Sarcouncil Journal of Engineering and Computer Sciences

Abstract: State-of-the-art data protection for modern enterprise environments goes beyond static encryption and access control mechanisms, demanding continuous intelligence and adaptive decision-making capabilities. This article discusses how the enterprise architect can design an Agentic AI ecosystem that autonomously discovers, protects, and governs data across distributed platforms spanning hybrid and multi-cloud infrastructures. The proposed architecture introduces a layered framework of agents: discovery agents map sensitive assets and establish comprehensive data lineage; policy agents interpret regulatory intents from frameworks such as GDPR, HIPAA, and PCI DSS; and execution agents, guided by contextual risk assessments, apply targeted encryption, masking, and tokenization accordingly. It utilizes enterprise-class services at Microsoft Azure for Cognitive Search, Purview, OpenAI, Logic Apps, Functions, Key Vault, and Defender for Cloud to instantiate the agentic architecture in real-world production settings. Next, empirical validation of its use was performed by conducting a pilot at a banking institution, which demonstrated significant improvements in operational metrics, including a reduction in the volume of compliance tickets, enhanced capabilities in incident response, and improvements in audit-readiness metrics. Continuous evolution is enabled by the system's reinforcement learning, eliminating the need for reconfiguration in response to emerging threats or regulatory changes. The architecture emphasizes explainability, generation of audit trails, and adherence to zero-trust security principles for the sake of transparency in automated decision-making.