Defending AI Sentinels: A Multi-Layered Runtime Security Architecture for Generative AI in AIOps - Ayobami E. Mesioye; Oladapo O. Adeduro; Johnson B. Oluwagbemi (Methods in Science and Technology Studies, 2026)
The rapid integration of Generative AI into Automated IT Operations (AIOps) has introduced "AI Sentinels", an autonomous agents capable of managing critical infrastructure. However, these systems introduce a novel attack surface evidenced in inference-time adversarial manipulations such as prompt injection and jailbreaking. While existing security paradigms protect network perimeters, they fail to safeguard the internal logic of AI agents, creating a research gap in runtime defense for autonomous infrastructure controllers. This study aims to develop a multi-layered, defense-in-depth architecture to neutralize these threats. The proposed system integrates three layers: an Intent Validation Engine (Layer 1) using semantic analysis, a Secure Sandbox (Layer 2) utilizing eBPF-based kernel monitoring within a digital twin, and a Static Analysis module (Layer 3) for infrastructure-as-code (IaC) compliance. Key findings indicate that while single-layer defenses achieve an Adversarial Success Rate (ASR) of 32–68%, the proposed multi-layered approach reduces the ASR to near-zero (0.2% in robust testing), maintaining an F1-score of 0.990. Despite the complexity of the pipeline, the system achieves a mean operational latency of 48.2ms on enterprise-grade hardware (NVIDIA A100). These implications suggest that runtime behavioral verification is essential for the safe deployment of LLMs in privileged environments, providing a foundational framework for resilient AIOps.