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.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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What problem does Enzai solve?
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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