Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Today we’re excited to announce that Zenity now protects the most powerful, enterprise-critical coding assistants - Cursor, Claude Code, and GitHub Copilot - from build-time to runtime. As AI becomes a first-class developer tool, Zenity gives security teams the visibility and control they need to safely embrace coding assistants everywhere they’re used, in IDEs, CLIs or in the cloud.

Security teams are entering a new phase of risk driven by the combination of AI agents and broad access to internal and external data. Agents are no longer limited to responding to prompts. They read files, pull documents from shared repositories, query external sources, and move information across systems on behalf of users. This shift brings real business value. Knowledge becomes easier to access, workflows move faster, and information that once required deliberate effort can be surfaced instantly.

In the first round of the AI gold rush, most conversations about AI security centered on models: large language models, training data, hallucinations, and prompt safety. That focus made sense when AI was largely confined to generating text, images, or recommendations. But that era is already giving way to something far more consequential.

Late last week, GreyNoise published one of the clearest signals we have seen that AI systems are no longer just research targets. They are operational targets. Their honeypot infrastructure captured 91,403 attack sessions between October 2025 and January 2026, revealing two distinct campaigns systematically mapping AI deployments at scale. This is a meaningful inflection point.

MITRE ATLAS has become a critical resource for cybersecurity leaders navigating the rapidly evolving world of AI-enabled systems.Traditional threat models are built for human-initiated workflows, APIs, and infrastructure, so they are no longer sufficient to describe modern AI attacks..

MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a globally recognized AI security framework that catalogs adversarial techniques targeting artificial intelligence systems. Similar in structure to MITRE ATT&CK but purpose-built for AI, machine learning, and agentic systems, ATLAS translates abstract AI risks into concrete, actionable attack techniques that security teams can monitor and mitigate.

AI agents are now acting across SaaS, cloud, and endpoint environments with identities and permissions that traditional controls cannot fully govern. Most enterprises already have more agents in production than they realize, many created without security review. Across the industry, the research aligns.

For security teams, the adoption of agents showed up operationally before it showed up strategically - creating new expectations and requirements. Risk is no longer tied to prompts or the model alone. It shows up in what agents do once they are connected to critical systems - coming from permissions they inherit, tools they invoke, and data they move.

The OWASP GenAI Security Project has officially released its Top 10 for Agentic Applications, the first industry-standard framework focused on the operational risks created by autonomous and semi-autonomous AI systems. AI has evolved in a way that directly changes how enterprises need to think about security. We started with machine learning systems designed to classify and predict.

Data is never the problem. Security teams rarely complain about having too much of it. The real danger comes from data that sits unconnected and unexplained. What teams actually need is data that is actionable and converges into meaning. Data that cuts deeper than surface level signals. Data that reveals what is unfolding and what needs to happen next.