Operational Resilience
The ability of AI systems and their supporting infrastructure to anticipate, withstand, recover from, and adapt to disruptions or adverse events.
Involves redundancy (failover servers), disaster-recovery plans (backups, warm-standby clusters), autoscaling to handle traffic spikes, and chaos-engineering drills to test system robustness. Governance requires defining resilience SLAs (RTO, RPO), conducting regular drills, and embedding resilience requirements into system design and procurement.
A healthcare AI for critical-care monitoring runs in an active-active cloud deployment across two regions. If one region fails, traffic seamlessly shifts to the other. Quarterly chaos tests simulate region outages, verifying automatic failover within the one-minute SLA - ensuring uninterrupted patient monitoring.
“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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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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