Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log Analysis
Large Language Models (LLMs) are increasingly being deployed in cybersecurity operations to assist cybersecurity analysts with rapid decision-making against emerging threats. However, there is a main criteria that must be met when using LLMs in cybersecurity, that is, trust in the generated outputs. As Agentic AI is integrated into operational systems, a robust evidence attribution and provenance tracking technique is essential to trace the origins of model generations. When autonomous agents make a decision (right or wrong), the ability to trace back through the decision chain is critical, as
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- PossiblePossibly related (embedding) · 61%Demystifying agentic AI: How to build production-ready AIOps with open source models →
- PossiblePossibly related (embedding) · 57%Prompt injection is exploiting enterprise AI's biggest design flaws by targeting agents, RAG pipelines and model routers →
- LinkedLinked via arxiv author · 85%Reza Fayyazi →
“Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log An”
- LinkedLinked via arxiv author · 85%Michael Zuzak →
“Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log An”
- LinkedLinked via arxiv author · 85%Shanchieh Jay Yang →
“Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log An”