AI-Assisted Decisions Require Proof, Not Trust

Artificial intelligence (AI) is increasingly making risk and compliance decisions for organizations, from vendor scores to compliance flags. Yet most legal and compliance leaders cannot explain how those decisions were reached. The accountability gap between trust and proof in AI-assisted decisions is emerging as a test of AI governance, writes Mitratech.

A Grant Thornton survey of 950 senior leaders found 78% lacked confidence in passing an independent AI governance audit within 90 days. Even as more than half of firms plan to expand AI use in risk management over the next two years, nearly half cite a lack of trust in AI as their main barrier. Just one in five organizations reports having a mature governance model for autonomous agents.

Third-party risk is often where the accountability gap surfaces first in AI-assisted decisions. A vendor can pass every check, receive a low-risk AI-generated score, and still later become the source of a breach, leaving teams unable to show which model version ran the assessment or who reviewed it. 

Testing whether an AI-assisted decision is truly governed comes down to three questions: whether the result can be reproduced, whether every step leading to it can be traced, and whether a human’s review is documented rather than merely policy. 

When evaluating AI, watch for vendors who refuse to produce decision logs, contract language that shifts liability for autonomous agents onto you, and models that revise outputs without logging a reason.

Regulators are also pressuring companies over AI-assisted decisions.  The European Union AI Act requires automatic logging and human oversight for high-risk systems. The Securities and Exchange Commission (SEC) has already penalized firms for overstating AI capabilities and US banking regulators’ updated guidance has yet to fully address generative and agentic AI, leaving the accountability gap unresolved.