Guides
AI systems need more than logs. They need traceable, verifiable evidence that shows what happened, why it happened, and whether execution matched the evaluated decision.
Use this guide to connect decision traceability, audit readiness, and verifiable evidence before you move into the demo or API.
AI systems are making real decisions, but most systems still cannot prove what actually happened. When those actions affect records, workflows, approvals, or external systems, teams need an audit trail instead of a pile of disconnected events.
An AI decision audit trail should preserve inputs, outputs, decision context, and final action state across the lifecycle so reviewers can reconstruct the decision later without guesswork.
An AI decision audit trail is a chronological record of how a decision moved from proposal to outcome. It includes the request context, the output or recommendation, the logic applied, the resulting decision, and what happened at execution.
The goal is reconstruction: what was proposed, what conditions were active, what was allowed or denied, and what actually happened downstream.
Outputs show results. Logs show isolated events. An audit trail connects those events into one traceable narrative so investigators can follow how the decision evolved instead of reading fragments from several systems.
That difference matters in incident review, where scattered logs may show that APIs fired but still fail to prove whether execution matched the evaluated decision.
Decision traceability supports compliance, debugging, incident investigation, and operator accountability. Teams in regulated or high-consequence environments need evidence they can retrieve later and trust during review.
That evidence should explain what the system saw, what it decided, and whether the downstream execution path honored the decision.
Many systems log prompts, outputs, or service calls without linking them. They record activity but do not bind the decision to the action that followed, which leaves reviewers with disconnected artifacts and no reliable narrative.
Integrity is another common gap. Mutable or unsigned records may still exist, but they do not qualify as verifiable evidence.
A real audit trail needs an execution boundary, deterministic records, signed artifacts, and linkage between evaluation and execution. Those elements make the trail reviewable and independently verifiable.
PFC applies that pattern by evaluating actions before execution, producing signed artifacts, and preserving linked evidence across the decision path.
You can see a real example of this in the Creative Lineage demo, which shows how exploration, evaluation, execution, and verification connect through signed lineage rather than screenshots or hand-assembled logs.
For the runtime control path behind that evidence model, see how PFC governs decisions. For the implementation surfaces, see the API.
AI without auditability is hard to trust because no one can prove what actually happened. Auditability needs to exist where execution becomes real, not only in analytics systems that observe events after the fact.
The operational goal is simple: produce evidence that can be reconstructed, linked, and verified. That is what turns ordinary logs into an AI decision audit trail.