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01/07/2026 1:58 am
TL;DR: AI audit readiness means having inventory, assessments, lineage, audit trails, policy evidence and decommissioning records in place before an external review begins, according to Collibra. The central issue is not whether AI exists, but whether teams can prove models and agents were governed continuously instead of reconstructed under deadline.
NHIMG editorial — based on content published by Collibra: AI audit readiness, a checklist for models, agents, and your first AI audit
Questions worth separating out
Q: How should security teams prepare AI systems for the first audit?
A: Start with a current inventory of every model and agent, then attach ownership, risk classification, assessment status, runtime logs, and retirement evidence to each record.
Q: What breaks when AI governance evidence is scattered across teams?
A: Audit readiness breaks because no single team can reconstruct the full control story on demand.
Q: How do you know if AI audit readiness is actually working?
A: A simple test is whether you can pick one model or agent at random and produce its inventory, assessment, lineage, policy evidence, and retirement records in minutes.
Practitioner guidance
- Build a single AI inventory of record Record every model and agent with a named owner, approved purpose, and current risk classification in one system that auditors can query directly.
- Capture action-level logs for agents Store every agent action, decision trace, and human override so reviewers can see what the agent did, why it did it, and who intervened.
- Attach evidence to lifecycle stages Link assessment, lineage, policy enforcement, and retirement records to the same lifecycle state change so proof is created when the AI system changes status.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- The complete ten-item readiness checklist with the exact evidence expected for each AI system.
- The agent-specific record types, including action logs, decision traces, scope records, and intervention logs.
- How the AI Command Center model maps evidence capture into day-to-day operating workflows.
- The article's own framing of what counts as being ready before an internal, regulatory, or customer audit.
👉 Read Collibra's checklist for AI audit readiness and agent evidence →
AI audit readiness for agents: what evidence do teams need now?
Explore further
01/07/2026 2:16 am
Audit readiness is really identity governance for AI evidence. The article is not about a document checklist in isolation; it is about whether the organisation can prove who or what was allowed to act, under what approval, and with what traceability. That is the same governance problem IAM and IGA teams have always owned, but now extended to models and agents that generate their own operational footprint. Practitioners should treat audit readiness as a control-evidence discipline, not a compliance scramble.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant behaviour gap in day-to-day control execution.
A question worth separating out:
Q: Who owns AI audit evidence when models and agents cross team boundaries?
A: Ownership should sit with the programme that governs the AI system end to end, not with whichever team generated one of the records. If control evidence is fragmented, accountability becomes ambiguous and the audit trail becomes harder to defend than the system itself.
👉 Read our full editorial: AI audit readiness exposes the evidence gap in agent governance
