AI Audit Trail Requirements: What Regulators Expect

The alternative treats the audit trail as a property of the system architecture, not a layer added on top: every action an AI agent takes generates a structured record because the platform is designed to produce that record as part of normal operation, not because a logging pipeline was configured to capture it. WEM No-Code's Architecture page covers how that design principle works at the platform level.

The practical consequence: an architectural audit trail contains every decision by definition, because every decision runs through the same workflow engine that generates the record.

There's no uninstrumented code path and no decision that happened before logging was enabled, because the system can't operate without generating the record. It also captures what custom logging tends to miss: the human review (because review happens inside the governed workflow), the escalation chain (because escalation is a workflow step), and the model version in use at any given moment (because the workflow references the configured version).

Different regulations require very different retention windows, and averaging them together is its own compliance failure: a trail kept for six months when a regulation expects years isn't compliant, no matter how good the trail is.

High-risk AI (EU AI Act): the Act's own floor, set in Articles 19 and 26(6), is at least six months for the automatically generated logs Article 12 requires, for a period appropriate to the system's intended purpose. Six months is a floor, not a target, and where another law requires longer retention for the same record, that longer period governs. This is a much shorter number than the sector-specific figures below, and worth not conflating with them.

Financial services (EU): DORA doesn't set one fixed retention number in the regulation itself; national competent authorities and supervisory expectations drive it, and in practice organizations generally plan for several years of ICT risk documentation. MiFID II is more explicit: transaction and communications records tied to client orders must be kept for at least five years, extendable to seven at a regulator's request.

Financial services (US): the Bank Secrecy Act requires SAR and CTR records to be retained for five years from the date of filing. OCC/Federal Reserve model risk management guidance (SR 11-7) doesn't specify a minimum retention period explicitly; examination practice has generally treated several years as standard for model validation records, though SR 11-7 was reportedly succeeded by newer guidance (SR 26-2) in April 2026, worth confirming current status before relying on the older figure.

Healthcare (EU): GDPR doesn't specify a retention period; it requires data to be kept only as long as necessary for the specified purpose, which, for an AI system making healthcare decisions, needs to be defined explicitly in the data protection impact assessment.

Healthcare (US): HIPAA's federal floor is six years from creation or last effective date, whichever is later, but that rule applies specifically to HIPAA compliance documentation (policies, risk assessments, training records), not to medical records themselves. Actual clinical or decision records tied to patient care are usually governed by state medical-record retention law, which can run longer, five to twenty-plus years depending on the state. An AI decision record touching PHI may need to satisfy whichever of those is longer, not just the six-year federal floor.

Retention isn't just about duration. Records need to be retrievable, not archived in a format that takes real effort to access. A regulator asking for a specific decision record expects it within examination timelines, which in practice means days, not weeks.

Every workflow on WEM No-Code generates an audit trail automatically. The record isn't assembled by a separate logging pipeline; it's produced by the workflow engine itself as a structural output. Every function call, state transition, and response inside the workflow is logged by the platform, not by the model, which is what gives an examination request a single place to look rather than four systems to reconcile.

For organizations in financial services and healthcare deploying agentic AI across high-stakes workflows, that's the practical difference between a compliance posture and a compliance performance. An audit trail that supports a DORA examination or an EU AI Act conformity review isn't something built separately from AI operations; it's something configured once, at deployment, and produced on demand.

The specific report format an examiner wants, and whether the underlying retention window is long enough for the regulation that actually applies, are still determinations the deploying organization needs to make itself.

For the broader set of practices this fits into, see WEM No-Code's agentic AI governance best practices guide (draft slug, confirm on publish), and for how the retention and examination question plays out specifically under DORA and the EU AI Act, see WEM No-Code's DORA compliance guide and EU AI Act guide.

The common thread across every audit element and every retention rule here is reconstructability: a regulator examining a decision made months ago should be able to understand exactly what happened without relying on anyone's memory or a scramble across four systems.

That's a property of how a system is architected, not something assembled under pressure when an examination request lands.

To see what an examination-ready export actually looks like, book a demo and walk through a decision trail against your own workflow.