Every organization deploying AI claims to be accountable, but none can produce a portable, verifiable artifact proving a specific decision was made through a documented process. Summit's Decision Receipt is the first system that closes this gap.
By the end of this episode, you will understand why audit logs, explainability tools, and governance frameworks all fail to deliver real AI accountability — and what a working alternative looks like with 3,222 signed receipts in production.
| Claim | Risk | Status | Evidence |
|---|---|---|---|
| No deployed AI product treats decision accountability as an infrastructure layer with a portable, verifiable output artifact. | high | approved | 1 |
| Five categories of adjacent tools — audit loggers, explainability, observability, governance platforms, and analysis systems — each address a piece of accountability but none produce a portable proof of how a specific decision was made. | medium | approved | 2 |
| The NIST AI RMF specifies what organizations must do for AI accountability across Govern, Map, Measure, and Manage functions, but provides no reference implementation for producing verifiable artifacts. | low | approved | 1 |
| Summit's Decision Receipt architecture produces a portable, cryptographically signed, independently replayable proof that a specific AI-assisted decision was reached through a documented process. | medium | approved | 2 |
| As of June 3, 2026, the Decision Receipt system has generated 3,222 signed receipts in production with a 99% acceptance rate across 9 agents. | low | approved | 1 |
The NIST AI Risk Management Framework tells organizations they need 'documented policies and procedures' for AI decisions. So here's my question: can any deployed AI system actually produce the artifact this framework demands? Not an explanation generated after the fact. Not a log entry. A portable, verifiable proof. Anyone?
Let's walk the landscape. Category one: audit loggers. Splunk, Elastic, Datadog. They record THAT a decision occurred. They do not record HOW. They cannot replay. They cannot produce independent verification. Category two: explainability tools. LIME, SHAP, even Anthropic's Constitutional AI logging. They generate a new explanation of model behavior — a new inference, not a replay of the original. Each explanation is itself an unverifiable claim.
Category three: LLM observability. LangSmith, Arize, Weights and Biases. They track prompts, tokens, latency, cost. They do not capture competing hypotheses or policy enforcement. No signed receipt. Category four: AI governance platforms. IBM OpenPages, OneTrust. They document risk frameworks. They do not produce decision-time artifacts. Paper compliance, not operational verification. Category five: analysis platforms. Palantir Foundry, Recorded Future. They are the system being analyzed, not the accountability layer.
Now let's look at what NIST actually requires. Function one: Govern. Requires documented policies and procedures. But there's no mechanism to prove policies were enforced at decision time. Function two: Map. Requires understanding of AI system context. But no mechanism to capture the context that existed when a specific decision was made.
Function three: Measure. Requires metrics and methods. But no mechanism to replay a decision against its original evidence to verify the measurement. Function four: Manage. Requires continuous monitoring. But no portable artifact an external auditor can independently verify. Four functions. Four gaps. That's the accountability gap this episode is named for.
So what does a working solution look like? A Decision Receipt is a portable, cryptographically signed artifact that captures what was decided, what evidence existed at decision time, what policy was enforced, and what alternatives were considered — all sealed into a bundle that can be independently replayed and verified.
It fills all four NIST gaps. Govern: deny-by-default policy enforcement with a signed receipt proving enforcement occurred. Map: evidence bundle captures full decision-time context. Measure: deterministic replay from sealed evidence. Manage: portable Ed25519-signed artifact for independent verification. Not a new explanation. Not a log entry. A proof.
And this is not a whitepaper. As of June 3, 2026, the system has generated 3,222 signed receipts in production. 3,178 accepted. 43 blocked. 1 escalated. 99% acceptance rate across 9 agents operating on a live codebase. Every receipt is queryable through a public verification endpoint at decrec.summitcognitive.ai.
The accountability gap has a name now. And it has a shape: four NIST functions, five categories of tools that miss the point, zero deployed products that fill the gap — until now. Check the source appendix for the full gap analysis, then visit decrec.summitcognitive.ai to inspect a real signed Decision Receipt. This has been Warrant, Season One, Episode One.