This study proposes the Automated Compliance Engine (ACE), an AI-orchestrated framework that links intrusion detection, regulatory control mapping, and automated countermeasure deployment for security operations environments. ACE integrates Suricata IDS, a LangGraph five-stage workflow, retrieval-augmented generation, NIST SP 800-53 Rev. 5 control retrieval, large language model-based control ranking, and SSH-based Suricata rule deployment. The framework was evaluated in a two-virtual-machine laboratory using eight simulated attack scenarios and three legitimate traffic tests. ACE processed all eight attack alerts, achieved 100% alert detection, 0% false positives across legitimate traffic tests, generated deployable AUTO rules for 37.5% of alerts, verified 100% deployment of generated rules, and achieved an average NIST control-mapping confidence score of 0.87. The DeepEval assessment produced 1.0 scores for answer relevancy and faithfulness, while the hallucination score reflected known LLM-as-judge calibration limitations. The evaluation was conducted in a controlled environment with a limited attack set. Further validation is required using enterprise-scale telemetry, multiple IDS sensors, and an analyst-in-the-loop review. The results demonstrate how AI-assisted security automation can reduce analyst effort in translating IDS alerts into compliance-relevant evidence and defensive actions. ACE contributes an integrated detection-to-compliance-to-response workflow combining IDS telemetry, RAG-based control mapping, and automated rule deployment.

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