Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

I’ve been on enough middle-of-the-night incident calls to know what separates the organizations that recover quickly from the ones that don’t. It’s rarely the size of the security budget. It’s whether leadership asks the right questions in the first hour, and then lets the team work.

Agentic AI security is a new budget line for most organizations, which means security buyers across EMEA often face the dual challenge of evaluating solutions and justifying the spend at the same time. For organizations operating within the EU AI Act, GDPR, NIS2, and DORA, the business case has a dimension that doesn't exist in less regulated markets: the cost of non-compliance is not a vague, abstract risk.

Cohere's researchers caught Qwen3.8-Flash-Next, Alibaba's August preview of its Qwen4 architecture, attempting an unrequested upload during a routine security task. They were running it through CTI-REALM, Microsoft's benchmark for agents doing a security analyst's job. The task: write a rule that detects curl downloading a bash script to /tmp and executing it.

Every security team I talk to has some version of the same document. It might be called an “AI Acceptable Use Policy” or “Agent Governance Standards.” It usually lives in Confluence or SharePoint. It says sensible things: coding agents shouldn’t touch production credentials, assistants shouldn’t email sensitive data outside the company, and nobody should connect an unvetted MCP server to an agent with access to customer records.

An AI innovation team is asked to build a customer support assistant in two weeks. The demo works, employees start using it, and leadership wants to put it in front of customers. Somewhere between the prototype and the rollout, the team has created a new data table, an API endpoint, and a scheduled job. The project has a sponsor. Does every piece across the customer support assistant have an owner?

Security investment decisions are always, at some level, conversations about risk quantification: how much exposure does the organization actually carry, what does it cost to reduce that exposure, and what's the cost of not acting at all. For agentic AI security, these questions are answerable once the right measurement is in place.

As AI agents operate with increasing autonomy in enterprise production, the sheer volume of security telemetry they generate creates an unprecedented scaling challenge. Human oversight remains required in many enterprises, while legacy triage tools simply can't keep up. To help security teams cut through the noise, Zenity’s AI group built the Blue Agent.

Anthropic's Inference Hooks update lets security teams validate Claude's tool calls before they run. Zenity supports it across Claude's surfaces, so teams can approve write-capable connectors, Model Context Protocol (MCP) servers, and wider Claude Code rollouts with policy on each action.

The agentic AI security conversation has, for good reason, spent most of its energy on the agents an organization builds or deploys directly: what they're authorized to do, what data they can reach, how their behavior is monitored. I think that focus is necessary and also increasingly incomplete, because the fastest-growing risk surface in agentic AI right now isn't the agents themselves.

Japan’s Ministry of Finance reports that AI adoption in large local enterprises has surged to 89%. Companies are progressively choosing to incorporate AI agents into their workflows, building with Microsoft Copilot Studio, ChatGPT Enterprise, Claude, and a myriad of other platforms, all living across a variety of environments with autonomy, privilege, and minimal supervision.