Investigate an AI agent incident by defining what you need to establish, preserving relevant records, and correlating the agent’s actions with identity, authorization, model, data, and downstream system logs. The agent’s final answer is only one artifact: it does not, by itself, prove which internal steps occurred.
1. Set the scope before collecting evidence
Record when the incident was detected, the suspected activity window, affected users or tenants, the agent deployment and version, the business function involved, and the actions or data that may be at risk. Identify the questions the investigation must answer, such as whether information was exposed, a tool performed an unauthorized action, or a safeguard failed.
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Preserve records before routine expiry or system changes. If containment is necessary, record what changed and when; avoid altering source evidence during collection. NIST’s December 2025 initial preliminary draft, IR 8596, frames incident analysis around establishing what happened and its root cause. It is a draft, not a final standard.
2. Turn each question into an evidence request
For every investigative question, identify the likely source, its owner, the query or extraction method, the time range to search, and the retention limit. AWS-authored incident-response preparation material hosted by NIST recommends mapping business functions to investigation questions, log sources, and queries. Prioritize gaps by the business impact of not being able to answer.
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| Question | Records to seek | What they can establish |
|---|---|---|
| Who initiated the activity, and under what authority? | Identity-provider events, agent and session identity, delegated authority, policy decisions, approvals, and denials | Which principal and agent were active, what permissions applied, and whether a decision or approval was recorded |
| What did the agent attempt and what happened? | Agent runtime events, tool gateway records, tool names, target resources, arguments or safe summaries, and execution outcomes | Which actions were attempted, their targets, and whether they succeeded, failed, or were blocked |
| What information or model context was involved? | Model gateway and inference records, model version, retrieval or search records, document identifiers, and relevant data or index versions | Which model and data context were recorded, and which retrieved items were associated with the request |
| What was affected downstream? | Application and data-store records, security monitoring, and relevant recipient or downstream-system records | Whether data or state changed, who or what received an output, and whether later controls blocked or amplified an action |
The table is a starting map, not a claim that every deployment records all these events. The available sources and retention vary by system.
3. Build a correlated timeline of the whole workflow
Collect records from the agent runtime, identity provider, policy or authorization service, tool gateway, application and data stores, retrieval layer, model gateway, and security monitoring system as applicable. Search across the full suspected window, allowing for differences in timestamp formats, time zones, and delays between systems.
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Use session, request, trace, or event identifiers to connect records. OWASP agent guidance recommends correlation IDs, retrieved-document identifiers, authorization decisions, model versions, and tool-invocation outcomes. Preserve the agent identity and action, target resource, authorization result, relevant tool arguments or a safe summary, execution outcome, model version, and retrieved-document identifiers when available.
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4. Preserve evidence, provenance, and uncertainty
For each collected item, record its source system, collection time, collection method, relevant timestamp and time-zone context, custodian or system, and any transformation applied. Preserve originals and protect integrity so another reviewer can follow how the evidence was handled and how conclusions were reached.
Depending on the incident and what exists, AI-relevant evidence may include model logs, inference records, input and output records, provenance data, decision chains, dataset versions, and model or configuration metadata. NIST IR 8596 identifies these as relevant incident data and calls for preserving integrity and provenance.
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If a trace was not collected or has expired, state that limitation rather than filling in a plausible sequence. AWS-authored preparation material hosted by NIST emphasizes that evidence not collected before an incident may not be available afterward. An absent record does not establish that an action did or did not happen.
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5. Handle prompts and other sensitive content carefully
Prompts, retrieved passages, model inputs and outputs, and tool arguments can contain personal data, credentials, or other secrets. OWASP guidance advises against logging raw content by default. When content is necessary to answer an incident question, preserve only the relevant material in a restricted evidence store, redact where possible, limit access, and apply retention limits. Keep useful identifiers and metadata even when raw content must be excluded.
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6. Test causes and estimate impact from corroborated records
Evaluate competing explanations against independent evidence. Examine whether the agent used authorized tool paths, whether user or delegated authority was valid, whether retrieved content or memory may have influenced the event, whether a model, index, dataset, or configuration changed, and whether downstream controls blocked or amplified the action.
Estimate affected data, users, resources, duration, and availability from records that support those estimates. Separate confirmed events from probable explanations, hypotheses, and unresolved questions. NIST IR 8596 calls for establishing what took place, analyzing root cause, and estimating and validating incident magnitude; the strength of any conclusion depends on the evidence available.
7. Report findings and close telemetry gaps
Present a reproducible timeline with references to the evidence behind each conclusion. Distinguish confirmed facts, probable explanations, unknowns, and telemetry gaps; record investigative actions and the reasoning used to assess cause and impact. Update the log-source map and retention plan, prioritizing gaps according to business impact, then rehearse the revised process. NIST-hosted preparation material recommends validating that retention spans the detection period for business-critical systems.
OWASP agent guidance also treats audit trails as a security control: logging should capture decisions and actions, and systems should fail closed if audit logging fails. For high-impact or irreversible actions, require explicit approval and retain the approval record so the action and its authorization can be examined together.
How to assess whether an audit trail is useful
When reviewing an existing logging approach, assess whether it covers agent, identity, policy, tool, model, and data events; correlates records across systems; protects integrity and provenance; minimizes sensitive content and restricts access; retains records for the likely detection window; and lets investigators reproduce queries and conclusions. 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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