Harm Assessment
Evaluating potential negative impacts (physical, psychological, societal) of AI systems and defining mitigation strategies.
A targeted review that categorizes harms (safety, privacy, economic, reputational) specific to the AI application. It uses stakeholder impact mapping, severity-likelihood scoring, and mitigation planning (design changes, guardrails, human oversight). Results feed into risk registers and inform whether to proceed, pause, or redesign the system.
Before launching an automated-credit-decision AI, a bank conducts a harm assessment: they identify potential economic harms (denial of services), reputational harms (public backlash), and propose mitigations (appeal processes, external review panels). This ensures that decision pathways and user-support channels are in place before rollout.
“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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