Query Privacy
Techniques and policies to protect sensitive information in user queries, ensuring that logged inputs do not compromise personal or proprietary data.
Involves anonymization or pseudonymization of query text before storage, selective redaction of PII, and access controls on raw query logs. Privacy-preserving query logging frameworks apply rules (regex masking, tokenization) and enforce strict retention or deletion schedules. Governance audits log-handling processes and ensures compliance with privacy regulations and internal data-usage policies.
A legal research AI masks client names and case references in query logs by replacing them with tokens (e.g., CLIENT_ID_123). Only compliance officers with special clearance can view re-identification tables, ensuring that sensitive client queries are protected while preserving logs for analytics.
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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.
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How is Enzai different from other governance tools?
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