AegisLayer: Enterprise Privacy Middleware

AegisLayer is a zero-trust enterprise privacy middleware designed to solve the critical data security bottleneck preventing enterprises from adopting Large Language Models. When employees send prompts to cloud AI providers, sensitive PII and proprietary secrets are often leaked. AegisLayer intercepts these prompts at the network boundary and sanitizes them in real-time before they ever leave the corporate network. Our solution employs a highly optimized dual-engine architecture: 1. CPU Regex Engine: A deterministic pass that instantaneously identifies and redacts structured data like IPv4 addresses, credit card numbers, phone numbers, and API keys. 2. AMD ROCm-Accelerated NER Engine: An advanced Named Entity Recognition pipeline powered by PyTorch and HuggingFace Transformers. Optimized specifically for AMD Instinct GPUs via ROCm, this engine identifies unstructured entities (Persons, Organizations, Locations) with sub-100ms latency. As entities are detected, AegisLayer maps them to opaque, entropy-free tokens (e.g., [PERSON_1]) and stores the mapping in an ephemeral, in-memory vault. The sanitized prompt is then safely forwarded to the external LLM provider. Once the AI generates a response, AegisLayer automatically de-tokenizes the text, restoring the original values seamlessly. The vault is immediately wiped after each round-trip, guaranteeing zero persistent storage of sensitive data. AegisLayer ensures maximum data privacy, regulatory compliance, and architectural resilience without introducing any friction to the end-user experience.