Securing Agentic AI Systems -- A Multilayer Security Framework

TL;DR

This paper presents MAAIS, a comprehensive security framework for agentic AI systems that addresses their unique cyber risks through a layered, lifecycle-aware approach validated against established threat models.

Contribution

It introduces a novel, structured security framework specifically designed for agentic AI, filling gaps left by existing security approaches.

Findings

MAAIS effectively maps to MITRE ATLAS threat tactics.

The framework provides a standardized approach for secure deployment.

Validation demonstrates comprehensive coverage of agentic AI security challenges.

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.