Evaluating AI approaches for compliance workflows
Assess rules, model outputs and human authority against the same workflow requirements instead of relying on architecture labels.
Turn architecture claims into test questions
When a supplier describes an approach as neuro-symbolic, rules-based or LLM-based, ask what the label means in the proposed system. Which component produces a recommendation, which checks a policy and which can carry out an action? Require an explanation for the actual workflow.
Use the same evaluation cases
Include ordinary examples, incomplete inputs, conflicting instructions, unfamiliar cases and proposals outside the permitted boundary. Define expected behaviour before testing. Retain failures and uncertainty alongside successful examples so comparisons do not depend on selected demonstrations.
Separate evidence from a model explanation
Ask for the source used, model or rule version, applicable policy, reviewer decision and observed result. A persuasive narrative is not a substitute for a record of the input and action. Check whether another reviewer can follow the decision without depending on the model to recreate it.
Keep authority explicit
Specify who may approve, reject or override a proposal and what happens if nobody approves. Check that an unsupported recommendation cannot bypass the agreed boundary. An architecture name does not establish legal compliance or confer authority on an automated system.
Evaluate and review over time
The NIST AI RMF Core describes testing and measurement before deployment and during operation. Use the CloudSeals governance checklist to structure questions. CloudSeals does not claim a benchmark advantage for one architecture through this guide.
Discuss your review requirements
Bring the inputs, operating boundaries and evidence needs for one workflow.