Everyone's talking about GenAI, and it isn't hard to see why. One of the newest and buzziest technologies on the market, GenAI unlocks support for a variety of use cases. GenAI-powered chatbots can act as informed assistants to team members, GenAI can produce well-written content with a single prompt, and GenAI can even help organizations respond agilely to outside threats.

It's exciting, especially for business owners who strive to stay on the technological innovation curve. So exciting that GenAI tech has been woven into a number of large, popular on-market applications, from Microsoft OS to Google. But being a new technology, it also comes with risk.

While GenAI is largely helpful, it also has its own set of inherent vulnerabilities, prompting no small amount of concern from cybersecurity analysts. For organizations like yours, the question is this: how can you take advantage of this new technology and reap the rewards, while also protecting your data and your systems from incursion?

Let's dig into it.

AI's Inherent Vulnerabilities

Firstly, let's break down exactly what you have to be worried about when using AI-powered tools. Most forms of GenAI work by collecting large swaths of data, analyzing that data to better understand the context of our language, and then using that knowledge to assemble output in response to user prompts. But how that data is collected, how it's used, and whether the AI is learning the correct things from its analysis are all points of concern. Analysts worry about:

• Data collection practices: There have been questions about the ethicality of how some popular GenAI applications collect their data. Where does their data come from? How much of it is public, and how much of it is private? Will GenAI accidentally replicate and reuse people's personal data in its responses? And how does the question of copyright come into AI data collection? Does AI (or will it) use copyrighted materials to learn and evolve?

• System bias: AI learns from the data it collects, meaning it ingests and sometimes replicates all the biases in that data. Scientists aren't necessarily able to identify when GenAI applications make biased decisions either, calling the decision-making process a "black box." Will AI replicate problematic ideas or ideologies? And how can you, the user, identify when your AI tool makes a biased decision?

• Content generation: AI generates content in response to user prompts, yes; but there's no guarantee the information it puts out there will be accurate. Moreover, AI can be used to generate highly unethical content, like deepfake videos and falsified photos. How can you make sure the content you generate is both ethical and accurate?

• Cyberattack innovation: AI can be used by cybercriminals to create innovative new cyberattacks. Whether it's making old tactics like phishing or social engineering seem more real, or coming up with an entirely new way to trick organizations, cybercriminals can use AI just as much as the rest of us; and you'd be right to ask, to what effect?
  

This hurricane of ethical questions and inherent vulnerabilities might tear down some business owners' interest in the technology, but we don't recommend moving away from AI entirely. Instead, you do what you do when a real hurricane hits: you shore up, and create a framework that will protect your property.

Creating a Cybersecurity Framework

Constructing a framework that will shore your business up against AI-related risk is easier than you think, especially once you know how those risks may affect your business. Putting cybersecurity protections in place is absolutely essential, as is planning which tools you aim to use and how (as different tools will have different levels of risk). You can also follow existing models like the NIST Framework, which will help you proactively identify areas of risk within your own organizational structure.

As you're creating your own risk management framework, follow these steps:

• Prioritize cybersecurity measures: data encryption and anonymization are absolutely essential for keeping sensitive information from getting ingested by AI. Alternatively, in the event of a breach, these features can keep cybercriminals from getting their hands on anything useful.

• Maintain regulatory compliance: treat federal and state regulations as best practices for data storage and transmission. Avoiding crossing those lines will add an extra layer of protection for your sensitive data.

• Establish controls: many GenAI tools come with built-in controls for how they handle your data. You can also customize those tools, either with your own development team or with the help of a consultant. Putting controls in place and monitoring their effectiveness will help you keep a pulse on how AI is using your data, noting and avoiding areas of risk.
  

As with most large-scale changes, integrating AI into your workplace requires a great deal of forethought, research, and iteration. You'll want to keep an eye on how systems mesh with the AI, how well your controls are keeping it within set parameters, and whether its responses seem biased in any way.

However, with the right security framework, you'll be well on your way to using AI in a way that's ethical, powerful, and profitable.

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