INTELLIGENT THREAT DETECTION AND AUTOMATED RISK MANAGEMENT IN IT USING MACHINE LEARNING

Abstract

This research investigates how machine learning enhances cyber security practices within IT management systems across 120 organizations between 2021 and 2025. Findings indicate that ML-powered intrusion detection systems reached 94.7% accuracy in detecting advanced threats, while traditional rule-based systems only achieved 78.3%. The adoption of ML reduced the average time to detect threats by 63%, from 18.4 hours to 6.8 hours, and cut average response time by 58%, from 12.1 hours to 5.1 hours. Statistical results show a 72% inverse relationship between ML adoption in security operations and the number of data breaches per year.Survey responses from 4,200 IT managers reveal that 68% of organizations using ML for log monitoring and anomaly detection experienced 41% fewer false positives and a 37% boost in vulnerability management efficiency. Financial analysis shows that each 1% rise in ML automation of security tasks is linked to $2.3 million in annual savings on security operations.

Predictive ML models were able to forecast 83% of phishing and malware attacks 48 hours in advance, with 89% precision. Companies using ML-based risk scoring and automated patching also showed 29% higher compliance with ISO 27001 requirements. At a national level, countries with 50% or more ML integration in cyber infrastructure saw 33% lower success rates of ransomware attacks.The study concludes that embedding machine learning into IT management enables a proactive, scalable, and cost-effective defense strategy. It recommends widespread enterprise adoption to strengthen security resilience and improve operational performance.