Picture this. You’ve spent three weeks building the case for an AI agent inside your company. The demo worked. The ROI math checks out. You rehearsed the presentation with your manager. You walk into the risk review meeting and hear: “This needs more analysis.” Again. You smile, pack up your laptop, and spend the elevator ride down trying to figure out what “more analysis” actually means.
That’s two years of u/Tall_Ad4729’s life right there. Not because the tech was bad. Not because budgets were tight. Risk teams had no real framework for evaluating what happens when AI starts making decisions without a human watching. The questions they were asking were vague. The answers they were getting were vaguer. So they just kept saying no. 🔍
Last week the Five Eyes alliance (US, UK, Canada, Australia, New Zealand) dropped official guidance on agentic AI risk. Someone on Reddit turned it into a prompt. And for the first time, a proposal got through initial review without getting sent back. One structured document. No surprise objections. No third request for “additional documentation.”
🛡️ Why this matters right now
A chatbot is one thing. An agent is different. It takes actions: querying your CRM, drafting emails, triggering workflows, updating records, calling external APIs. It operates across time, not just inside a single conversation. When something goes sideways, “the AI did it” is not something your compliance team accepts. Someone signed off on deploying it. That someone is you.
The Five Eyes guidance identifies five risk categories: privilege escalation, design flaws, behavioral drift, structural weaknesses, and accountability gaps. These are exactly the questions a risk team asks, often in different words and in a different order every time. Privilege escalation means your agent could end up with more access than it started with. Behavioral drift means it might do things slightly differently three months from now than it does today. Accountability gaps mean nobody can clearly answer who is responsible when the agent does something wrong.
These aren’t hypothetical problems. They’re the ones that kill deployments after months of work. This prompt makes you answer them before the meeting, not during it. And answering them in writing, in advance, signals something most risk teams rarely see: that you thought about failure before you asked for approval.
⚙️ How to use it
Paste the prompt into ChatGPT or Claude. It will ask you to describe your agent setup: what it does, what systems it touches, what permissions it holds, how long it runs unsupervised, and what oversight exists. The more honest you are here, the more useful the output. If your agent has broad permissions and one person checking a dashboard once a week, say that. That’s exactly the kind of detail that matters.
Then it generates a structured risk report across all five categories. Each one gets a risk rating (LOW, MEDIUM, HIGH), a list of specific vulnerabilities it spotted, concrete mitigation steps, and the documentation a compliance officer wants to see. The output is formatted for a governance meeting. You show up with answers instead of shrugs. You reference a document instead of improvising.
The original author tested it on a customer service agent with CRM read access, no approval workflow for email drafts, and one person checking a dashboard once a week. The report flagged shared API keys, missing escalation processes, and permission creep. All real problems. All findable before the meeting. None of them were things the team had intentionally hidden. They just hadn’t looked. The prompt made them look.
💡 Tips and tricks
- Run it before submission. Find the objections before your risk team does. You get to frame the problem and present the mitigation in the same document. Dramatically fewer back-and-forths, and you look like someone who takes security seriously rather than someone who got caught off guard.
- Use it on vendor pitches. Sales teams love phrases like “fully autonomous AI” and “seamlessly integrated.” Paste their architecture description into the prompt and see what they’re quietly not mentioning. You’ll walk into vendor calls with sharper questions and a much clearer sense of what you’re actually buying.
- Run it on existing agents too. You probably have something already deployed that nobody has reviewed in six months. Paste in its current setup, current permissions, and current oversight structure. The report will tell you whether the thing running in production still looks like the thing you originally got approved.
- Schedule quarterly audits. Agents drift. Permissions creep. The person who was supposed to check the dashboard left the company. Running this three months after launch catches the things that weren’t problems on day one. Every single time.
🚀 Try it today
Grab the full prompt from the Reddit post. Describe your agent setup with as much detail as you can. Include what systems it connects to, what actions it can take on its own, what requires human approval, and how long it typically runs between check-ins. The more specific you are about permissions, oversight, and how long it’s been running, the more useful the output. A vague description gets a vague report.
If you’re building with agentic AI, your risk team is eventually going to ask these questions. The only variable is whether you have the answers ready or whether you spend two more years hearing “this needs more review.” One prompt. Maybe forty minutes of honest thinking about your setup. That’s the trade.
ChatGPT Prompt of the Day: The Agentic AI Risk Scanner I Wish I Had at My Last Job
by u/Tall_Ad4729 in ChatGPTPromptGenius