Zero Trust for AI applies the "never trust, always verify" security model to an organization's AI ecosystem. It assumes that no user, device, application, or data flow involving AI tools should be automatically trusted, regardless of whether it originates inside or outside the corporate network.
Core zero trust principles applied to AI include: verify explicitly (authenticate and authorize every AI interaction based on all available data points — user identity, device health, location, data classification, and behavior patterns), use least-privilege access (grant minimum necessary permissions to AI tools and users — limit which data AI can access and which capabilities are available), assume breach (design AI security controls assuming that any AI tool could be compromised — implement monitoring, segmentation, and containment capabilities).
Implementation of zero trust for AI involves: identity-aware AI access (SSO integration, MFA for AI tools, conditional access policies), data-centric protection (classifying data before it reaches AI tools, enforcing handling rules at the data level), micro-segmentation (isolating AI workloads and limiting blast radius), continuous monitoring (real-time analysis of AI usage for anomalies), automated response (immediate policy enforcement when violations are detected), and encryption (protecting data in transit and at rest across AI interactions).
Zero Trust for AI is particularly important because AI tools often require broad data access to be effective, creating tension between utility and security that must be carefully managed.
