AI-Driven Threat Intelligence and Automated Incident Response: Enhancing Cyber Resilience through Predictive Analytics

Abstract

Cybersecurity is a critical concern for organizations as the complexity and volume of cyber threats continue to grow. Traditional methods of threat detection and incident response, such as signature-based detection and rule-based systems, are increasingly ineffective against sophisticated and evolving attacks. This study explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing threat intelligence and automating incident response. By leveraging predictive analytics, anomaly detection, and real-time data processing, AI-driven systems offer significant improvements in both the detection and mitigation of cyber threats. The research evaluates the effectiveness of an AI-powered threat intelligence system across various attack types, including phishing, ransomware, DDoS attacks, Advanced Persistent Threats (APTs), and malware variants. Results show that the AI system achieves a 94.44% detection rate for phishing attacks, with significant improvements in response times and mitigation accuracy. Predictive analytics further enhances cyber resilience by forecasting potential threats with 90% accuracy, allowing for proactive defense strategies. Despite the positive results, the study acknowledges limitations such as dataset diversity, model biases, and scalability issues. The findings suggest that AI, when integrated with human expertise, can revolutionize cybersecurity by providing faster, more accurate, and scalable solutions. Future research should focus on improving the explainability of AI models, addressing ethical concerns, and exploring the scalability of AI-driven solutions in large-scale networks. The study advocates for the adoption of predictive analytics as a core element in cybersecurity practices to build more resilient systems capable of combating the increasing threat landscape.