AI Governance and Evidence Trails, Daniel Joye

What to log

Capture enough context to reconstruct what the system saw, produced, and did without duplicating sensitive records unnecessarily.

• Request ID, timestamp, user or service identity, business purpose, and affected workflow
• Model and prompt-template version, retrieval sources, tool calls, configuration, and policy version
• Input and output references or protected snapshots, with sensitive fields minimized or tokenized
• Evaluation scores, safety flags, confidence signals, latency, errors, and downstream action

How human review is recorded

Treat review as a signed decision record, not a checkbox added after the outcome.

• Reviewer identity, role, authority, time, and the evidence available at the moment of review
• Decision: approved, corrected, rejected, escalated, or deferred, with a required reason code
• Before-and-after values for an override, plus comments and any supporting attachment
• Escalation owner, service-level deadline, final disposition, and separation-of-duties checks

Retention and access

Apply the insurer's records schedule by use case and jurisdiction rather than choosing one arbitrary duration for every log.

• Map each evidence class to its business record, legal basis, retention period, and deletion trigger
• Use role-based access, encryption, tamper-evident storage, access logs, and periodic access review
• Preserve linked evidence under litigation or regulatory hold and suspend normal deletion where required
• Document disposal, exceptions, and proof of deletion; have legal, privacy, security, and records owners approve the schedule

How evidence reaches auditors

Produce a scoped package that connects policy to control, control to execution, and execution to a sample of decisions.

• System inventory, intended use, owners, risk classification, data flow, model card, and change history
• Control matrix linking obligations to policies, tests, approvals, monitoring, incidents, and remediation
• Representative decision traces with human reviews, overrides, timestamps, and artifact checksums
• Read-only exports, an evidence index, data dictionary, lineage notes, known limitations, and redactions

Starting point

Governance software: A governance platform such as Credo AI provides a shared inventory, policy and control mappings, assessments, evidence workflows, and reporting patterns.

Custom-built trail: A custom trail starts with the insurer's existing claims, underwriting, policy-administration, identity, and records systems.

Time and operating load

Governance software: Usually faster when the priority is establishing a consistent program across several AI use cases. Subscription, configuration, integration, and adoption work still remain.

Custom-built trail: Usually requires more initial engineering and permanent ownership, but can avoid operating a separate governance layer when evidence already lives in internal systems.

Fit and flexibility

Governance software: Best when standard workflows and mappings cover most needs and teams can adapt their process to a common governance model.

Custom-built trail: Best when approvals, data boundaries, retention rules, or legacy integrations are insurer-specific and materially shape the control design.

Audit presentation

Governance software: Provides consistent assessments and artifacts across a portfolio. The insurer must still validate that configurations and collected evidence satisfy its auditors and regulators.

Custom-built trail: Can produce evidence in the exact format auditors and internal assurance teams expect, but completeness and regulatory maintenance remain the insurer's responsibility.

Governance software is the better choice when

• The insurer needs a common AI inventory and control language across many teams or vendors
• Compliance needs maintained mappings to multiple frameworks and repeatable assessments
• A configurable product meets most workflows and the organization can fund integration and change management

A bespoke evidence trail is the better choice when

• The first need is one or a few high-value workflows tightly coupled to existing insurance systems
• Data residency, retention, access, or review rules require precise implementation inside the current environment
• The insurer has engineering ownership for testing, security, control updates, support, and auditor-facing exports

A hybrid is often practical when

• A governance platform manages inventory, policy mappings, attestations, and executive reporting
• Custom instrumentation captures decision-level evidence inside production workflows
• Stable identifiers and APIs connect each governance record to the underlying trace without copying unnecessary sensitive data

Self-reported

A result from Daniel's operating records or direct project experience that has no published third-party audit.

Published document

A claim supported by an official project document, recommendation letter, or attributable client statement.

Live product

A product or behavior that a visitor can inspect on its public website.

What should an insurer log when a human reviews an LLM output?

Record the request and model versions, relevant input and output references, automated checks, reviewer identity and authority, evidence viewed, decision, reason code, any before-and-after correction, escalation, final action, and timestamps. Minimize or tokenize sensitive data and preserve links to the governed source records.

How long should AI evidence trails be retained?

There is no universal retention period. Tie each artifact to the insurer's approved schedule for the underlying business record, applicable jurisdiction, complaint and litigation needs, and contractual obligations. Document deletion triggers and legal holds, and have legal, privacy, security, and records owners approve the schedule.

How should LLM evidence be presented during a regulatory audit?

Start with a system inventory and control matrix, then provide a read-only evidence index linking policies, tests, approvals, monitoring, incidents, changes, and representative decision traces. Include a data dictionary, lineage, integrity checks, redactions, and known limitations so an auditor can reproduce the chain without receiving unrelated sensitive data.

Should a mid-size insurer buy governance software or build a custom evidence trail?

Buy when broad inventory, framework mappings, and repeatable cross-team workflows are the main need. Build when a small number of high-value workflows require deep integration or insurer-specific controls. A hybrid—commercial governance at the program layer and custom decision-level instrumentation—is often the most practical fit.

How do I document human oversight of an LLM for regulatory audits?

Log each review event with the request, model and prompt versions, input and output references, automated checks, reviewer identity and authority, the evidence the reviewer viewed, the decision, the reason code, any before-and-after correction, escalation, final action, and timestamps. Keep reviews in a durable, access-controlled store, retain the artifacts on the approved schedule, and give auditors a read-only evidence index that links policies, tests, approvals, monitoring, and representative decision traces. The full control list, evidence-trail flow diagram, and retention guidance are on this page.