Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Zenity Labs is publicly launching AI Total: a free service that runs an AI agent skill in a sandbox and tells you what it actually did, before you let it near your agents.

Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know Many conversations about AI agent risk over the past year start from the same unspoken assumption: something bad happened because someone or something manipulated the agent. A hidden instruction in a document, a poisoned prompt, an adversary steering the model toward an action it shouldn't take. That's a real category of risk, and it deserves the attention it's getting.

Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know.

Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know Healthcare, as an industry vertical, is moving faster on agentic AI than it has in past technology evolutions. Some reports say it is outpacing other regulated industries. Ambient scribes are documenting patient visits in real time. Prior-authorization and revenue-cycle agents are handling payer workflows that used to require staff to log into multiple systems manually.

Zenity has been named a Market Shaper in Gartner's inaugural Emerging Market Quadrant for AI Application Security — Startup Vendors, a new report evaluating vendors on their ability to secure AI agents across the full agent lifecycle.

Enterprises aren't standardizing on one AI agent platform. Security teams are watching Copilot run alongside ChatGPT Enterprise, homegrown agents built on internal frameworks, and endpoint coding agents like Claude and Codex, often all inside the same organization. Each platform brings its own credentials, tool access, and blind spots, and none of them wait for a security review before taking an action.

Ask ten people what "AI regulation" means, and you'll get ten different answers, and most of them will assume the others are talking about the same thing. They're not. "Regulate AI" has become a catch-all phrase covering several genuinely distinct regulatory questions, each with its own goal, its own toolkit, and its own plausible answer, bundled together so tightly that arguing about one gets mistaken for arguing about all of them.

Security teams evaluating an AI agent security platform tend to ask the same question after the first demo: will this keep up? Agentic AI changes shape every few weeks, with new frameworks, new coding agents, and new ways for an agent to reach a tool or a credential. A platform that covers today's stack and stalls on next quarter's isn't much of a bet.

AI agents are moving into production faster than security teams can govern them. And unlike traditional applications, agents continuously make decisions, invoke tools, access data, and take actions. Every one of those interactions creates security context that needs to be understood. At enterprise scale, asking analysts to manually evaluate every finding becomes impossible.

Cursor has become one of the primary AI coding environments for development teams, and its agents increasingly reach into the outside world through MCP servers: databases, ticketing systems, cloud consoles, and internal APIs. Every connection extends what an agent can do. It also extends what could go wrong if that access goes unmonitored or unchecked.