Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Agent Risk Manager Moves into Early Access

When we first introduced Agent Risk Manager, the response was clear: many security teams are actively looking for a way to secure the AI agents already running in their environment and how they can confidently adopt AI across their organization. AI agents now operate inside organizations with real access to email, files and business systems, often with little visibility for the teams responsible for securing them. That’s exactly the problem Agent Risk Manager was built to solve.

From Data Classification to Runtime Data Security for AI

Authentication used to be a login form. Then it became IAM: identity providers, roles, federation, lifecycle. Then it became Zero Trust: no built-in trust, every request checked in context. Each step did not replace the last so much as fold it into a bigger runtime decision. The login still happens, but it is now one input to a constant, context-based check.

AI Model Risk Intelligence Know Which Models You Can Trust Before You Deploy

When we started thinking about how to surface AI model risk inside Evo, the obvious answer was to borrow from how we score everything else: find the issue, assign a severity, surface it. Done. The core of the new approach is a real risk score, built the way security teams already reason about risk: Likelihood × Impact. Likelihood comes from Attack Success Rate (ASR), the share of real adversarial attacks that succeed against a model. Impact is how much damage the attacker's goal does when it lands.

Zenity Raises $125 Million to Secure the Era of Autonomous AI

A few years ago, when Michael and I started Zenity, most of the industry was not ready to hear what we believed. Software itself was changing. AI would let millions of people, not just engineers, build and automate real work. And securing that world would take a completely new approach, because we would no longer be protecting software. We would be protecting systems that think, decide, and act on their own.

A Safer Future with Agents

We built agents to act on their own. We're somehow surprised when they do. Two weeks ago, OpenAI ran a cyber eval with the model's guardrails turned down. The model got hyperfocused on solving the benchmark. So it broke out of its sandbox exploiting a zero-day in jFrog Artifactory, reached the open internet, exploited another zero-day to break into HuggingFace, all to steal the answers and cheat on the test.

The New Reach Security: Autonomous Security Control Assurance

AI-powered attacks, meet AI-powered defense. Reach Security's rebranded site is live today. Most of what changed came from customers. Security leaders have been telling us they need a faster, more continuous way to know where their controls are weak, understand what matters most, and close the gaps before attackers exploit them. Our new website reflects that signal. It brings greater clarity to the problem we solve, the category we are building, and how Reach helps security teams identify blind spots, prioritize action, guide remediation, and continuously validate the controls they already own.

Agentic Attacks Require Agentic Threat Prevention

AI-powered attacks are moving faster, adapting in seconds, and overwhelming traditional defenses with machine-speed activity. In this video, Jason Wright explains why security teams need Agentic Threat Defense built on customized predictions, automatic adaptation, and cloud-native scale. Watch how Cato Agentic Threat Prevention helps reduce the risk of AI-powered threats, stop adaptive attacks earlier, and scale prevention to stop agentic attacks.

Defending at machine speed: Predict, Adapt, Stop Agentic Attacks

AI-powered adversaries are accelerating vulnerability discovery and automating attacks. For security teams, the challenge is adaptive attack chains, machine-speed execution, and attack volumes beyond manual workflows. Cato is redefining prevention in the AI era—predicting enterprise-specific attack paths, adapting protections at machine speed, and scaling defense with cloud-native scale. This means enterprises can do more than just react to attacks, they can prevent them.

Behavior Intelligence for the Agentic Enterprise

The rise of AI agents is transforming the enterprise — and redefining insider risk. As organizations deploy AI agents alongside human employees, understanding behavior has become essential to detecting threats that traditional security approaches miss. Exabeam secures both human and AI agents with Behavior Intelligence, combining behavioral analytics and agent-powered security operations to reduce risk, accelerate threat detection, investigation, and response, and help organizations confidently secure the agentic enterprise.

Super Instinct Meets Super AI | Arctic Wolf Aurora

Attackers are using AI to move faster, scale broader, and automate attacks at machine speed, but no one wants fully autonomous AI making high-stakes decisions unchecked. There's a better way: Super Instinct meets Super AI. Meet the Aurora Agentic SOC, the world's largest commercial agentic SOC, built on the Aurora Superintelligence Platform. The completely new operating model pairs human instinct with AI-powered security operations to outperform human-only and AI-only approaches alike.