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To investigate an AI agent incident, preserve the relevant records, then reconstruct the full chain from the initiating user or service identity through the agent’s decisions, permissions, tool calls, and downstream effects. Correlate records across systems by time and shared identifiers; verify each step against independent evidence. A model’s final answer alone does not establish what actions occurred.
1. Define the incident and protect the evidence
Start by recording what is known, what is suspected, and what must be determined. Set a working time window broad enough to cover the suspected activity, not just the alert. Note the affected business function, users or tenants, agent deployment and version, and any data or actions at risk.
- Record the detection time, suspected start and end of activity, and the time zone used by each source.
- Identify the agent runtime, model gateway, identity and authorization services, tools, data stores, retrieval systems, and downstream applications that may be in scope.
- Preserve relevant records before routine expiry or system changes. Record any containment action and when it occurred; avoid altering source evidence during collection where possible.
- Assign an incident identifier and keep a record of who collected each item, from which system, by what method, and when.
NIST IR 8596’s initial preliminary draft, dated December 2025, frames AI incident analysis around establishing what happened and its root cause. It is draft guidance, not a final standard.
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Write questions that can be answered or explicitly marked unresolved. For each one, identify the likely source, its owner, the query or extraction method, the time range, and the applicable retention limit. AWS-authored incident-response preparation material hosted by NIST recommends mapping a business function to investigation questions, log sources, and queries.
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| Investigation question | Records to seek |
|---|---|
| Who initiated the activity, and under whose authority? | Identity-provider events, user or service principal, agent identity, session context, delegated authority, and authorization decisions. |
| What did the agent attempt, and what actually ran? | Runtime events, tool-gateway records, tool names, target resources, arguments or safe summaries, execution results, and denied actions. |
| What information or configuration could have shaped the activity? | Model and configuration versions, retrieved-document identifiers, index or dataset versions, relevant memory or data state, and access outcomes. |
| What changed or was exposed downstream? | Application and data-store audit records, recipient or access records, transaction history, and relevant security-monitoring events. |
| Was a safeguard or human approval involved? | Policy decisions, approval records, control outcomes, alerts, and evidence that a safeguard blocked, allowed, or changed the action. |
Do not assume a single logging system contains the whole answer. Map the agent’s system boundaries first, then request records from each component that could establish a link in the chain.
3. Reconstruct the sequence across systems
Build a timeline from the runtime and identity records through authorization, model and retrieval activity, tool execution, and downstream application changes. Use session, request, trace, or event identifiers to connect records. Where identifiers are missing, use timestamps and other context cautiously, and label the linkage as uncertain rather than presenting it as a confirmed match.
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For each event, capture the timestamp and time zone, source system, principal and agent identity, action, target resource, authorization result, tool outcome, and relevant model or data version. Include retrieved-document IDs where available. OWASP’s agent guidance recommends correlation IDs, authorization decisions, model versions, retrieved-document IDs, and tool invocation outcomes as useful audit-trail details.
Treat the model’s response as one artifact in the timeline, not a complete record of internal steps. Compare it with runtime, tool, and application records. NIST evaluation-probe work illustrates a stronger form of traceability: structured audit trails can connect agent decisions to supporting document evidence.
4. Preserve provenance and note what is missing
Keep original records and preserve their context: source system, collection time and method, timestamp and time-zone details, and any transformations made for analysis. Maintain a record of investigative actions so another reviewer can understand how evidence was obtained and how conclusions were reached. For AI-specific analysis, relevant material may include inference records, input and output records, decision chains, provenance data, dataset versions, and model or configuration metadata when available and pertinent.
If a needed trace was not collected or has expired, state that plainly. Missing telemetry can prevent confirmation of a particular action or decision; it does not justify filling in the sequence from the agent’s final answer or from assumptions. The NIST-hosted AWS preparation presentation emphasizes identifying evidence sources and retention before an incident because records that were not collected may not be recoverable afterward.
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5. Minimize exposure of sensitive content
Prompts, retrieved passages, model inputs and outputs, and tool arguments may contain credentials, personal information, or confidential business data. OWASP advises against logging this raw content by default. For an investigation, collect only the content needed to answer a specific question.
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- Prefer identifiers, access outcomes, timestamps, and redacted summaries when they are sufficient.
- When exact content is necessary, place it in a restricted evidence store, limit access to investigators with a need to know, and apply an appropriate retention limit.
- Keep useful metadata and identifiers even when sensitive content must be excluded from ordinary logs.
6. Test causes, scope, and impact
Evaluate competing explanations against independent records. Determine whether the activity followed an authorized tool path, whether the user or delegated authority was valid, whether retrieved content or memory could have influenced the event, and whether a model, index, dataset, or configuration changed. Check whether downstream controls blocked, limited, or amplified the action.
Estimate affected data, users, resources, action duration, and availability from corroborated evidence. Separate confirmed facts from probable explanations and unresolved questions; state where missing or conflicting records limit the estimate. NIST IR 8596’s preliminary draft calls for analyzing what took place, identifying root cause, and estimating and validating incident magnitude.
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Present a time-ordered account with evidence references so another reviewer can reproduce the reasoning. Distinguish confirmed events, probable explanations, unresolved questions, and telemetry gaps. Record the impact estimate and the basis for it without treating an unverified sequence as fact.
After the investigation, update the source map and retention plan according to business impact, then test whether records remain available for the detection window. OWASP recommends clear audit trails of agent decisions and actions and advises failing closed when audit logging fails. For high-impact or irreversible actions, require explicit approval and preserve the approval record. These controls make later reconstruction more reliable; they do not replace incident-specific evidence.
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- Incident identifier, affected business function, detection time, and investigation window.
- User or service principal, agent and session identities, and delegated authority.
- Correlation, request, trace, or event IDs, with timestamp and time-zone context.
- Authorization and policy decisions, approvals, and denied actions.
- Tool names, target resources, necessary redacted arguments or safe summaries, and execution results.
- Model and relevant data or index versions, retrieved-document identifiers, and access outcomes.
- Relevant prompts, outputs, or content only when necessary, with restricted access and redaction where possible.
- Source provenance, collection method, integrity protections, and retention limits.
- Timeline, investigative actions, findings, impact estimate, root-cause reasoning, and unresolved gaps.
How to assess whether an audit trail is investigation-ready
When evaluating a logging design or process, check whether it captures agent, identity, policy, tool, model, and data events; correlates records across systems; protects integrity and provenance; limits sensitive-content exposure; retains records long enough for the actual detection window; and lets investigators reproduce queries and conclusions. A trail that records only the agent’s text response may be insufficient to establish which tools ran, what they changed, or what evidence informed a decision.
NIST’s AI RMF Playbook supports auditability, traceability, and documented security testing. NIST’s work on agent identity, interoperability, and security evaluation is ongoing, so related initiative outputs may evolve.
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