Noma Launches Agent Access Control for Enterprise AI Governance

Jun 2, 2026
Noma Security has introduced Noma Agent Access Control, a platform designed to help enterprises discover, manage, and enforce access policies for AI agents and Model Context Protocol servers.

Noma Security announced in a press release the launch of Noma Agent Access Control, a system designed to help enterprises discover, govern, and enforce access policies for AI agents and Model Context Protocol (MCP) servers.

The platform automatically builds a full inventory of agents and MCP servers within an organization, defining what each can access and maintaining a current view of the environment. This eliminates manual work and ensures complete visibility for security teams.

Key features include an enterprise agent registry, agent identity management, a flexible governance model, tool-level control, and runtime enforcement. The system provides the first layer of governance by defining boundaries, while Noma's AI Detection and Response platform offers runtime verification to ensure those boundaries hold.

Noma Security describes the new product as part of its broader effort to secure AI systems by detecting behavioral threats and protecting agents across enterprise environments.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Cybersecurity AI Weekly, Enterprise AI Brief or Daily AI Brief.

Also, consider following us on social media:

Cybersecurity AI Enterprise AI AI Brief AI Brief (X)
Free newsletter

Cybersecurity AI Weekly

Weekly newsletter about AI in Cybersecurity.

Guide
Contract Lifecycle Management with AI

Contract Lifecycle Management with AI

Platforms where the assistant comes attached to the repository, the workflow and the approvals

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.