Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

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.

The June 2, 2026 White House Executive Order on Promoting Advanced Artificial Intelligence Innovation and Security has officially set the clock ticking. With mandates requiring CISA and federal agencies to deploy AI-enabled defensive tools on civilian systems, the directive for Federal CIOs, CISOs, and SOC Directors is clear: AI adoption is an operational requirement to effectively defend federal networks.

The Remediation Agent and Malicious Code Defense are the first two pieces of Evo Agentic AppSec: security that not only surfaces risk, but resolves it and prevents the next ones. This morning, we announced the broadest expansion of the Snyk AI Security Platform to date: discover, remediate, validate, and prevent. A loop with a missing segment is not a loop; it is a gap that an autonomous attacker will occupy. Evo Continuous Offensive Security closes validation and shipped today.

AI is quickly becoming one of the most useful tools available to security researchers. Its ability to analyze enormous volumes of data, identify vulnerabilities, reconstruct attacks, connect seemingly unrelated signals, and help defenders respond faster than humans could alone is incredibly beneficial.

AI agent sprawl is the uncontrolled proliferation of AI agents, autonomous assistants, and LLM-powered tools across an organization without centralized tracking or governance. It mirrors historical IT challenges like SaaS sprawl and shadow IT, and it emerges when decentralized business units build or deploy agents independently, without coordinated oversight from security, IT, or risk teams. ‍ The difference is that these agents are active software actors.

Governing AI agents at enterprise scale requires a fundamental change in how security, risk, and compliance teams think about AI oversight. The generative AI era focused governance on output quality: what the model says, what it produces, and whether the content meets policy standards. ‍ The agentic era demands governance of action and delegated authority: what the AI is allowed to do, what systems it can touch, and how its decisions trace back to human accountability.

Aaron Beardslee, Security Researcher, Securonix Threat Labs As many of you have heard, OpenAI was running a cyber-capability evaluation against advanced models, including GPT-5.6 Sol and a more capable pre-release model with reduced cyber refusals. The environment was meant to be constrained and the model still brute forced through it.

You’re collecting more telemetry than ever, but chances are it hasn’t made your job easier. Fragmented data, disconnected tools, and manual investigations mean more noise, slower response times, and higher operational costs. The promise of AI is that it will solve it, but in most cases, you just end up trading noise for expensive hallucinations. Point an LLM at raw, unorganized log storage, and you get an assistant that burns through credits to produce generic, unreliable answers.