It seems like we’re jumping into the AI pool pretty quickly. While I love AI, and there are some absolutely positive cases for it, I think we have to talk about AI risk management in all scenarios. These are the safeguards we put in place before an AI is launched into the world, so trustworthiness is built into the package. But that’s just the start of it.
Because AI is so new and, thanks to science fiction, has a lot of people scared of their robot overlords, we need to address AI risk management now. And, if we don’t talk about these things today, state and country governments are going to make the decisions for us—just look at the EU Artificial Intelligence Act to see what I’m talking about.
Let’s Address AI Risks
What kind of things do we have to worry about with AI? Well, here are a few basic AI risks:
- A Loss of Reality: Between deepfakes and devices like the Pixel 9 creating “memories” instead of photos, it’s hard to figure out sometimes what’s a photo and what’s not. Do we lose a sense of self when this happens? How do we know what to trust?
- Bad Data: If the AI is trained on corrupt or poor-quality data or the data is not transparent, then the results can be biased for or against certain groups of people.
- People Losing Jobs: There are already folks who were gainfully employed and are now replaced by AI. Is this what we want AI to do, or is it a tool to help other people? Will we lose a human connection when we replace people with AI?
- Privacy: Is my information being used to train AI systems? How would I know? Can I trust the AI to keep whatever questions and information I enter into it private?
And this is just cracking the surface. There’s a lot more to talk about and consider. For example, how do we mitigate AI cybersecurity risks? One way is to ensure that access to the data is kept locked down tight. Once data and privacy issues rear their heads, you’ve got some big fish to fry.
Of course, that brings us to a bigger topic:
What Are the Best Practices for AI Security Risk Management?
This is a tricky and complex question, but there are a few key things to think about.
- Data and Privacy Concerns: This should be a standard for pretty much any organization that uses people’s data, but that’s even more important when it comes to AI. You don’t want someone’s personal information fed into the AI slurry, nor do you want it exposed unintentionally. Lock that information down as tight as possible.
- Keeping Your Employees Aware: While you may not think it’s critical to let your normal rank-and-file employees know what’s going on with your AI, some people will either want to use it and do so too much or feel like they could mess around with the product. You have to set strict guidelines. Otherwise, people will cross lines.
- Access Control: Who can get into your AI system? What can they do? Are they someone that can be trusted? Do they have the ability to enter data into the system? There’s a lot to think about when it comes to who is allowed to do what within your organization.
Again, we’re just chipping away at this right now because there’s much more to talk about. Has your organization considered any of these things before? Because if you’re letting everyone run wild with ChatGPT or Claude without any guardrails, you may need to really think about where that path leads. Really, you could just ask yourself:
Are You Ready for the AI Future?
Now, all of this may seem a little bit daunting, and I get that. Just talking about AI risk management can make you feel like you’re wearing only a pair of underwear to your senior prom, and I get that. Fortunately, I’ve got a way to help you feel a little bit more confident about your AI future.
I’ve built a test that I call the AI Ecosystems Readiness Assessment. In it, we’ve got a whole bunch of questions designed to help you determine exactly how ready—or not—you are for an AI rollout at your organization. Take the test today and see what you discover. It’s pretty eye-opening, and when you’re done, you’ll know what you need to do next.
