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To audit an AI agent, record not only what it did but also which agent and human authorized the activity, what authority and policy applied, which inputs informed the action, and what tools or resources it used. To investigate an incident, preserve those records alongside identity, application, and infrastructure logs, then correlate them into a timeline. No universal, finalized audit schema for AI agents is established; use existing audit-record practices as a baseline and add agent-specific context that fits your deployment.
Why agent activity needs more than ordinary action logs
An AI agent combines model output with software capabilities and may take actions that affect real systems. Its security risks therefore include familiar software vulnerabilities as well as AI-specific risks such as adversarial inputs, insecure models or data, and behavior that is misaligned with its specification. NIST’s January 2026 request for information on securing AI agent systems discusses these risks and the importance of constraining and monitoring access.
A record saying that an agent called a tool can establish that the call occurred. By itself, it may not establish who granted the authority, which instructions or retrieved content influenced the call, or why the model selected it. Those distinctions matter when investigating unauthorized access, unexpected delegation, policy bypass, harmful actions, or suspected prompt injection.
What should an AI agent audit record contain?
Start with the baseline audit information in NIST SP 800-171 Rev. 3: event type, time, place, source, outcome, and associated identities or entities. NIST’s discussion also identifies useful record details such as timestamps, source and destination addresses, process identifiers, event descriptions, file names, and invoked access or flow rules. Extend that baseline to capture the context needed to understand an agent workflow.
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| Record area | Information to capture | Why it matters |
|---|---|---|
| Event and outcome | Event type, timestamp, location or system, source, target, result, and any error or denial. | Establishes what happened and where. |
| Agent and human identity | Agent identity and version; human who initiated or approved the run; identity or credential used for each call. | Distinguishes the software actor from the person or service that authorized its activity. |
| Authority and policy | Delegation or authority chain, applicable policy, and relevant approval or authorization decision. | Shows what the agent was permitted to do and under whose authority. |
| Model and provenance | Model identity and version, provenance where available, and relevant prompt or instruction provenance. | Provides context for the system and instructions involved in the run. |
| Inputs and evidence | Untrusted inputs or retrieved content relevant to the action; supporting evidence or citations for material factual outputs or decisions, where applicable. | Helps investigators assess what information may have influenced an action or claim. |
| Tools and resources | Tool or resource invoked, action category or arguments, target, timestamp, result, and links to the parent run, child agent, tool call, and resulting system-side event. | Connects the agent’s activity to changes in the systems it can access. |
This is a practical design synthesis, not a list of fields mandated together by one standard. NIST’s experimental work on evaluation probes explores machine-readable trails linking agent outputs or decisions to trusted evidence, with checks for faithfulness, completeness, and sufficiency. NIST’s 2026 concept work on software-agent identity and authority raises identification, authorization, auditing, and non-repudiation questions; comments on that concept paper advocate richer context such as tool use, delegation, provenance, and tamper-evident records. These concepts, experiments, and stakeholder comments are not a finalized universal control set.
How to prepare logging before an incident
Map the agent and its authority
- Inventory each agent, its deployment environment, connected tools and data, human owner or authorizer, identity and credential model, and applicable authorization policy.
- Record whether the agent is enterprise-managed, locally deployed, or externally owned. The degree of control an organization has over identity, configuration, and records can differ across these arrangements.
- Identify which system can answer each investigation question: the agent runtime, model or provider, identity provider, tool or API, application, network, cloud control plane, or endpoint.
Plan collection, retention, and access
NIST describes log management as the process of generating, transmitting, storing, accessing, and disposing of log data. Plan each part before deployment: decide which events are generated, how they reach a collector, where they are stored, who can access them, and how long they are retained under applicable policy and investigation needs. NIST SP 800-92 Rev. 1 is a draft planning guide, not a finalized standard.
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Set retention based on policy and the time likely to be needed to detect and investigate incidents. Protect records with appropriate access controls. Agent traces may contain prompts, sensitive inputs, identities, and security-relevant context, so collect only the detail needed for audit and investigation.
Make logging failures visible
Define an alert and response for missing or interrupted records. NIST SP 800-171 Rev. 3 calls for alerting assigned personnel within an organization-defined time when logging fails and selecting additional actions suited to the failure. Storage exhaustion, capture errors, or collector interruption can leave gaps; treat them as operational events to investigate rather than assuming the audit trail is intact.
How to investigate suspicious or harmful agent activity
- Preserve relevant evidence. Collect agent traces, identity and authentication events, tool and API calls, application and infrastructure records, and the relevant model and configuration versions. Preserve records in a way that retains their context and relationships.
- Build a common timeline. Normalize timestamps as your systems allow, note uncertainty, and correlate records across repositories. NIST SP 800-171 Rev. 3 describes reviewing audit records for unusual activity and correlating records from multiple repositories to reconstruct event sequences.
- Trace the authority and workflow. Determine who or what initiated the run, which identity and authority were used, what policy and approvals applied, what instructions and external content were relevant, which tools and targets were involved, and what results followed.
- Check the affected systems. Compare tool-call records with application, cloud, network, and endpoint events to identify subsequent system changes and establish whether a recorded action took effect.
- Test investigation hypotheses. Look for unauthorized access, unusual privilege use, unexpected delegation, policy bypass, adversarial content such as prompt injection, or harmful behavior without an external attacker. These are avenues to examine, not proof of cause.
- Separate evidence from inference. State what records directly show and what remains an interpretation. A tool-call record proves the call was logged; it does not necessarily explain the model’s selection of that call. NIST’s probe work explores connecting outputs to evidence, while public comments on the identity concept paper note that conventional action logs can omit intent and authority context.
- Document gaps. Record missing sources, logging failures, uncertain timestamps, and retention limits. Qualify conclusions where those gaps prevent a complete reconstruction.
How to evaluate whether an audit setup is adequate
Assess coverage against the questions responders would need to answer, rather than treating the existence of a log repository as proof of auditability. Check whether the setup can capture and correlate agent-runtime, identity, tool, application, and infrastructure events; show delegated authority; preserve relevant inputs with privacy controls; restrict access; retain records appropriately; protect record integrity; alert on logging failures; and support analyst review and timeline building.
Deployed-AI monitoring is broader than logging. NIST’s March 2026 discussion of monitoring deployed AI systems identifies six monitoring categories and describes fragmented logging as a barrier. Logging supports monitoring and investigation, but it does not by itself answer every operational monitoring question. The materials do not establish a universal monitoring cadence or a settled relationship between every monitoring activity and audit records.
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