Guide

Stopping sensitive data leakage in AI workflows

How sensitive data moves through prompts, files, retrieval context, responses, and tool outputs.

Guide8 min readRisk and Compliance, Security Engineering

Leakage Paths

AI leakage can occur when users paste data into tools, applications retrieve restricted context, models expose source text, or agents send sensitive outputs into external systems.

Data Classes

Policies should recognize PII, PHI, payment data, credentials, source code, client material, regulated communications, and proprietary strategy. Generic keyword matching is rarely enough.

Controls That Work

Combine detection, redaction, block decisions, coaching, and approved destinations. For agentic systems, include tool outputs and memory in the inspection scope.

Evidence for Review

Leakage prevention must produce audit-ready records without retaining unnecessary sensitive content. Teams need proof of policy decisions, not new data stores full of risk.

More Resources

Continue exploring AI security thinking.

Guide

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The enterprise guide to AI agent runtime security

A practical security model for agents that retrieve context, reason over data, call tools, and act across business systems.

For CISO, Head of AI, Security Engineering

Executive Brief

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How CISOs can govern workforce AI without slowing adoption

Policy patterns for shadow AI discovery, prompt DLP, app governance, and employee enablement.

For CISO, CIO, Risk and Compliance

Research Note

10 min read

Red teaming RAG systems for poisoned context and over-disclosure

Testing approaches for retrieval abuse, source trust, sensitive context leakage, and model response drift.

For Security Engineering, AI Product Teams

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