Robustness
The ability of an AI system to maintain reliable performance under a variety of challenging or adversarial conditions.
The property of withstanding input perturbations, distribution shifts, or attack vectors (adversarial examples). Achieved through adversarial training, ensemble methods, or robust optimization. Governance requires specifying robustness requirements for each use case, testing under defined stress scenarios, and incorporating robustness checks into validation and monitoring processes to ensure systems remain dependable in real-world conditions.
A self-driving car vendor subjects its vision system to simulated fog, glare, and adversarial patch attacks. They incorporate these adversarial examples into the training set and enforce a governance policy that performance under each condition must meet minimum detection rates before highway deployment.
We help you find answers
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.
Who is Enzai built for?
How is Enzai different from other governance tools?
Can we start if we have no existing AI governance process?
Does AI governance slow down innovation?
How does Enzai stay aligned with evolving AI regulations?
Research, insights, and updates
Empower your organization to adopt, govern, and monitor AI with enterprise-grade confidence. Built for regulated organizations operating at scale.