Generative AI tools like ChatGPT, Claude, and Copilot are changing the game for many organizations. They offer fresh avenues for efficiency and innovation but also bring new risks. If your company handles sensitive data or faces compliance pressures, jumping in without careful thought isn’t wise.
First, you need to grasp both the intended and unintended uses of GenAI. Understand its strengths and weaknesses before diving in. Following trends blindly isn’t the way to go; let risk guide your decisions instead.
Many organizations think they need entirely new policies for GenAI. That’s usually not the case. It’s better to build on what you already have—like acceptable use policies and data classification systems. Adding disconnected rules can confuse everyone and lead to policy fatigue. Instead, weave GenAI considerations into existing frameworks.
One major oversight is input security. People often focus on whether the output is accurate or biased, but the immediate risk lies in what staff are inputting into public large language models. Inputs can include sensitive information—project names, client data, financial numbers, even passwords. If it’s something you wouldn’t share with an outside contractor, don’t hand it over to a public AI.
Not all AI risks are created equal, so differentiate between them. Using facial recognition for surveillance poses different risks than allowing developers to use an open-source GenAI model. Grouping everything under a single policy oversimplifies reality and can create blind spots.
Here are the five core risks that cybersecurity teams should keep an eye on:
- Inadvertent data leakage: This can happen through public GenAI tools or mishandled internal systems.
- Data poisoning: Malicious inputs can skew AI models or lead to poor internal decisions.
- Overtrust in AI: Relying on AI outputs, especially when their accuracy can’t be verified, is dangerous.
- Prompt injection and social engineering: People can exploit AI systems to extract data or manipulate others.
- Policy vacuum: If AI is being used casually without oversight, that’s a problem.
Addressing these risks isn’t purely tech-based; it’s about people. Education plays a key role. Your staff must understand what GenAI is, how it works, and where it can go wrong. Tailored training for different roles—like developers, HR, and marketing—can significantly lower misuse and foster critical thinking.
Your policies also need to set clear guidelines for acceptable use. For instance, is it acceptable to use ChatGPT for coding but not for drafting client emails? Can AI summarize board minutes, or is that off-limits? Establish clear boundaries, and create feedback loops where users can flag issues or seek clarification.
Finally, integrating GenAI use into your cyber strategy is crucial. It’s tempting to get swept up in the AI hype, but leaders need to start by identifying the problem they aim to solve—then consider if AI fits into that solution. If it does, it can be incorporated safely and effectively into your existing frameworks.
The goal isn’t to block AI; it’s to embrace it thoughtfully through careful risk assessment, policy integration, user education, and continuous improvement.