Risk Assessment
The process of identifying, analyzing, and prioritizing potential harms or failures in AI systems to determine appropriate mitigation strategies.
A systematic activity that catalogs threats (e.g., bias, privacy breaches, security exploits), evaluates their likelihood and potential impact (financial, reputational, safety), and ranks them by risk score. The output is a risk register that drives mitigation planning. Risk assessments are revisited whenever models, data, or operational contexts change, ensuring that emerging threats are captured throughout the AI lifecycle.
Before deploying a credit-decision AI, a bank’s risk team maps risks (e.g., false positives denying credit, data leaks), assigns likelihood/impact ratings, and identifies top risks. They then develop targeted controls - enhanced human review for denied applications and encrypted logging - to address the highest-priority items.
“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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