arXiv:2606.19464v1 Announce Type: new Abstract: Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance. This includes specifying what agents are permitted and prohibited from doing, what they areobliged to do after certain actions (e.g., notify the CISO), under wha
The proliferation of advanced autonomous AI agents, particularly those driven by LLMs, necessitates urgent development of governance frameworks to manage their capabilities and potential risks.
As AI agents gain power to act across organizational boundaries, establishing clear deontic policies is critical for ensuring security, compliance, and responsible deployment within enterprise environments.
The focus is shifting from basic authentication and access control to comprehensive runtime governance, requiring a new class of policies that define permissions, prohibitions, and obligations for AI agents.
- · Cybersecurity firms specializing in AI governance
- · Enterprises adopting AI agents securely
- · AI platform providers integrating governance tools
- · Organizations deploying AI agents without strong governance
- · Traditional security vendors slow to adapt to agentic AI
- · Users impacted by uncontrolled AI agent behavior
Increased investment and research into AI safety and governance solutions.
Development of new regulatory standards and compliance requirements specifically for autonomous AI systems.
The emergence of 'AI compliance as a service' as a significant new market segment.
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Read at arXiv cs.AI