Generic anomaly thresholds do not map cleanly across prompt injection, tool-call abuse, credential misuse, and unauthorized data access categories.
• Instrument logging before writing detection logic: Comprehensive logging of prompts, outputs, and access events, consistent with CISA and NSA guidance, is a prerequisite for any triage procedure the runbook describes.
• Assign escalation authority explicitly: Document who owns agent permission configuration decisions, not only who owns the incident ticket.
• Test containment actions for workflow impact: Confirm that credential revocation or permission scoping executes without disrupting unrelated agent workflows before relying on it during a live incident.