AI Evals

Expanded Definition

AI evals are not just ad hoc model checks. They are repeatable measurement processes that compare system outputs against defined expectations, using the same inputs, scoring criteria, and run conditions so teams can detect drift, regressions, and unsafe behaviour. In practice, AI evals sit between product testing and governance: they help answer whether a feature is useful, reliable, and safe enough for release, while also exposing where human review is still required.

For NHIMG, the important distinction is that evals assess observed behaviour, not intent. A model can appear fluent in a demo yet fail on edge cases, policy-sensitive prompts, or tool-using workflows. That is why AI evals increasingly cover multiple dimensions such as accuracy, refusal quality, robustness, latency, and harmful output handling. Definitions vary across vendors on what should count as an eval, but a defensible program uses documented test sets, explicit rubrics, and versioned results. The strongest programs also align evaluations to risk, rather than treating every model output as equally critical. The most common misapplication is treating a one-time demo score as a production-ready evaluation, which occurs when teams skip repeatable test design and controlled scoring.

Examples and Use Cases

Implementing AI evals rigorously often introduces process overhead, requiring organisations to weigh release speed against confidence in model behaviour.

• A support chatbot is tested against a fixed set of customer questions to measure factual accuracy, refusal behaviour, and escalation quality before launch.
• An internal RAG assistant is evaluated on whether it cites the right source documents and avoids answering when retrieval returns weak evidence.
• An AI coding assistant is scored on secure code suggestions, including whether it recommends unsafe patterns or mishandles secrets in generated examples.
• An agentic workflow is run through repeated scenarios to see whether tool calls stay within authorised boundaries and fail safely when tools are unavailable.
• A policy-facing GenAI feature is benchmarked against prompt sets designed to surface bias, hallucination, and prompt injection resilience, informed by guidance such as the NIST Cybersecurity Framework 2.0.

These use cases are most useful when the test data reflects the real operating environment, not just clean benchmark prompts. Teams often maintain separate eval tracks for regression testing, safety testing, and release gating so that one weak dimension does not get hidden by another.

Why It Matters for Security Teams

Security teams need AI evals because AI failures rarely present as obvious outages. More often, they appear as subtle trust failures: an assistant invents an answer, an agent executes an unintended action, or a workflow leaks sensitive context through tool use. Without evals, these risks are discovered only after deployment, when the blast radius is already real.

This matters directly for identity and NHI governance when AI systems can access secrets, invoke privileged APIs, or act on behalf of users. In that setting, evals are part of proving that the system respects access boundaries and operational guardrails, not just that it “sounds correct.” Good programs connect eval results to release decisions, incident response, and change management, which is consistent with the control-oriented approach reflected in the NIST Cybersecurity Framework 2.0. Organisations typically encounter the real value of AI evals only after a model or agent has produced a harmful output, at which point systematic testing becomes operationally unavoidable to contain recurrence.

Related resources from NHI Mgmt Group

• What is the difference between evals and observability in AI operations?
• What breaks when AI evals are not grounded in production behaviour?
• How can teams keep AI evals relevant as models and users change?
• How should teams design AI agent evals so models cannot game them?