VALOR-RAG
Value-aware security framework for enterprise RAG
A security framework for retrieval-augmented generation systems focused on improving trust, reliability, and robustness against retrieval failures and adversarial manipulation. Incorporates trust signals and retrieval quality analysis for enterprise RAG deployments. Under review at HICSS 2026.
- Trust-aware retrieval analysis over enterprise knowledge bases
- Context quality evaluation for generated responses
- Retrieval security mechanisms against adversarial manipulation
- Adaptive defense strategies tuned to enterprise RAG pipelines
Architecture
Retrieval
Vector store query with candidate document set
Trust Scoring
Trust-signal analysis over provenance and retrieval quality
Context Validation
Quality evaluation of retrieved context before generation
Adaptive Gate
Defense strategy that admits or flags low-trust context
Generation
LLM generates only from validated, trust-weighted context
Python · PyTorch · Transformers · Vector Databases