What should security teams do first when building LLM evals?

Start with the workflow outcome, not the rubric

The first move is to define the smallest pass or fail result that proves the workflow worked. For LLM evals, that means naming the user task, the expected success condition, and the minimum evidence that the model completed the workflow correctly. If the team cannot state that in one sentence, the eval is too abstract to be useful.

That baseline should be narrow enough to distinguish success from failure before you add quality layers. A good first eval answers, “Did the system do the thing?” not “Was every output ideal?” This keeps the team focused on validity before elegance, which is the difference between a test and a scoring exercise.

For security teams, this is also where the control boundary starts to matter. If the workflow depends on retrieval, tool use, approvals, or downstream actions, the pass or fail condition should reflect the exact step that could create harm or prove safe execution.

Test the baseline against realistic cases

Once the outcome is defined, run it across several representative cases that reflect how the workflow will actually be used. The point is to see whether the eval can separate stable success from edge cases, prompt drift, or brittle instructions. A baseline that only works on one happy-path example is not yet a reliable eval.

Use cases that vary by input shape, user intent, and context strength. The goal is not to broaden the rubric immediately, but to check whether the same pass or fail rule holds under normal variation. If the result changes wildly across ordinary cases, the task definition is still too loose or the success criterion is too vague.

This is also where teams should resist overfitting to a single prompt template. Good evals measure the workflow, not the wording of one test prompt. If a small prompt change flips the result, the eval is probably measuring surface form instead of actual task completion.

Add quality dimensions only after the baseline is stable

Once the pass or fail outcome is consistent, then add secondary dimensions such as completeness, concision, tone, groundedness, or hallucination avoidance. Those dimensions are useful, but they are only meaningful after the team already knows what “worked” means. Otherwise, you end up scoring style and certainty before you have confirmed task success.

That sequencing matters because complex rubrics create noisy disagreement. Reviewers may diverge on subjective dimensions even when the core workflow is correct, and that can hide whether the system is actually improving. Start with one clear outcome, then layer in finer judgment where it changes decisions.

For higher-risk workflows, keep the first rubric especially simple. The more a workflow can trigger an action, expose data, or support a decision, the more important it is to anchor the eval on the concrete task result before adding softer quality measures.

Risk and Threat Considerations

LLM evals become misleading when teams measure polish instead of failure. A noisy rubric can make a weak workflow look better than it is, while also hiding where the model is brittle, overconfident, or unsafe in realistic use.

Failure mechanism: The team begins with multiple subjective criteria, so reviewers disagree on scoring before the workflow’s basic success condition is even established. That makes the eval hard to trust and easy to game with improved wording rather than better behavior.

Impact: Security teams can green-light a workflow that still fails on the core task, misses unsafe outputs, or behaves inconsistently when the input changes. In practice, that means weaker release decisions and less confidence in whether the system is ready for broader use.

Practitioner Guidance

What to prioritise: Define one primary pass or fail criterion first, then build a small test set that proves whether the workflow succeeds under normal variation. If the workflow involves tool use or downstream action, make the success condition reflect the exact point where a mistake would matter.

What to verify: Before expanding the rubric, verify that multiple reviewers would reach the same conclusion on the baseline cases. If they cannot, the eval needs clearer task definition, not more scoring dimensions.

Common mistake: Treating completeness, style, and hallucination checks as the starting point. That usually produces a rich-looking rubric with weak decision value.

Practitioner takeaway: The best first eval is the one that tells you, with minimal ambiguity, whether the workflow actually worked. Everything else should come after that signal is stable.

Related resources from NHI Mgmt Group

• How should security teams choose between binary and numeric evals for LLM quality checks?
• What should security teams do first when building a vulnerability management programme for the SOC?
• What should security and compliance teams do first when building a trust management programme across vendors and frameworks?
• How should teams get started building effective evals for LLM systems?