A Self-Healing Multi-Agent Data Loss Prevention (DLP) Proxy with Context-Aware PII Sanitization and SLA-Driven Circuit Breaking for Enterprise LLMs

The rapid integration of Large Language Models (LLMs) into enterprise workflows has introduced severe data exfiltration risks, including the inadvertent leakage of Personally Identifiable Information (PII), Protected Health Information (PHI), cloud infrastructure credentials, and proprietary corporate context. Existing Data Loss Prevention (DLP) mechanisms face a fundamental operational trade-off: deterministic rule-based filters (Regex) execute at microsecond latency but fail to capture unstructured semantic sensitivity, whereas standalone LLM-based guardrails suffer from high inference latency, stochastic masking hallucinations, and single-point-of-failure vulnerabilities. In this paper, we propose an autonomous, self-healing Multi-Agent AI DLP Security Proxy engineered under a Zero-Trust paradigm. Our framework introduces a cascaded three-layer sanitization pipeline uniting: (1) a deterministic Layer-1 Pre-Filtering Agent for critical infrastructure secrets and structured identifiers, (2) a context-aware Layer-2 Neural PII Detection Agent for unstructured entities and domain-specific clinical and financial disclosures, and (3) a Layer-3 Retrieval-Augmented Generation (RAG) semantic policy auditing engine powered by ChromaDB vector embeddings. Furthermore, we formulate a dynamic mathematical risk scoring model on a standardized [0, 100] scale and integrate an SLA-driven self-healing circuit breaker (T_SLA = 2.5 s) paired with a Shadow Auditing Agent to intercept masking hallucinations and runtime service exceptions. Experimental ablation studies and chaos-engineering fault-injection benchmarks across healthcare, financial, and DevOps scenarios demonstrate that our hybrid architecture achieves robust entity sanitization with sub-millisecond to millisecond execution latency, while guaranteeing a 0.0% unmitigated data leak rate across all induced timeout, hallucination, and crash failure modes.