Agentic AI architecture proposes data sanitization before LLM calls

The author proposes a "Data Egress Boundary" approach for Agentic AI systems to prevent sensitive information from being sent to Large Language Models (LLMs). This involves implementing a sanitization layer before data is processed by retrieval-augmented generation (RAG) or embedding models. The proposed architecture includes cleaning raw data, validating it, and then feeding it to RAG or LLMs, with a strict rule to avoid calling the model if sensitive data like API keys or bearer tokens are still detected after sanitization. This architectural approach aims to provide LLMs with necessary context without exposing unneeded sensitive details, thereby enhancing AI governance. AI

IMPACT Proposes architectural changes to enhance data security and AI governance in agentic systems, potentially influencing how sensitive data is handled.

RANK_REASON The item discusses a proposed architectural approach for managing data sent to LLMs, rather than announcing a new product, model, or research finding.

Read on dev.to — LLM tag →

• Agentic AI
• AI Architecture
• API keys
• Bearer Tokens
• embedding model
• generative artificial intelligence
• governance of artificial intelligence
• JWTs
• LLM
• retrieval-augmented generation
• Vector DB

• product
• safety
• infra

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