The increasing complexity of financial architectures and the rise of cyber threats, securing enterprise data and regulatory compliance, has never been more necessary. The identity governance landscape is a domain that can benefit significantly from this approach, as autonomous AI agents have shown that they can act in real-time and adjust as per learning. These intelligent agents can also help enhance financial security through proactive insider threat detection and dynamic compliance enforcement. Machine learning algorithms are used by AI to identify anomalies and shut down anything that appears as ZBB (Zero Based Budgeting) implementation taking place, which significantly minimizes the chances of data theft and insider fraud. Moreover, these agents are able to monitor and enforce adherence to ever-changing regulatory requirements in the field, ensuring organizations’ operational health and preventing heavy fines for non-compliance. Using autonomous AI agents minimizes business risk for organizations by allowing them to identify potential insider threats earlier, decrease false-positive rates, and streamline compliance tracking. The emergence of autonomous AI agents embedded in identity governance frameworks is the next innovative step in solving the challenge of financial data security and helping enterprises stay one step ahead of outside or insider threats.