Autonomous Cyber Incident Response Using Cognitive Security Agents

Abstract

Autonomous Cyber Incident Response (ACIR) harnesses the capabilities of cognitive security agents—software entities endowed with perception, reasoning, and learning functions—to detect, analyze, and mitigate cyber threats without continuous human oversight. This manuscript expands on an ACIR framework that integrates real‑time telemetry ingestion, knowledge graph construction, hybrid cognitive architectures (ACT‑R/SOAR), and reinforcement learning (RL) to orchestrate end‑to‑end incident response workflows. The extended abstract details the motivation, architectural components, experimental setup, key performance metrics, results, and implications for cybersecurity operations. Through extensive simulations emulating enterprise networks with cloud and on‑premises assets, ACIR agents demonstrated a 60% reduction in mean time to detect (MTTD) and a 50% reduction in mean time to respond (MTTR) compared to traditional SIEM‑based human workflows. False positive rates remained stable at approximately 5%, illustrating that speed improvements did not compromise accuracy. Importantly, RL‑driven adaptation yielded a 30% improvement in first‑shot remediation success across repeated attack scenarios, evidencing the agents’ ability to learn from past outcomes and refine decision policies. The architecture’s modular design facilitates incremental integration with existing security infrastructures, enabling organizations to adopt ACIR capabilities alongside legacy tools.