Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI Governance Tools and the Audit Trail Problem

An AI governance platform demo shows you the present. Compliance posture at seventy-nine percent, four controls needing attention, a register of systems with owners attached. Every figure describes today, and the demo is persuasive precisely because today is legible. ‍ An audit asks a different question.

OpenTelemetry and AI Governance: Where the Standard Stops

OpenTelemetry graduated from the Cloud Native Computing Foundation in May 2026, which formally settled a question the industry had answered informally years earlier. It is the standard way applications emit telemetry, second only to Kubernetes in contributor volume, and native across every major observability backend. ‍ A security and governance company has a specific reason to care.

AI Security Policy in Practice: How to Define What AI Can and Cannot Do

Most organizations that try to write an AI security policy start with two lists. Approved tools and banned tools. But, that list is inevitably out of date within a month. Employees adopt AI features embedded in everyday software faster than any review board can evaluate them, and a blanket ban does not stop the behavior, instead it pushes people toward personal accounts and unmanaged services.

Uncovering Shadow AI Before It Becomes Your Biggest Risk | WatchGuard Technologies Webinar Series

Your employees have already adopted AI. The question is whether your organization knows where, how, and with which data. While many companies are encouraging AI innovation, a new security challenge is emerging in parallel: Shadow AI. Employees are connecting AI tools faster than security teams can evaluate them, moving sensitive data into unmanaged applications, and creating blind spots that traditional security controls were never designed to see. Even organizations with mature AI strategies are discovering that sanctioned AI is only part of the story.

Why AI Discovery Must Be the First Step in Enterprise AI Security

Every enterprise security leader is being asked the same question by their board: are we secure against AI risk? Most cannot answer it with confidence, and the reason is rarely a lack of tools. It is a lack of visibility. AI has spread through enterprises faster than almost any technology before it. Developers wire large language model APIs into internal tools. Business teams stand up copilots and chat assistants. Data science teams build retrieval pipelines against customer and financial data.

Secure all your internal vibe-coded applications - in one click

AI has enabled employees across every team to build applications faster than ever before. But that speed is also what's keeping every CISO up at night: any employee can build an application, deploy it to the public Internet, and accidentally expose internal work or company data. Today, we're launching new tools to make it easy to keep your applications hosted on Workers private.

How Cloudflare detects MCP traffic and helps secure it

Most companies designed their resource permissions with a human user in mind. A senior engineer may be able to deploy to production, query a sensitive database, or revoke another user's access. Those privileges come with risk, but that risk has traditionally been bounded by two assumptions: the engineer will use human judgment, and the engineer can only act at human speed.

Unmasking the Invisible: How to Identify AI Tools, Hidden Devices, and Other Unknown Assets on Your Network

You cannot protect what you do not know exists. That statement has been true throughout the history of cybersecurity, but it has become even more important as organizations adopt new technologies and employees gain access to powerful AI tools. While many organizations focus on defending against external threats, they often overlook a growing problem much closer to home: devices, applications, and services operating within their environment that nobody knows about.