Managing Security Risks of AI Agents in Adversarial Contexts: A Conceptual Integration of the CIA Triad and Organisational Resilience for Digital Governance
Year: 2026
Type: article
Abstract: Artificial intelligence (AI) agents are increasingly deployed across organisational environments, introducing not only efficiency gains but also complex security and governance challenges. This paper explores how the integration of technical and managerial frameworks can enhance the resilience of organisations operating under adversarial conditions. Building on a conceptual and narrative review, the study synthesises cybersecurity principles represented by the CIA triad (Confidentiality, Integrity, Availability) with the organisational resilience model outlined in ISO 22316:2017. The proposed conceptual integration demonstrates how resilience principles of anticipation, adaptation, and recovery complement traditional security controls, transforming AI agent protection into a strategic capability that supports digital transformation and continuity management. The research question guiding this study is: How can organisations enhance resilience and manage security risks arising from the deployment of autonomous AI agents in adversarial environments? This paper argues that integrating resilience principles into AI agent governance strengthens organisational security, operational continuity, and adaptive capacity under adversarial conditions. This interdisciplinary approach extends the discourse beyond technical cybersecurity, positioning AI agent safety within the broader domains of digital governance, management, and economics. The study contributes a novel conceptual framework and identifies strategic implications for policy making, innovation management, and sustainable digital ecosystems.
Authors: Krystian Bień, Elwira Pyk, Mariusz Rafało
Institutions: Kozminski University, SGH Warsaw School of Economics
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $69
OpenAlex ID: W7154681867
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