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Best Practices for Using Generative AI in the Workplace

Joseph Sena

IT Specialists Working in Office

Over the past few years, we’ve seen artificial intelligence go from a technology of the future to something that is widely accessible. As a result, many organizations are looking for ways to incorporate generative AI in the workplace. 

When used effectively, AI and machine learning can be very beneficial for your organization. These technologies can help boost productivity at work, making your daily operations more efficient and less stressful for your team. 

Unfortunately, generative AI technology comes with some serious security concerns to be aware of. Putting a policy in place can help protect your company’s data and ensure that you’re using generative AI safely. 

Here’s what you need to know about using generative AI and machine learning tools at work safely. 

Key Takeaways

  • Generative AI technology uses AI to respond to prompts and create pieces of data, such as text or images. 
  • Many organizations are using generative AI to automate processes and work more efficiently. 
  • Generative AI tools come with a number of security and privacy risks that organizations should be aware of. 
  • Creating a company-wide AI policy can help you avoid data breaches and other frustrating AI security problems. 

What is Generative AI/Machine Learning? 

Generative AI tools use artificial intelligence technology to respond to prompts and create pieces of data. For example, generative AI tools can create text, images, audio, or video based on user input. 

Machine learning is a much broader branch of AI technology. This focuses on teaching machines to recognize patterns and learn new things so that they can complete tasks without any programming. 

Over the past few years, generative AI tools have become widely accessible to the general public. ChatGPT popularized the concept of a free large language model chatbot, which can answer questions and create content based on user input. 

MSP Expert Tips on AI/ML Best Practices

MSP Expert Tips on AI/ML Best Practices

It’s very important to be cautious when using generative AI and machine learning technology in the workplace. As a managed IT services provider, here are the best practices we recommend when incorporating AI tools into your operations. 

Create a Company Policy

A company AI policy should set guidelines for when and how to use AI tools in the workplace. Proactively putting an AI policy in place can help your entire team minimize cybersecurity risks and use these tools safely. It also sets expectations clearly to help prevent any confusion. 

In your AI policy, specify which AI tools your teams can use and which scenarios they can use them in. You should also specify what types of information can be used in AI prompts. 

You can also use this policy to set expectations and guidelines for adopting new AI tools. For example, if an employee wants to try using a new AI tool as part of their work, this policy should specify who they should talk to and how to get approval before implementing a new AI tool. 

Finally, you should specify potential consequences for breaking this policy. 

This policy should be reviewed by cybersecurity experts before it is finalized to ensure you’ve addressed all possible risk factors. Distribute the policy to all employees and have them sign it. Requiring a signature from your employees will help ensure enforceability. 

Prompts Should Be Trackable

If your team opts to use AI for any reason, all prompts or queries should be fully trackable and auditable. Management should have access to every employee’s AI accounts. 

This way, if any security issues arise, you’ll be able to determine exactly where each prompt came from and solve the problem. 

Additionally, generative AI output should always be carefully reviewed for accuracy before being implemented. For example, if you use generative AI to speed up your document creation, those documents should still be reviewed manually by someone on your team before they move forward. 

Limit Access to AI/ML Tools at Work

AI and machine learning tools can help make your operations faster and more efficient, but they’re not appropriate in every context. To keep your systems and data safe, limit access to AI tools to areas of your business where they can have a significant impact. 

For example, if you’re struggling to maintain an online presence, you might want to focus on using AI for social media, rather than applying it broadly across your organization. 

Generative AI tools can help you come up with ideas for your content calendar and proofread your captions before they go live. 

Of course, all AI prompts and output should be reviewed for accuracy and output. However, limiting AI applications to specific areas of your business makes this technology much easier to monitor. 

When your teams do use AI and machine learning technology at work, they should be very cautious about the information they share. All prompts should avoid sharing identifying information like company and employee names, phone numbers, addresses, and emails. 

You should also avoid sharing intellectual property, protected health information, or any other sensitive data. 

Consider Designating a Prompt Engineer

A prompt engineer is someone who can review suggested AI prompts and output before it is used in a business context. This creates an extra layer of security and protection and ensures that prompts are optimized for efficacy. 

Ideally, your prompt engineer would be a designated staff member with extensive AI training and expertise. However, if this isn’t feasible, you can also implement a prompt review board with your existing team members. 

Use a Private AI/ML Model

Many popular open AI models have struggled with data breaches over the past few years. As of early 2024, 77% of companies have reported that their AI models have faced data breaches. 

To avoid these risks, consider implementing a private AI model instead. While private AI models do require extensive investment, they are far more secure than open AI models. They can also be fully customized to meet your organization’s needs. 

How are People Using Generative AI at Work? 

Workplaces have quickly adopted generative AI tools as a way to be more productive. Generative AI is particularly helpful for automating repetitive tasks so employees can get them done faster. 

This gives employees more time to focus on complex tasks that AI can’t do, such as business development or working with customers. 

Many teams also use generative AI tools for writing and coding in the workplace. Many people find generative AI to be very useful when brainstorming new ideas, as it can help answer complex or esoteric questions to spark new thoughts. 

On top of that, generative AI tools can be very helpful for documentation and data analysis. For example, generative AI can synthesize a large volume of documents at once and answer questions about them. 

AI technology is evolving quickly, so we will likely see even more workplace applications in the near future. Many SaaS organizations are in the process of adding generative AI features to their platforms, which will further integrate this technology into the workplace. In fact, over 3,500 apps have already added “GPT” to their descriptions. 

What are the Concerns of Generative AI/Machine Learning? 

While generative AI technology has some powerful capabilities, it also comes with some unique security and privacy concerns that every organization should be aware of. 

Many generative AI tools have very unclear privacy and security policies, which means that users need to be very careful about the information they share with them. 

For example, many generative AI tools are trained on the information users input into the system. This means that if you ask a generative AI tool to assess a proprietary document, it will use that document as part of its training and could even share that proprietary information with future users

Additionally, generative AI tools aren’t always clear about their cybersecurity policies. These tools have been heavily targeted by cyber criminals due to the large volume of data they store in their algorithms. 

This means that if you use a generative AI tool that doesn’t have a good cybersecurity strategy in place, your information could be at risk. If that tool is successfully targeted by cyber criminals, they could gain access to any sensitive personal or proprietary information you’ve shared with it. 

Many hackers also use generative AI tools to conduct their attacks more efficiently. For example, there has been a significant increase in phishing and social engineering attacks since the release of ChatGPT. 

Between Q4 2022 and 2023, there was a 1,265% increase in phishing emails, likely due to the newfound accessibility of AI technology. This is because hackers can use generative AI to write more convincing phishing emails and send them out more quickly. 

Another significant concern with generative AI tools is accuracy, especially if you are using them to create content or handle complex calculations. For example, the free version of ChatGPT is only trained up to January 2022, which means it is missing more than two years of current events and updated research. 

If you use inaccurate AI output in your work, it could cause a variety of issues for your organization. This could range from inaccurate financial calculations to non-functional code to reputational damage. 

Additionally, some generative AI tools have been trained on copyrighted material. This means that you could inadvertently plagiarize if you use AI tools to create content, which could open your organization up to lawsuits.