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I’m currently building a cybersecurity-focused RAG (Retrieval-Augmented Generation) system designed to act as a first-line analyst for SOC workflows and potentially assist offensive/security testing use cases. Core idea: Ingest logs, alerts, and raw telemetry Map activity to MITRE ATT&CK techniques Provide structured triage (technique chain, confidence, reasoning) Suggest containment/remediation steps Reduce analyst fatigue on repetitive investigations What I have so far: Early working prototype (test version functional) Handles scenarios like: PowerShell spawned from Office → outbound to suspicious domain Maps to techniques (e.g., execution + C2) Outputs triage-style report instead of raw LLM text What I’m trying to validate: For SOC analysts: How much time could something like this realistically save per alert? Would you trust it as a Tier 1 triage assistant, or just as enrichment? For detection engineers: Does structured reasoning + MITRE mapping add real value, or is it noise? For red teamers / offensive: Any value in simulating detection paths or validating stealth against such systems? Existing work: I’m aware of SIEM enrichments and some LLM-based copilots, but haven’t seen many tightly integrated RAG + ATT&CK reasoning pipelines. Are there existing tools/projects doing this well that I should study? Constraints I’m thinking about: Avoiding hallucinated technique mapping Not hardcoding detection logic Making it generalizable across environments (not SIEM-specific) Keeping outputs deterministic enough for real SOC use If you’ve worked in SOC / IR / detection engineering: What would make this actually usable vs just another “AI security tool”? submitted by /u/Think-Application240 (link) (comments)
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