Securing Agentic AI Systems -- A Multilayer Security Framework
Sunil Arora, John Hastings
TL;DR
The paper identifies security and governance gaps for agentic AI systems and proposes MAAIS, a seven-layer, lifecycle-aware security framework extending CIAA to include accountability. Methodologically, it combines a design science approach with a systematic literature review and validates the framework by mapping its controls to the MITRE ATLAS adversarial threat landscape. The key contribution is a practical, defense-in-depth artifact spanning infrastructure, data, models, execution, governance, access, and monitoring to secure agentic AI in enterprise contexts. This work enables enterprise CISOs and security teams to deploy agentic AI with structured controls, ongoing governance, and measurable alignment to recognized threat frameworks, while outlining future standardization and regulatory mapping efforts.
Abstract
Securing Agentic Artificial Intelligence (AI) systems requires addressing the complex cyber risks introduced by autonomous, decision-making, and adaptive behaviors. Agentic AI systems are increasingly deployed across industries, organizations, and critical sectors such as cybersecurity, finance, and healthcare. However, their autonomy introduces unique security challenges, including unauthorized actions, adversarial manipulation, and dynamic environmental interactions. Existing AI security frameworks do not adequately address these challenges or the unique nuances of agentic AI. This research develops a lifecycle-aware security framework specifically designed for agentic AI systems using the Design Science Research (DSR) methodology. The paper introduces MAAIS, an agentic security framework, and the agentic AI CIAA (Confidentiality, Integrity, Availability, and Accountability) concept. MAAIS integrates multiple defense layers to maintain CIAA across the AI lifecycle. Framework validation is conducted by mapping with the established MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) AI tactics. The study contributes a structured, standardized, and framework-based approach for the secure deployment and governance of agentic AI in enterprise environments. This framework is intended for enterprise CISOs, security, AI platform, and engineering teams and offers a detailed step-by-step approach to securing agentic AI workloads.