AI agents are driving efficiency, but also introducing serious, often unseen security risks. As adoption accelerates, unvetted access, prompt injection, and poorly controlled environments can expose sensitive data and disrupt operations. This episode of Compliance Unfiltered breaks down the key threats and shows how to mitigate them using proven principles like least privilege, input validation, and isolated execution. Learn how to secure AI deployments and turn a growing risk into a resilient advantage.
Episode Transcript:
You’ve been talking about the AI zombie walk for some period of time now. What perils are folks walking into with AI agents?
Adam Goslin:
We’ve got the democratization of agentic AI on the march. Anybody can get the latest tools and create these incredible AI agents that can do almost anything you can imagine. It’s part of the main reason why the agentic AI move can be a substantive risk for the organization as well.
One of the big problems they’re having right now is that, while security and compliance folks understand the risks of AI, there are a ton of frontline users that are just clicking buttons and building tools and making things automated and better. There isn’t, in a lot of cases, any thought to the security implications of what they’re in the process of doing.
Thinking about security isn’t an element of day-by-day workflow, and that’s where this notion of agentic AI comes in riskiest, if you will.
Leveraging platforms for building these AI agents without a security background, I’d liken it to giving a three-year-old an arc welder. It might be able to figure out how to turn it on, but can you imagine the untold damage that they could do with it?
You put a powerful AI tool in the hands of people that don’t know about protecting data, layers of security, and how to appropriately restrict access. They’re not going to know how to use it safely.
There are a lot of considerations when it comes down to building AI agents in a secure fashion. But most of it honestly comes down to security principles.
It’s possible for employees to build those safely, but there needs to be that marrying of the security and compliance-style mindset in conjunction with what’s going on.
Todd Coshow:
What type of efficiency risks are folks running into?
Adam Goslin:
AI agents are often used for personal efficiency and internal workplace functions. To give some examples: consolidating, summarizing, and filtering across multiple email accounts; automated execution of auto-replies; analyzing workflows and calendars for identifying efficiencies; gathering up data; and preparing written summaries of client projects.
These things may seem harmless, but just consider the risk of letting the AI agent loose on your calendar. How much data did you just expose as a result of clicking, “Sure, you’re going to have full access to my calendar”?
The agent has access to your client lists, client contacts, emails, signatures, phone numbers, and cell phone numbers. All of that starts to come into play as you’re granting blind access to Office 365, as an example.
Maybe that extends to OneDrive and SharePoint. Maybe, depending on the user and their access levels, they could be granting a ton of access.
Even things that seem innocent, such as tracking birthdays or anniversaries, could similarly produce greater levels of exposure than you were even considering.
The company needs to consider what is the data and information that’s exposed to the AI engine, how do we want to use it, and whether or not that data is secluded from other things.
If you’re moving from a free version to a paid version of AI, your users may still be jammed into some gigantic public pool of data storage.
Even when the AI vendors are claiming, “We don’t hand your data over to public AI models,” you need to look closely at what they are doing, such as utilizing the information that’s gleaned from the individual users when it comes to training their engines.
