Mitigation Strategies
Planned actions (e.g., bias remediation, retraining, feature re-engineering) to address identified AI risks and compliance gaps.
A catalogue of interventions mapped to risk types: data-drift triggers → retraining pipelines; fairness violations → bias-mitigation algorithms; security threats → input-sanitization layers; performance degradation → architecture tuning. Governance entails selecting appropriate strategies, documenting implementation, and verifying their effectiveness through follow-up assessments and tests.
After a bias audit finds that loan approvals favor one demographic, the team applies re-weighting of training samples and adds fairness constraints. They then conduct post-mitigation testing to confirm that approval rates now meet the bank’s equity thresholds.
“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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How is Enzai different from other governance tools?
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