Leveraging AI-Driven Predictive Cyber Analytics for Proactive Threat Hunting in Enterprise Security

In the rapidly evolving cyber threat landscape, organizations must shift from reactive to proactive threat detection strategies. Cybersecurity teams face an overwhelming number of security alerts, making it difficult to identify true threats amidst the noise. This research explores how AI-driven predictive analytics, integrated with Microsoft Sentinel, Data Lakes, and Large Language Models (LLaMA 2 and GPT-4), can transform cyber threat hunting by leveraging machine learning, natural language processing, and data correlation techniques. This study presents an LLM-driven predictive cyber analytics framework that aggregates security telemetry to detect anomalous user behaviors, phishing campaigns, malware activity, lateral movement, and insider threats. By utilizing predictive modeling, machine learning anomaly detection, and AI-driven triage, this research aims to improve decision-making, automate security operations, and enhance cyber resilience in organizations. Limitations include the high cost of training the model with the desired amount of data.