An Organization-Scoped LLM Agent Runtime Architecture for Regulated Cybersecurity Operations
arXiv:2605.30604v1 Announce Type: cross Abstract: Regulated cybersecurity workflows lack a runtime substrate that enforces organization-level scope across retrieval, tool calls, memory, findings, reports, and audit while remaining model-agnostic and locally deployable. Recent large language model (LLM) agent systems report strong results on isolated cybersecurity tasks, yet they do not by themselves define an auditable platform architecture for regulated security operations centre (SOC) and compliance workflows, where a single analyst may trigger actions that bind the organization, and where t
The rapid acceleration in LLM agent capabilities and the increasing pressure for robust, auditable cybersecurity solutions in regulated environments converge to make this research timely.
This development addresses a critical gap in deploying advanced AI agents within sensitive security operations, offering a pathway for regulated industries to adopt LLM technology safely.
The proposed architecture provides a framework for integrating LLM agents into highly regulated cybersecurity workflows, ensuring compliance and accountability, which was previously a barrier.
- · Regulated cybersecurity firms
- · Organizations with SOCs
- · AI agent developers specializing in security
- · Compliance software providers
- · Cybersecurity consultancies reliant on manual audit processes
- · Legacy security software without AI integration
- · Companies unable to adapt to new regulatory AI requirements
Regulated industries gain the ability to leverage AI agents for enhanced cybersecurity postures.
This framework could become a standard for auditable AI deployments across various regulated sectors, beyond just cybersecurity.
Increased adoption of organization-scoped AI agents might lead to new regulatory bodies focused on AI accountability and auditing standards.
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Read at arXiv cs.AI