Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The Homegrown Agent Maturity Model for Execution & Risk (HAMMER): is a five-level framework with seven domains for security teams to locate where they stand on securing homegrown agents, then make the case for what they need to advance. The most powerful AI agents in your enterprise may be ones you never bought. Your own teams are building them by wiring models, tools, and data into software that acts on its own, often with little security approval or oversight.

I have lived through major incidents firsthand, including the urgent calls that come in the middle of the night. In my experience, the teams that contain damage quickly are not the ones with the most tools. They’re the ones that ask the right questions in the first hour.

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.

Zenity pioneered AI Agent Security because we recognized early that agents would require a different approach to protection. Software that can access sensitive data, use tools, and creator credentials to take action on their behalf requires security that understands what the agents can do and how they actually behave. Today, Zenity is joining as a founding member of the ServiceNow Control Tower Alliance.

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.

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.

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.