Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

An agent’s logs are written by the agent. Every framework log, trace span and tool-call record that an agentic AI security platform ingests comes from the process being watched, or from a gateway that sees only what that process routes through it. When a trusted prompt coerces an agent into misusing a permission it already holds, the record shows a normal tool call, because from inside the process it was one. Running more analysis on that record returns the same answer faster.

The agent that worries you is authorized. It holds a service account you provisioned, calls tools you approved, and reaches destinations someone signed off on. Zero trust asks two questions at every decision point, who is this and what are they allowed to do, and an agent redirected by trusted input answers both correctly every time. For a person those questions fire at a session boundary, where context gets re-checked. An agent on Kubernetes has no such boundary.

The board wants to know which AI agent risk to fund first, and a likelihood score cannot answer it. No incident survey gives base rates for agents on your architecture, and an agent can take a different path on the same input. What a CISO can estimate is what each risk would cost to recover from: the work to detect it, scope it, revoke the authority it used, and prove what happened. Ranked on that cost, unexpected code execution drops toward the bottom.

NIST has published no AI agent authorization standard, and nothing in its February 2026 draft gives an auditor a control to test. What the NCCoE did publish is more useful to a CISO with an audit on the calendar: five areas of interest that read like an assessor’s question list. Together they ask which agent acted, on whose behalf, under what authority, and with what record. A Kubernetes cluster has a primitive it can point to for each area.

Databricks has become the data and AI platform of record for a large and growing share of the enterprise market. Unity Catalog gives teams unified governance over data assets, AI models, and agentic workflows, controlling what exists, who can use it, and what policies apply.

The alerts come in at 3 AM. You've been awake for a full day and into the night, and there are critical questions you have to answer: Is this our problem, or someone else’s? A routing change that went sideways or lateral movement? A misconfigured ACL or an intrusion in progress? The questions get answered as quickly as possible by disconnected dashboards and human-error-prone manual processes. Before anyone else is awake to check the work.

AWS Dogwood brings stateful authorization to AI agents. Learn how it fits with workload identity, AuthZEN, IAM, and the move away from long-lived credentials.

If you run CertKit across multiple collections, September was your month. The new home page lets you search across all of them, the new dashboard surfaces the context you need, and a read-only API pulls it all into your external tools.