White-Box Testing
Assessing AI systems with full knowledge of internal workings (code, parameters, architecture) to verify correctness, security, and compliance.
A thorough validation approach where testers inspect source code, model weights, and configuration to craft tests targeting specific logic paths, parameter ranges, and potential vulnerabilities. Governance integrates white-box tests into CI/CD pipelines, requiring coverage thresholds for critical modules, automated security‐analysis tools, and manual code reviews to ensure models behave as intended and comply with policy.
A self-driving car’s perception module undergoes white-box testing: engineers inject edge-case sensor inputs directly into the model’s internal layers to verify that object-detection logic correctly handles occlusions, and static-analysis tools scan code for insecure library calls before any deployment.
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Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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