Should we store the prompts themselves?

Generally no. Prompt storage creates a durable record of employees' work and of whatever sensitive content was in it, and it rarely changes the answer to a governance question. Where a specific regulatory obligation requires content retention, it should be a deliberate, separately-scoped decision rather than the default.

How long should AI audit records be kept?

It depends on the obligation, but a common split is a longer retention for aggregate and metadata-level records and a short one for anything nearer to content. The important part is that retention is chosen and documented rather than defaulting to whatever the storage tier does.

Is a SIEM the right home for this?

Often, for correlation with the rest of the estate. Two things usually need to be preserved when forwarding: the session linkage between related actions, and the distinction between an attempt and an outcome. Flattening either makes the events much less useful on arrival.

What makes an AI audit trail different from an ordinary application log?

The decision. An application log records what the system did; a governance trail additionally records what was attempted, what policy said about it, and why. Without the decision layer it documents behaviour rather than evidencing control.

Operations

What Belongs in an AI Audit Trail

Oconee Runtime TeamPublished September 6, 20264 min read

Short answer

What belongs in an AI audit trail, and what should be left out?

An AI audit trail records each governed action: who or what attempted it, on which surface and tool, what was detected, which policy rule applied, what the system did, and when. It should carry classifications and identifiers rather than the sensitive content that triggered it, because a log holding the credentials it detected is a larger liability than the events it documents. The test of a good trail is whether an incident can be reconstructed from it months later without any other source.

Definition

AI audit trail: An AI audit trail is a durable, time-ordered record of AI-related actions and the policy decisions applied to them, sufficient to reconstruct what was attempted and what the organization's controls did about it.

Diagram

What one audit record has to carry

Ordered by how often each layer turns out to be the missing one during an actual investigation.

The action
What was attempted — a submission, a command, a file write, a push — and against what target.
The context
Surface, tool, user, repository or workspace, permission mode, and the session it belongs to.
The decision
Which rule matched, what severity was assigned, and whether the action was allowed, warned on or blocked.
The classification
What category of sensitive data was involved — never the value that matched.

The questions the trail has to answer

Design the record backwards from the questions it will be asked. In practice there are four, and they come from different people at different times.

Incident response: what did this user, agent or session do, in order, around this time?
Assurance: are our AI policies actually being applied, and how often does each fire?
External review: can we show a customer or an auditor what controls exist and that they ran?
Tuning: which rules produce noise, and which fire on things nobody cares about?

Sessions and correlation are what make a log investigable

A flat stream of independent events answers 'did this happen' and not 'what happened'. An investigation almost always needs the sequence: the agent read this, then proposed that, then was blocked, then tried something adjacent.

That requires a session identity linking related actions, and a correlation identity linking an action to the decision made about it. Without them, reconstruction becomes timestamp archaeology across several systems, which is slow when it works and unreliable when it does not.

Record blocked attempts as first-class events

It is tempting to log only what happened and treat prevented actions as non-events. That discards the most useful data in the system.

A blocked attempt is evidence the control worked, and it is the only signal that shows what people and agents are trying to do. A rising rate of blocked credential submissions in one team is a workflow problem worth fixing upstream. Nothing else in the estate would have surfaced it.

Retention, access and the liability of the log itself

Two constraints pull against each other. Investigations and audits want long retention; privacy obligations and breach exposure want short. Splitting by sensitivity resolves most of the tension: keep the aggregate and the metadata for a long period, keep anything closer to content for a short one.

The log is also a system that needs governing. Who can read it, whether the detail that identifies individuals is separately gated, and whether reads are themselves recorded, are all questions with real answers — and an audit trail nobody may read is not evidence, while one everybody may read is surveillance.

What Oconee Runtime records

Events carry the surface and tool, the detection category and severity, the policy decision, and repository and session context where relevant. Session and correlation identifiers link related activity so an investigation follows a sequence rather than a timestamp.

The dashboard exposes this as an activity feed, policy violations with risk severity, session timelines and enforcement logs. Detections are stored as classifications and metadata rather than as the matched content, for the reason set out above.

Examples

A customer security review asks whether AI tools were used on their data and what controls applied.
Answerable from tool usage, detection categories and policy decisions over the period, without producing anyone's prompts.
A secret is found in a public repository and the question is whether an AI tool was involved.
The trail shows whether a credential of that class was detected, in which tool, and whether it was blocked or allowed through. A trail without blocked attempts answers only half of it.
An agent session produced an unexpected change and nobody can reconstruct what led to it.
The session identifier links the sequence of proposed actions and decisions, which is the difference between an explanation and a guess.

Frequently asked questions

Should we store the prompts themselves?: Generally no. Prompt storage creates a durable record of employees' work and of whatever sensitive content was in it, and it rarely changes the answer to a governance question. Where a specific regulatory obligation requires content retention, it should be a deliberate, separately-scoped decision rather than the default.
How long should AI audit records be kept?: It depends on the obligation, but a common split is a longer retention for aggregate and metadata-level records and a short one for anything nearer to content. The important part is that retention is chosen and documented rather than defaulting to whatever the storage tier does.
Is a SIEM the right home for this?: Often, for correlation with the rest of the estate. Two things usually need to be preserved when forwarding: the session linkage between related actions, and the distinction between an attempt and an outcome. Flattening either makes the events much less useful on arrival.
What makes an AI audit trail different from an ordinary application log?: The decision. An application log records what the system did; a governance trail additionally records what was attempted, what policy said about it, and why. Without the decision layer it documents behaviour rather than evidencing control.

Sources

Audit evidence
AI governance
Compliance
Policy enforcement