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

arXiv:2610.09906 · cs.CR, cs.AI, cs.CL · Submitted 2026-10-07 · Read on arXiv

cs.CR, cs.AI, cs.CL

Submitted: 2026-10-07

Updated: 2026-10-07

Comments: 7 pages, 3 figures, 4 tables. Accepted at the 2026 IEEE International Conference on Cyber Security and Resilience (IEEE CSR 2026)

Code: https://github.com/microsoft/presidio

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 6× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Human-in-the-Loop — 3× in this paper · explained in 3 episodes
• Provenance — 3× in this paper · explained in 5 episodes
• Agentic AI — 2× in this paper · explained in 2 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Agentic AI systems — 1× in this paper · explained in 1 episode
• Alert Fatigue — 1× in this paper
• LLM Integration — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 8 episodes
• Personally Identifiable Information — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Detecting Language Model Attacks with Perplexity
• Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
• Universal and Transferable Adversarial Attacks on Aligned Language Models

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