AI-based insider trading detection is transforming financial market surveillance by identifying suspicious activities that indicate illegal trading based on non-public information. AI systems analyze trading patterns, transaction histories, and market data in real time, flagging unusual trades that deviate from normal investor behavior. Machine learning models detect sudden spikes in trading volumes, irregular order placements, or abnormal profits that may signal insider activity. Natural Language Processing (NLP) further enhances detection by monitoring news, earnings reports, financial disclosures, and social media for hints of leaked information. AI can correlate these findings with trading behavior to uncover potential cases of insider trading. Regulatory bodies and financial institutions use predictive analytics and automated alerts to investigate suspicious activities before they impact the market. Unlike traditional manual methods, AI can process massive datasets quickly, improving enforcement efficiency. However, human oversight remains essential to verify AI-generated alerts and prevent false accusations. The combination of AI-driven detection and regulatory intervention strengthens market integrity, fairness, and transparency, reducing illegal trading risks.