Design and Computational Modeling of an AI-Based Automated Cybersecurity Incident Response System

Modern cybersecurity operations face unsustainable alert volumes, averaging 22000 weekly alerts with 68% false positives, overwhelming defenses and delaying incident response due to limitations in conventional SOAR platforms. To address this, an AI-driven Automated Incident Response (AIR) system is proposed, integrating STIX/TAXII multimodal fusion for unified data ingestion, attention-LSTM networks for adaptive threat recognition across temporal sequences, Bayesian game-theoretic decision layers for strategic response planning, and DRL validation for real-time optimization. This architecture reduces false negatives by 42% in C2 tunneling detection and achieves Nash equilibrium in 97.3% of adversarial engagements. Rigorous testing on hybrid infrastructure datasets (100 K normal events, 20K DDoS, 5K C2 attacks) demonstrates a 93% mean F1-score across attack scenarios, end-to-end latency of 58.3 ms, and $12.5\times $ higher strategy updates/sec versus baselines. Compared to existing models, the system improves detection F1 by 10.7%, reduces false positives by 39%, and enhances energy efficiency to 1850 events/Joule ( $2.98\times $ Snort). The framework establishes a new paradigm for agile, auditable incident response validated by STIX action chains.