Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Japan’s Ministry of Finance reports that AI adoption in large local enterprises has surged to 89%. Companies are progressively choosing to incorporate AI agents into their workflows, building with Microsoft Copilot Studio, ChatGPT Enterprise, Claude, and a myriad of other platforms, all living across a variety of environments with autonomy, privilege, and minimal supervision.

Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know Your team just hired someone who never sleeps, reads everything, and takes work from whoever asks. That’s the elevator pitch for Claude Tag, and on the face of it, it’s hard to see why anyone would push back on the new hire. Enterprises will want this in every workspace.

Most of the industry discourse on agentic AI risk has settled into a comfortable framing: agents get attacked the way models get attacked, through some form of prompt manipulation, and the fix is a better guardrail. I think that framing is dangerously incomplete, and I want to walk through five specific scenarios that make the case directly rather than abstractly. Two of them are manipulation. One exploits trust between agents rather than any single agent's behavior.

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.