This paper examines how emerging global AI frameworks and regulations, including the EU AI Act and NIST AI RMF, shift compliance from abstract ethics to operational cybersecurity controls. As organizations integrate large language models and autonomous agents, security risks converge on two central priorities: preventing unauthorized access or exfiltration of proprietary model weights, and protecting training datasets against malicious data poisoning.
Because artificial intelligence systems inherently rely on existing enterprise infrastructure, permissions, and directories, they can rapidly amplify existing permission sprawl and overprivileged accounts. To achieve regulatory readiness, organizations must unify identity and data security across a continuous operational lifecycle.
This practical approach entails discovering and classifying sensitive AI assets, enforcing least-privilege access, eliminating standing privileges, applying endpoint data loss prevention, tracking file integrity, and maintaining detailed audit trails for incident investigation.
Ultimately, connecting identity governance with data protection provides a durable foundation for navigating rapidly evolving AI compliance obligations.
