Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems

Compliance automation is moving from paperwork to code: Traditional AI governance relies on prose documentation and manual checklists, but this paper demonstrates that regulatory obligations can be compiled into machine-checkable rules that automatically verify compliance—reducing the need for time-consuming manual audits.

Evidence matters as much as rules: The framework requires explicit documentation of what evidence supports compliance claims (through provenance tracking), meaning organizations must maintain clear records of why their AI systems made decisions, not just what decisions they made—critical for accountability in high-stakes domains like resource allocation.

Governance can be reconfigured without changing AI systems: By separating compliance rules from service code, organizations can update their governance profiles (e.g., adding fairness checks or transparency requirements) without modifying the underlying AI system itself, making it easier to adapt to new regulations.

Real-world validation is fast enough for deployment: Testing on an AI resource allocation system showed validation checks complete in 12.6-100.3 milliseconds, suggesting this approach can work in live systems without significant performance penalties.

Different governance profiles can be compared objectively: The research demonstrates a method to test whether one compliance profile is more comprehensive than another, helping organizations understand the actual differences between governance frameworks rather than relying on subjective interpretation.