AI-Powered Incident Response Automation in Critical Infrastructure Protection

The increasing frequency, sophistication, and speed of cyberattacks on critical infrastructure demand advanced, adaptive, and rapid incident response capabilities. AI-powered incident response automation offers a transformative approach to safeguarding essential sectors such as energy, transportation, water, healthcare, and communications by enabling real-time detection, analysis, and mitigation of threats. This study explores the integration of artificial intelligence with security orchestration, automation, and response (SOAR) platforms to enhance the efficiency, accuracy, and resilience of incident management in critical infrastructure environments. Leveraging machine learning, natural language processing, and deep learning models, AI-driven systems can automatically correlate threat indicators, analyze network anomalies, prioritize alerts, and execute predefined containment or remediation actions with minimal human intervention. By processing large volumes of heterogeneous security data including logs, sensor readings, and operational technology (OT) telemetry these systems reduce mean time to detect (MTTD) and mean time to respond (MTTR), thereby minimizing operational disruptions and potential safety hazards. The paper evaluates key AI capabilities such as predictive analytics for proactive threat hunting, reinforcement learning for adaptive response strategies, and explainable AI for transparent decision-making in regulated environments. Challenges including integration with legacy systems, false positives, adversarial AI risks, and compliance with sector-specific regulations are critically assessed. Case studies from power grid cybersecurity, intelligent transportation systems, and smart water management highlight real-world deployments, demonstrating measurable improvements in incident containment speed, threat neutralization rates, and operational continuity. The findings indicate that AI-powered incident response automation not only strengthens cyber resilience but also aligns with national and international frameworks for critical infrastructure protection, such as NIST, ISO 27001, and sector-specific standards. Future research directions include developing interoperable AI models for multi-sector coordination, enhancing trust through AI explainability, and integrating AI with blockchain for secure audit trails. By bridging advanced analytics with automated security operations, AI-powered incident response emerges as a crucial enabler for safeguarding critical infrastructure in an era of increasingly complex and high-impact cyber threats.