THREAT INTELLIGENCE AT SCALE: LEVERAGING AI FOR PREDICTIVE CYBER DEFENSE IN ENTERPRISE NETWORKS
With the increasing number of virtual threats in our time, businesses face growing difficulty in identifying and protecting against complex attacks in large and complex network systems. Traditional threat intelligence solutions often lack the flexibility and foresight to preemptively act on emerging threats. In this paper, the authors suggest an extendable model that will employ artificial intelligence (AI) and machine learning (ML) to help in improving the predictive cyber defense services within the enterprise's network. The framework will allow for the detection of criminal activity patterns early in the event of the integration of real-time threat feeds, behavioral analytics, and anomaly detection algorithms. It was found, using simulated traffic conditions of an enterprise and publicly available data on cybersecurity, that the probability of predicting threats, acceleration of detection of detected threats and reduction in the number of false positives is significantly higher than with traditional tools. In the study, the distribution topology, performance tradeoffs, and security considerations of AI-driven threat intelligence systems at scale are also discussed. The study will lead to advances in smart, autonomous, and dynamic defense systems, bringing us potential resilient and proactive cybersecurity for large-scale enterprise infrastructures.
Threat Intelligence, Predictive Cyber Defense, Artificial Intelligence, Machine Learning, Enterprise Networks.