Securing Autonomous AI Agents: A Reference Architecture for Data Protection in the Agentic AI Era

Problem. Agentic Artificial Intelligence (autonomous systems with persistent memory, multi-step reasoning, and real-world tool invocation) has redefined the data-protection threat surface. Unlike stateless Large Language Models, agentic systems independently access, transform, and can exfiltrate data across distributed infrastructures, exposing novel risks from prompt injection, memory poisoning, tool misuse, shadow-agent emergence, and cross-layer threat propagation. Approach. This paper develops a cross-layer reference architecture for secure autonomous data protection by synthesizing recent agent-specific contributions with foundational protection literature. Architecture. Five integrated layers are proposed: cryptographic identity and delegation; sandboxed execution; policy enforcement and data mediation; observability and audit; and lifecycle governance. Each layer is anchored to specific provisions of the EU AI Act and the NIST AI Risk Management Framework, and is mapped to representative research including SAGA (Syros et al., 2025), the Caging the Agents healthcare deployment (Maiti, 2026), TRiSM (Raza et al., 2026), and enterprise designs (Bokka, 2025; Kolli et al., 2024). Findings. The paper identifies persistent gaps in memory integrity, federated revocation, post-quantum identity, and benchmarking under non-deterministic execution, and offers a forward-looking research agenda for trustworthy, accountable, and privacy-preserving agentic deployments.