AI Data Loss Prevention and Sensitive Information Protection

You are designing a DLP program for generative-AI usage in [INSERT_ORGANIZATION]. The goal is to prevent sensitive data leakage into prompts, third-party models, logs, and generated outputs. 1. Authorization & Policy: Under the CISO/legal-approved AI usage policy, define allowed AI services, permitted data classifications ([INSERT_DATA_POLICY]), and user populations; shadow-IT discovery stays within approved monitoring. 2. Data Inventory: Map where regulated and confidential data lives ([INSERT_DATA_STORES]) and how it could flow into AI channels (copy-paste, plugins, RAG ingestion, API agents). 3. Detection Process: Inspect, with appropriate privacy approvals, outbound content to AI services and model API traffic for PII, secrets, source code, and regulated fields using pattern matching, NER, and ML classifiers. 4. Endpoint & Gateway Controls: Deploy endpoint DLP, a sanctioned model gateway, CASB/SSE controls, and browser safeguards that block, warn, or encrypt based on policy. 5. Prompt/Output Controls: Apply redaction and token-level filtering before data reaches external models; scan model outputs for reflected secrets or sensitive records. 6. RAG Hygiene: Enforce document classification checks at ingestion and access-control-aware retrieval so users cannot retrieve content beyond their entitlements. 7. Tooling: Integrate approved DLP ([INSERT_DLP_TOOL]), secrets scanning, the AI gateway, and proxy logs; avoid storing prompt content beyond approved retention. 8. Privacy Compliance: Align monitoring with [INSERT_REGULATION] (GDPR/PIPL/HIPAA), employee privacy notices, and minimum-necessary inspection; no covert surveillance outside policy. 9. Incident Workflow: On violation, alert the SOC, block the transaction where feasible, preserve evidence, and trigger investigation and credential rotation when secrets leaked. 10. User Enablement: Provide sanctioned alternatives (internal/private model instances) and guidance so teams can adopt AI without risky workarounds. 11. Third-Party Risk: Vet model vendors on zero-retention terms, subprocessors, data residency, and breach notification before enabling enterprise use. 12. Remediation: For each finding - block-rule tuning, user coaching, data reclassification, RAG permission fixes, and revocation of exposed secrets - assign owners and SLAs.