Output validation

The process of confirming that an AI system’s outputs are correct, safe, meaningful, and in line with the expected behavior defined by the system’s requirements and intended use.

Source: AIEOG AI Lexicon (Feb 2026), adapted from NIST AI 100-1

Output validation is the practice of checking what an AI system produces before that output is used, shared, or acted upon. It answers the question: Is this output correct, safe, and appropriate for its intended use?

Output validation can be automated (rules that check outputs against defined criteria), human-driven (a reviewer assessing the output), or a combination. The appropriate approach depends on the risk level and volume of outputs.

For financial institutions, output validation is particularly important for generative AI systems (where hallucination is a risk), decision-support systems (where incorrect outputs could lead to bad decisions), and customer-facing systems (where inaccurate information could cause harm).

Output validation is a critical control in the AI governance framework. Regulators expect institutions to verify that model outputs are reasonable and appropriate. SR 11-7 requires model users to understand and critically evaluate model outputs.

Without output validation, institutions risk acting on incorrect AI outputs, delivering inaccurate information to customers, filing incorrect regulatory reports, and making flawed business decisions.

1. Define validation criteria for each use case. Specify what “correct, safe, and appropriate” means for each AI application.
2. Implement automated checks. Deploy automated validation rules that catch obvious errors, out-of-range values, and format violations.
3. Require human review for high-stakes outputs. Outputs used for regulatory filings and customer decisions should receive human review.
4. Test for hallucination. For generative AI outputs, implement fact-checking processes that verify claims against authoritative sources.
5. Log validation results. Maintain records of validation activities, including outputs reviewed, issues found, and actions taken.
6. Monitor validation effectiveness. Track how often validation catches errors and adjust processes to improve detection.

Validation, Guardrails, Hallucination, Human-in-the-Loop (HITL), Performance monitoring, Deterministic (algorithm/model)