Episode 3: AI Audit Trails

An AI audit trail is a complete, chronological record of what an AI system did and why — the data it used, the decision it produced, the action it took, and the policies and people involved. It's the evidence layer of AI governance: the thing that lets you reconstruct any decision after the fact and prove a system behaved within the rules. A regulator asks how your AI reached a decision. You have no owner, no record, and no answer. AI is now making and acting on decisions at a scale no human reviews in real time, and when something goes wrong — a bad output, a leaked record, a wrong decision — the only honest answer you have is your audit trail. Traditional logging captures raw events, but logs scattered across systems with no policy context and no link to the model that produced them aren't evidence. For autonomous agents, the problem runs deeper: you need to capture not just what the agent decided, but every tool it called, every system it touched, and whether any of it was actually permitted. In this video, Michael Rahm, Director of Product Marketing at Collibra, walks through the four questions every audit trail needs to answer: 1️⃣ What data went in 2️⃣ What came out 3️⃣ What the system did with it 4️⃣ Whether it was allowed For models, that's inputs, outputs and versions. For agents, it expands to every tool called, every system touched, the decision trace behind each step, and the runtime policy checks that did — or didn't — fire. The good news: a Command Center, like Collibra's AI Command Center, captures all of this automatically. By registering every model and agent at deploy time and enforcing policy as code, the audit trail becomes a byproduct of running your AI — not a separate project your team has to maintain by hand. The question isn't whether your AI will make a decision someone questions. It's whether you'll be able to explain it — or just apologize for it. 📖 Get the full breakdown, including a complete logging checklist for agents, why audit trails matter for EU AI Act and NIST AI RMF compliance, and how audit trails differ from logging and lineage: https://www.collibra.com/blog/ai-audit-trails-what-to-log-for-models-and-agents-and-how-a-command-center-captures-it #AIAuditTrail #AIGovernance #AIObservability #Collibra #AICompliance #ResponsibleAI #AICommandCenter #EUAIAct