AgenticCyOps: Securing Multi-Agentic AI Integration in Enterprise Cyber Operations

• URL: http://arxiv.org/abs/2603.09134v1
• Date: Tue, 10 Mar 2026 03:15:36 GMT
• Title: AgenticCyOps: Securing Multi-Agentic AI Integration in Enterprise Cyber Operations
• Abstract summary: Multi-agent systems (MAS) promise adaptive, reasoning-driven enterprise, yet granting agents autonomous control over tools, memory, and communication introduces attack surfaces absent from deterministic pipelines.<n>We introduce AgenticCyOps, a framework built on a systematic decomposition of attack surfaces across component, coordination, and protocol layers.<n>We apply the framework to a Security Operations Center (SOC) workflow, adopting the Model Context Protocol (MCP) as the structural basis.
• Score: 7.660803748903973
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-agent systems (MAS) powered by LLMs promise adaptive, reasoning-driven enterprise workflows, yet granting agents autonomous control over tools, memory, and communication introduces attack surfaces absent from deterministic pipelines. While current research largely addresses prompt-level exploits and narrow individual vectors, it lacks a holistic architectural model for enterprise-grade security. We introduce AgenticCyOps (Securing Multi-Agentic AI Integration in Enterprise Cyber Operations), a framework built on a systematic decomposition of attack surfaces across component, coordination, and protocol layers, revealing that documented vectors consistently trace back to two integration surfaces: tool orchestration and memory management. Building on this observation, we formalize these integration surfaces as primary trust boundaries and define five defensive principles: authorized interfaces, capability scoping, verified execution, memory integrity & synchronization, and access-controlled data isolation; each aligned with established compliance standards (NIST, ISO 27001, GDPR, EU AI Act). We apply the framework to a Security Operations Center (SOC) workflow, adopting the Model Context Protocol (MCP) as the structural basis, with phase-scoped agents, consensus validation loops, and per-organization memory boundaries. Coverage analysis, attack path tracing, and trust boundary assessment confirm that the design addresses the documented attack vectors with defense-in-depth, intercepts three of four representative attack chains within the first two steps, and reduces exploitable trust boundaries by a minimum of 72% compared to a flat MAS, positioning AgenticCyOps as a foundation for securing enterprise-grade integration.

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