- Alert triage summaries that flip uncooked telemetry into a brief “what occurred, why it issues and what I ought to verify subsequent” narrative
- Investigation copilots that generate a timeline from logs, tickets and chat transcripts, then spotlight gaps and really helpful pivots
- Detection engineering help for drafting Sigma, YARA or question language snippets that an engineer can assessment and check
- Vulnerability administration copilots that cluster comparable findings, clarify exploitability in enterprise phrases and suggest patch home windows
- Coverage and requirements Q&A, the place the assistant solutions questions by citing the precise inside management language it relied on
Even in these secure situations, the working rule I exploit is easy: deal with the LLM output as untrusted. If a mannequin is allowed to write down code, suggest a containment motion or reference inside knowledge, it is best to assume it could hallucinate, be socially engineered or be prompted into unsafe conduct.
The OWASP neighborhood has cataloged frequent failure modes for LLM-enabled purposes, together with immediate injection, insecure output dealing with, delicate info disclosure, extreme company and overreliance. These are usually not educational ideas. They map on to the methods LLMs fail in security workflows. See OWASP Prime 10 for LLM purposes.
Virtually, I consider an LLM deployment in security as three layers: the mannequin, the info it could see and the actions it could take. You may get important worth by widening the primary layer (e.g., through the use of higher fashions or prompts) whereas protecting the opposite two layers constrained.
