Generative AI is increasingly prevalent across all industries from retail to financial services to healthcare. You may have recently used AI to help brainstorm content ideas, write code, or edit an image. For most businesses, however, generative AI still has major drawbacks that can ultimately cause more harm than help. Here are our experts’ top tips for how to make the most of generative AI in your business.
- Safeguard Sensitive Information
- Be Mindful of Budgets
- Tread Carefully on Coding Shortcuts
- Organize Your Workflows
- Avoid Misinformation
Business Security Concerns With Generative AI
Most importantly, remember—technology is a tool, but you need to use it properly. When generative AI is used to rush processes or take shortcuts, it can, at best, result in poor craftsmanship and, at worst, lead to a serious exposure of confidential data or an increase in legal liability. While AI can often save time by automating mundane tasks or expediting manual processes, it’s important to reallocate that extra time to improving your business systems and workflows. Effective companies think about IT strategy, not just which tools they can use to get the job done.
1. Safeguard Sensitive Information
Publicly available tools, such as ChatGPT or Google’s Gemini, save the data they collect. They also make inputs available to human reviewers. Even with proprietary machine learning tools, it’s important to think critically about the information you’re using for inputs as it may be recycled in unexpected ways down the road.
For example, if a product engineer entered technical details or blueprint designs of a new prototype into a free version of a generative AI tool, or if a human resources team member used generative AI to sort employee information, these sensitive details could be picked up in the model’s training and potentially shared with unauthorized parties.
It’s best to have clear company policies for what information cannot be entered into third-party generative AI tools and to keep confidential materials and proprietary trade secrets in a secure hosting environment.
2. Be Mindful of Budgets
While many generative AI tools start out with free versions, most eventually move to paid subscription status. As of this writing, Microsoft Copilot is an additional subscription fee on top of what your organization is already paying per user for Microsoft 365.
It’s worth thinking about the potential of these tools from a resource allocation perspective. While Copilot might save time by automating certain tasks or speeding up some processes, is the amount of money it saves offset by the subscription cost? Is there an opportunity cost of not investing in another tool because all of your budget went into generative AI?
Not having a long-term strategy is one of the biggest mistakes a business leader can make. Before you sign up for a new AI tool, ask yourself what problems it’ll solve and if it’s worth the investment.
3. Tread Carefully on Coding Shortcuts
Generative AI absolutely saves time on coding—at least initially. The problem with going too fast with software development is that your teams may end up skipping important QA and security checks. Rushing through coding can also result in technical debt. A quick “hack” in generative AI might end up being a cumbersome fix later on when your business scales.
For non-coders, it’s helpful to compare generative AI’s output on written content. While AI tools often create paragraphs convincingly, a closer inspection can usually find a few factual errors or instances of unnatural syntax.
The same applies to coding. Generative AI will give you a quick solution to nearly any coding problem, but has it included an error that will lead to a security issue? Has it offered some syntax that’s actually not the most elegant solution? Expert coders should still carefully review any AI outputs before they’re deployed on large projects.
4. Organize Your Workflows
Another concern of over-relying on generative AI is that it’s difficult to track decision-making and workflows. Unlike most cloud-based services, it’s often challenging or even impossible to see a revision history on a project.
At scale, this can make it difficult to work backward. If a marketing team needs to make an edit to a video or a sales team needs to access a previous draft of a proposal, it may be difficult to “go back” and find original files in a generative AI application.
For any project involving AI, it’s important to keep careful documentation as to what inputs you used, what the original output was, and how it was edited prior to implementation.
5. Avoid Misinformation
Lastly, generative AI may increase your legal exposure due to the technology’s potential for misinformation. Used improperly, generative AI can create convincing inaccurate or plagiarized content which may:
- Infringe on intellectual property rights
- Violate privacy laws
- Deceive consumers
Regulatory bodies are increasingly scrutinizing the use of AI technologies, which could eventually lead to imposing fines and penalties for non-compliance with data protection and consumer rights regulations. In extreme cases, your business may face lawsuits for defamation, copyright infringement, or fraudulent activities facilitated by AI-generated content.
Strategies to Reduce Risks of Using Generative AI in Your Business
To get started, it’s essential to establish robust protocols for data handling and access control. This involves limiting access to sensitive data used in training generative AI models.
Secondly, provide comprehensive training programs for employees on cybersecurity best practices to enhance awareness and prevent inadvertent breaches. They should be educated on recognizing and responding to potential risks posed by AI-generated content.
You can also utilize private cloud solutions. They can enhance your data security by providing dedicated resources and isolated environments for running generative AI models. This helps prevent unauthorized access and data breaches.
For all this and more, partner with TenHats as your IT MSP. We specialize in cybersecurity to provide your business with valuable expertise and resources for implementing and managing robust security measures.
We also offer proactive:
- Threat monitoring
- Network infrastructure
- Strategic guidance
By adopting these strategies and leveraging the expertise of an MSP, your company can effectively mitigate security risks associated with the use of generative AI.
In 2016, TenHats built the region’s first purpose-built colocation data center in over 20 years. Located in Knoxville, TN, our data center can serve any organization in East Tennessee and beyond. With our team’s IT experience, we provide a lot more than simply protected data. When you call us, you talk to a real IT expert. Connect with our team about our data center today.
