Autonomous governance integrating agentic AI and zero trust for intelligent cybersecurity in distributed enterprise ecosystems

Abstract The fast adoption of enterprise infrastructures into distributed, cloud-native, and API-based ecosystems has presented sophisticated security requirements that cannot be effectively handled with more traditional frameworks based on perimeters and rules. The growing number of multi-cloud ecosystems, micro services, Internet of Things (IoT) gadgets, and AI-to-AI interactions have broadened the attack surface, creating the need to deploy more adaptive and intelligent security measures. This paper is a complete framework of autonomous security by uniting Zero Trust Architecture (ZTA) and Agentic Artificial Intelligence (AI) to provide context-sensitive and real-time threat detection and response. The offered Autonomous Governance Framework (AGF) takes advantage of multi-layered architecture that includes data acquisition, behavioral analytics, decision intelligence, enforcement, and feedback learning in order to provide constant monitoring and adjusting policymaking. The framework facilitates moving risk scoring dynamically and autonomous decision making by using generative AI to predictively model threats and machine learning-based behavioral analytics. One of the main contributions of this work is the idea of self-healing identity perimeters, in which credentials are automatically revoked and reissued according to a risk assessment based on context, thus limiting the use of compromised credentials. Moreover, the framework incorporates automated orchestration and real-time Data Loss Prevention (DLP) to enhance the protection of sensitive data in distributed environments. Context-aware API security provides a controlled access to data flows and is also consistent with the principle of Zero Trust that constantly verifies. Implementation considerations in multi-cloud and enterprise systems are also discussed in the paper emphasizing scalability, performance, and interoperability issues.