Decision Ledger Guide

AI Decision Ledger: What Organizations Should Record and Why

A Decision Ledger is the operational record of an AI-assisted decision: which models contributed, what a reviewer did, who authorized the result, and what was retained. It is not a substitute for policy — it is the evidence that policy was applied at the moment of action.

Quick Answer

Quick Answer

A Decision Ledger captures the accountable path of an AI-assisted decision: what was asked, which models answered, what a person changed, who authorized the result, and what was retained.

Application logs and observability remain useful. They usually cannot reconstruct why an organization acted. The ledger exists for that reconstruction.

Definitions

A Decision Ledger is not a log dump

If model identity, reviewer action, and authorization are missing, later reviewers can see that software ran without being able to reconstruct the decision. That is the failure a ledger is designed to prevent.

This educational page owns the “what to record and why” question. The Decision Ledger product page describes how SmartSolo presents that record in a governed workflow. They should stay distinct: one is the record design, the other is a product implementation.

What to record

Core Decision Ledger fields

  • Use case, workflow identifier, and timestamp
  • Model provider, identity, and version for each contributing output
  • Prompt or instruction version and relevant configuration
  • Inputs and retrieved or attached sources within privacy bounds
  • Model outputs shown to the reviewer
  • Reviewer identity or role, edits, rejections, and comments
  • Authorization, exception, override, or escalation decision
  • Retention, access, and export references for qualified review
Why it matters

Accountability attaches to the action, not the model

Models change. Prompts change. Providers change. The institutional fact that remains is whether a named person authorized a specific outcome under a defined process. That is why Decision Ledger records sit beside human-in-the-loop controls rather than replacing them.

Question after the factLogs often answerA Decision Ledger is meant to answer
Did a request occur?Yes, as an eventWhich workflow and use case it belonged to
Which model ran?Sometimes, if loggedProvider, identity, and version for each contributor
What did a person do?RarelyReview, edit, reject, escalate, or authorize
Why was an exception allowed?Usually notReason, reviewer, and timestamp
Exceptions

Overrides and escalations belong in the same record

If a reviewer rejects the recommendation and an authorizer still proceeds, both facts belong together. If the item was escalated, the ledger should show who received it and what they decided. Exception reasons that live only in conversation will not be available to the next auditor or the next incident reviewer.

Do not create a second unofficial ledger in email. Either the workflow can represent the exception, or the exception is not actually under control.

Retention

How long to keep a decision record

Retention should follow the organization’s existing records schedule for the type of decision, not a generic “keep everything forever.” Some fields are more sensitive than others. Access should be limited to roles that have a reason to reconstruct the decision.

If you cannot export a package for a qualified reviewer under access control, you do not yet have an operational ledger. You have logs that only the original operators can interpret.

Related records

Provenance is necessary, not sufficient

A Decision Ledger that omits model identity cannot explain the technical path. Provenance that omits authorization cannot explain the institutional path. Record both. See the AI model provenance guide for the model-side fields this ledger should incorporate.

Limits

Records support review; they do not certify compliance

Preserving a decision record does not by itself establish NIST, SOC 2, FedRAMP, or agency authorization. Those determinations depend on the actual system boundary, evidence, and the acquiring organization’s process. The ledger is operational evidence — not a certificate.

Where this becomes operational

Connect the record to execution

A ledger design that never appears in the runtime workflow will not be populated. Governed execution has to collect the fields at the moment of review. SmartSolo can retain model provenance and authorization history as part of governed multi-model execution; the field list on this page is what to demand of any such implementation.

FAQ

Frequently asked questions

What is an AI Decision Ledger?

It is the operational record of an AI-assisted decision: what was asked, which models answered, what a person changed, who authorized the result, and what was retained.

How is a Decision Ledger different from logs?

Logs capture events. A ledger is organized around an accountable decision path and is meant to be retrieved as a package, not grepped from mixed telemetry.

What must a Decision Ledger include?

For consequential actions, typically workflow identity, model provenance, outputs shown to the reviewer, reviewer actions, authorization or exception, timestamps, and retention references. Exact fields depend on policy.

Does keeping a ledger mean we are compliant?

No. A ledger is operational evidence. It does not by itself establish NIST, SOC 2, FedRAMP, or agency authorization.

Next step

Apply these ideas in an operational workflow

Educational resources explain governance concepts. SmartSolo helps teams operationalize review, authorization, and decision records.

See governed AI execution in a live workflow

Review how SmartSolo coordinates multiple AI models, routes human authorization, and preserves the decision record.