Workload Segregation
Separating AI compute environments (e.g., dev, test, prod) and data domains to limit blast radius of failures or security breaches.
The practice of isolating computational workloads, data stores, and network segments according to environment or classification level (development vs. production, PII vs. non-PII), enforced through network policies, distinct IAM roles, and separate clusters or namespaces. Governance defines environment boundaries, data-domain labels, and access controls, ensuring that a compromise or failure in one area does not propagate to critical systems or expose sensitive data.
A cloud-based AI platform runs development workloads in a separate Kubernetes namespace with no access to production databases. Only approved release pipelines can promote container images to the production namespace - enforcing strict workload segregation and minimizing risk of accidental data exposure.
“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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