Topic starter
25/10/2026 11:09 am
TL;DR: AI agent auditability depends on connected execution records that preserve identity, policy, credential references, and data handling, according to KeyRunner. Investigations need evidence that follows the execution path, not just isolated API lines.
Editorial analysis by NHI Mgmt Group, based on content published by KeyRunner: “How Do You Audit API Calls Made by AI Agents?”.
Key questions
Q: Why are API gateway logs not enough for AI agent governance?
A: Gateway logs describe network activity, but agent governance needs to show which identity selected the tool, which policy was evaluated, whether approval was required, and how sensitive output was handled.
Q: How should teams implement AI agent governance without losing auditability?
A: Start with a centralized control plane that all agent-to-tool traffic must pass through.
Q: How should security teams test AI agents that can call tools and APIs?
A: They should test the agent's actual execution paths, not just its text responses.
Practitioner guidance
- Define a governed agent audit schema Record agent identity, initiating user or workload, session, tool, parameters, policy decision, approval state, credential reference, redaction, outcome, and timestamp in one execution record.
- Separate secret values from evidence Store credential identifiers and vault paths in logs, but keep secret values out of agent context and out of the audit trail.
- Link policy decisions to outcomes Correlate the allow or deny decision, approval reason, retries, timeout, rollback, and downstream API result so the security team can reconstruct the sequence later.
Bottom line: AI agent auditability depends on connected execution records that include identity, policy, credential references, and data handling.
Glossary terms: Connected Execution Record, Credential Reference, Policy Decision Evidence, Response Redaction Evidence
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:16 pm
Auditability for AI agents fails when teams assume API telemetry is sufficient evidence. Gateway logs can show that a request happened, but they do not capture the policy context, user intent, or redaction state needed to explain the execution. That creates an evidentiary gap, not just a visibility gap. Security teams should treat agent governance as an execution-record problem, not a log-retention problem.
A few things that frame the scale:
- 1 in 3 organisations encountered suspicious AI agent activity in 2025, and 99.4% experienced a SaaS or AI ecosystem incident.
A question worth separating out:
Q: How do organisations prove that an agent did not expose secrets?
A: They need records that show the credential was referenced by identifier only, that redaction rules were applied before output, and that the secret value never entered the agent context or the audit store. Proof comes from the governed execution path, not from the absence of an obvious leak in a single log line.
👉 Read our full editorial: Auditing AI agent API calls needs connected execution records
This post was modified 7 days ago by NHI Mgmt Group