AI BriefingPolicyGuard on Amazon Bedrock: A Pre-Model Semantic DLP Framework for Coding Assistants
Key point
Introducing PolicyGuard, a Pre-Model DLP framework that prevents sensitive information leakage in coding assistant prompts.
Details
The proliferation of AI coding assistants has created new pathways for API keys and PII to leak to external LLMs. Existing rule-based DLP systems miss context-dependent information, and LLM safety classifiers have limitations in enforcing organization-specific policies.
PolicyGuard is a Pre-Model Semantic DLP framework that blocks sensitive information before API calls by leveraging Amazon Bedrock-based classifiers at the coding agent Hook stage. Policies are defined via natural language policy files (policy.txt), allowing immediate enforcement without fine-tuning, while a fail-closed design ensures security.
Evaluation on a 2,000-item multilingual dataset achieved an Effective Block Rate (EBR) of 96.5% and a False Positive Rate (FPR) of 3.0%. This represents a significant performance improvement over existing Presidio (EBR 62.2%), with verified policy portability across various LLM models.
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