More Than Dashboards: AI Decisions Must Be Provable

Dark Reading

by James Urquhart

February 23, 2026

AI-Generated Deep Dive Summary

AI systems are increasingly being scrutinized for their ability to provide detailed, provable records of decisions in real-time, beyond what traditional dashboards can offer. As enterprises adopt AI in regulated and high-risk environments, stakeholders demand transparency into specific actions taken by these systems—what actually happened at the moment a decision was made, under what authorization it occurred, and its impact. Dashboards, while useful for monitoring trends and performance metrics over time, fall short when investigating specific incidents. When errors or compliance failures arise, summaries and averages are insufficient. Investigators need a factual record of events tied to a particular instance, which dashboards simply cannot provide. This gap highlights the growing need for AI systems to emit tamper-resistant, replayable records at decision points—a concept known as "proof-of-decision." Proof-of-decision involves capturing detailed inputs, authorization scope, executed actions, and contextual factors like data access and constraints. Unlike explainability tools or telemetry, which focus on broader patterns or reasoning behind decisions, proof-of-decision provides direct evidence of what happened in a specific case. This is critical for accountability in scenarios where AI influences consequential outcomes, such as fraud detection, healthcare decisions, or financial transactions. For

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Originally published on Dark Reading on 2/23/2026