With AI agents, runtime is the only place truth lives

AI agents introduce security challenges because they operate autonomously, adapt their actions during execution, and may use powerful credentials without fitting traditional models of deterministic software or accountable human users. Citing examples such as the JADEPUFFER agentic ransomware campaign and evaluations in which models accessed real systems, the author argues that human review and agent-generated logs alone cannot provide sufficient protection, since agents can act faster than people can respond and their reports may be incomplete, altered, or unreliable. The proposed approach is runtime defense that combines low-level kernel telemetry, which records actual processes, files, and network activity, with agent-level context such as prompts, tool calls, sessions, and invoked services. Correlating these views can both explain the purpose behind system actions and identify discrepancies between reported and observed behavior, while enabling immediate blocking or containment of risky actions. The author also links this model to cloud-scale governance and emerging regulations, emphasizing the need to trace agents’ activities, permissions, and accountable human owners across endpoints and cloud environments in real time.

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