AI-Driven Threat Intelligence for Enterprise Cybersecurity

Abstract

The research investigates how artificial intelligence technologies operate within enterprise cybersecurity frameworks by studying threat intelligence automation and advanced detection techniques. The research uses extensive literature analysis to show that machine learning algorithms achieve detection accuracies above 95% and deep learning approaches enhance F1-scores by up to 33% above traditional methods. Real-time data integration with behavioral analytics boosts threat identification abilities, allowing systems to detect 150,000 threats per minute and preventing 8 out of 10 attacks from causing system compromise. The current implementations primarily use centralized architectures, but distributed approaches show benefits for particular deployment situations. The research identifies essential challenges, which include privacy concerns, transparency limitations, algorithmic bias, data quality issues, and integration complexity. The research demonstrates that effective countermeasures against advanced threats require security innovations governed by comprehensive frameworks that balance technological capabilities with ethical considerations through continuous evaluation processes.