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AgenticCyOps is introduced as a security framework for multi-agent systems in enterprise cyber operations, addressing vulnerabilities arising from autonomous agent control over tools, memory, and communication. The framework decomposes attack surfaces across component, coordination, and protocol layers, identifying tool orchestration and memory management as primary trust boundaries. By implementing five defensive principles—authorized interfaces, capability scoping, verified execution, memory integrity & synchronization, and access-controlled data isolation—AgenticCyOps achieves defense-in-depth, reduces exploitable trust boundaries by at least 72%, and intercepts most attack chains early in the process.
Securing enterprise multi-agent systems boils down to rigorously controlling tool orchestration and memory management, which can slash exploitable trust boundaries by over 70%.
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.
