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.
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.
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
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 fact | Logs often answer | A Decision Ledger is meant to answer |
|---|---|---|
| Did a request occur? | Yes, as an event | Which workflow and use case it belonged to |
| Which model ran? | Sometimes, if logged | Provider, identity, and version for each contributor |
| What did a person do? | Rarely | Review, edit, reject, escalate, or authorize |
| Why was an exception allowed? | Usually not | Reason, reviewer, and timestamp |
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.
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. Multi-model paths should also retain how work was routed — see routing, selection, and governance. For why durable records matter in high-accountability settings, see Audit That Doesn't Blink. Research completeness framing is in the Decision Traceability benchmark design. 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.
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.
References
Authoritative sources cited for nearby factual claims. Links open official publisher pages.
- NIST — AI Risk Management Framework (2023)
- NIST — Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 (2023)
- NIST — SP 800-92, Guide to Computer Security Log Management (2006)
- OMB — Memorandum M-25-21, Accelerating Federal Use of AI through Innovation, Governance, and Public Trust (2025)
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.