Why your SOC's new AI agent might be a malicious actor in disguise (www.techradar.com)

Security teams and managed providers are increasingly adopting multi-agent systems (MAS)—collections of autonomous AI agents—to scale threat detection and incident response, but TechRadarPro warns these systems can become liabilities if poorly designed. The article highlights concrete risks: agent hallucinations that confidently produce wrong conclusions, semantic misalignment across data sources (SIEM, EDR, cloud identity), race conditions and bottlenecks from inadequate communication protocols, and a widened attack surface where compromised agents act as insider threats. As agent counts grow, interactions multiply exponentially, making orchestration, state management, dynamic load balancing and fault tolerance critical technical challenges. To make MAS safe and effective, the piece recommends engineering and governance controls: robust inter-agent communication and normalization layers, policy-driven autonomy with confidence thresholds and human-in-the-loop escalation, grounding techniques and cross-agent validation to prevent hallucinations, encrypted channels, strict access controls and per-agent audit logs, and privacy-by-design for regulatory compliance. It also urges explainability (reasoning chains), continuous feedback loops for learning from human validation, and ethical AI frameworks. The takeaway: MAS can transform SecOps from alert triage to autonomous resolution, but only if teams build for interoperability, trust, resilience and verifiable decision-making—otherwise an “AI agent” could behave more like a malicious actor than an ally.