Constrained-Action AI Remediation for SIEM/XDR via a NeMo-Guardrails Proxy

Key points

• Large Language Models (LLMs) are increasingly proposed as reasoning engines that triage alerts and, in autonomous deployments, issue commands that block IPs, kill processes, or quarantine files on production hosts.1
• This coupling introduces a new risk: a single adversarial alert can become a remote code path through the LLM's reasoning, leading it to recommend an action the SOC then executes.1
• The stock proxy lifts injection recall from 25.0% to 94.5% at a 0.1% false-positive rate, and a live red-team exercise confirms that the closed intent vocabulary and argument validator contain the observed LLM failure modes before any command crosses the trust boundary.1
• As an architectural fit (not yet a measured operational-technology deployment), the constrained-action property suits critical-infrastructure settings where a wrong remediation has physical, not merely operational, consequences.1

Sources (1)

• [1]Constrained-Action AI Remediation for SIEM/XDR via a NeMo-Guardrails Proxy
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 12:00 PM

  Security Operations Centers (SOCs) for information technology and operational technology share one incident-response problem: a flood of correlated alerts and too few analysts.

  Large Language Models (LLMs) are increasingly proposed as reasoning engines that triage alerts and, in autonomous deployments, issue commands that block IPs, kill processes, or quarantine files on production hosts.

Extractive summary: sentences quoted from the sources.