The future of governing AI agents

The rapid advancement of autonomous security agents in the industry has outpaced the development of governance frameworks, creating a gap that must be addressed to ensure accountability and compliance with emerging regulations such as ISO 42001, DORA, NIS2, and the EU AI Act. These agents perform a variety of security tasks, but the challenge remains to verify their effectiveness and ensure human oversight. Current practices often adopt a tiered autonomy model based on risk levels, but this doesn't guarantee quality performance. A proposed solution involves a four-layer architecture separating skills, reasoning, models, and context to better manage and evaluate agent reasoning, ensuring consistent and explainable decision-making. This framework allows for model flexibility and portability across providers, reducing concentration risk and maintaining trust evidence. Progressive trust is emphasized, whereby oversight is adjusted based on accumulated evidence of agent reliability. Additionally, the importance of monitoring agent reasoning and the need for a robust observability platform is highlighted, as demonstrated by Uber's Agentic Detection and Response system. Elastic Security is presented as a solution capable of supporting the governance of autonomous agents, offering integrated capabilities for telemetry ingestion, storage, and quality checks, all critical for ensuring compliance and operational reliability in the face of evolving regulatory landscapes.

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