Adaptive Learning

CHERENKOV learns from every scan through the LATTICE memory system — a local vector database that stores embeddings of past findings, attack patterns, and compliance mappings.

How It Works

1. Scan completes → embeddings generated from findings
2. Embeddings stored in Qdrant vector DB
3. Next scan → LATTICE retrieves similar past findings via semantic search
4. TENSOR uses retrieved context to generate more targeted attack chains

Benefits

• Reduced false positives — Learns which findings are real vs noise
• Faster scans — Prioritizes attack vectors that worked before
• Compliance mapping — Automatically maps findings to regulatory frameworks
• Dialect-aware — Supports Arabic diglossia via dialect-to-MSA RAG pipeline

graph TD
    A[Component] --> B[Subcomponent]
    B --> C[Implementation Detail]