A network or data governance approach that divides resources into zones with distinct policies, restricting AI system access according to data sensitivity.
Architectural segmentation where environments (e.g., dev, test, prod) or data classifications (e.g., public, internal, confidential) are assigned to distinct network or logical “zones.” Access policies - firewalls, IAM roles, encryption - are tailored per zone. Governance requires defining zone trust levels, documenting inter-zone communication rules, and auditing zone configurations regularly to prevent unauthorized lateral movement or data leaks.
A healthcare AI platform enforces zone-based access: patient-identifiable data resides in a “confidential” zone accessible only by the privacy-approved analytics service, while anonymized datasets live in an “internal” zone for broader data-science experimentation - ensuring strict separation and policy enforcement across zones.
