The Blind Spot in Internal AI: Security and Reputation Risks
As AI becomes a staple in the workplace, businesses are increasingly focusing on using internal AI tools, like chatbots, to improve efficiency. But as these tools grow more capable, they also introduce new risks—risks that often start with curiosity and end with reputational or legal consequences.
One of the biggest challenges is the constant game of cat and mouse between users trying to “jailbreak” AI systems and developers racing to close those loopholes. Users can get creative, finding ways to bypass filters and protections built into AI models. Whether it’s tricking the system with cleverly phrased prompts or, as seen recently, reversing words or text,) people often find ways to push AI systems beyond their intended limits. This ongoing struggle between users and developers is a security issue not just for public-facing AI but for internal systems as well.
While this may seem like an external threat, it actually opens up a reputational risk vector that many companies haven’t thought of for their internal chatbots. Just because it’s behind the firewall doesn’t mean it’s safe from misuse.
It’s All Fun and Games… Until It Isn’t
Confession time: when I come across a new chat tool, whether it’s an internal widget at work or a customer service chatbot on a local restaurant’s website, the first thing I do is try to get it to write me a Python script. Why? Maybe because I’m really in need of new hobbies, but also because I want to see if the tool has context filters in place. If it politely declines, I’ll try a few more prompts, including, now, reverse text, to test how sophisticated the filters really are. It might seem a little obsessive, but in talking with others, I’ve learned I’m far from the only one who does this. Chatting with AI about anything and everything feels novel and fun, and people are going to push that envelope.
That’s the thing: whether out of curiosity, mischief, or boredom, employees are going to test internal AI tools. While it might start off as harmless experimentation, it can quickly lead to inappropriate results. And once word spreads that the AI generated something unexpected or even dangerous, it’s not just a private joke anymore — it’s a reputational risk for the entire company. As with many things, it’s funny until it suddenly isn’t.
Why Internal AI Tools Aren’t Immune
Many companies focus on securing external AI tools, but internal systems are often overlooked. The assumption is that employees will use these tools responsibly, or that because they’re “behind the firewall,” the risk is lower. But that can be a dangerous mindset. Just as employees push the boundaries of external AI, they’ll do the same with internal tools. The risk of misuse — whether accidental or intentional — can be just as severe inside the company as it is outside.
Take the example of someone using an internal chatbot to find sensitive or harmful information, like in the case of that bomb recipe query. Even if that chatbot isn’t meant for such tasks, once the information is generated, the company is at risk of reputational damage and legal consequences. The line between playful experimentation and actual harm is thinner than many realize, especially when AI is involved.
Simple Steps to Strengthen Internal AI Security
So, how can companies get ahead of this? It starts with the basics. First, employees need to know that their chat queries are recorded and subject to review. Posting this information clearly within the chat tool is a simple step that can act as a deterrent for inappropriate use. When people know they’re being watched, they’re less likely to test the limits.
Next, remember how you posted that chat queries are subject to review? The hard part is actually following through on that promise. Companies should implement a regular feedback review cycle. AI models should be updated frequently, and any queries that slip through existing filters should trigger a prioritized process to close those gaps. Internal feedback loops are key to keeping these systems safe and reliable.
And don’t overlook what your provider might already offer. Depending on your infrastructure, your chat system might have safety tools built in that can make a huge difference. For example, Microsoft offers an AI content safety product that helps filter out harmful content. It’s worth spending time understanding these tools and integrating them into your AI strategy.
If you’re rolling your own chat, consider whether AI could help police itself, With advancements in machine learning, AI tools can be trained to flag suspicious or inappropriate queries automatically. These red flags can be sent to a human reviewer, ensuring that questionable behavior is addressed quickly. Essentially, AI can help monitor and protect other AI tools, creating an additional layer of security.
A New Era of Corporate Policies
Just like acceptable internet use policies have evolved over the years, AI use policies need to follow suit. Companies should outline what’s considered unacceptable behavior with internal AI tools and establish clear consequences for misuse. But policies alone aren’t enough — AI safety mechanisms need to be built directly into the tools. This includes content filtering, query logging, and oversight measures.
These steps aren’t just about protecting company data; they’re about safeguarding employees and the company’s reputation. If we don’t take action now, we risk a future where AI becomes a dangerous tool in the wrong hands, even if those hands are inside the company.
Building Safe AI from the Inside Out
As AI continues to change how we work, it’s essential that companies approach internal tools with the same focus on safety as external-facing systems. Employees may test the limits for fun, but without proper safeguards, this behavior can lead to serious consequences. By clearly posting usage policies, implementing regular reviews, and even using AI to monitor AI, businesses can create safer environments while protecting their reputation and their people.
(Photo: an alley in Downtown Toronto, taken September 2024)
