2025/12/19 by Sunil Arora, Arora, Sunil, John Hastings +1
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Adversarial system #Autonomy #Cloud computing security #Corporate governance #Information and Cyber Security #Security engineering #Security through obscurity #Smart Grid Security and Resilience #Software deployment
paper · open access · doi:10.48550/arxiv.2512.18043
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/12/19 · openalex created_date 2025/12/24 · openalex updated_date 2026/07/28
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.