Does Your Business Have an AI Policy?

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Does Your Business Have an AI Policy?

Does Your Business Have an AI Policy?

By on Feb 4, 2025 in Artificial Intelligence, IT Consulting

The AI wave has quickly revolutionized business over the past few years, finding its way into everything from customer service chatbots to predictive analytics, human resources departments, and a long list of other use cases.

Yet, as adoption accelerates, many businesses still underestimate the risks of AI.

To make matters worse, inconsistent, and incomplete regulations only add to the confusion. In the U.S., there’s no federal law regulating AI for now, but states have their own fragmented rules. While states like California, Illinois, and New York have strict laws on automated decision making or profiling, many others don’t have clear regulations in place. This uncertainty creates a complicated landscape for companies looking to harness the benefits of AI in their businesses.

Whether your team is already experimenting with AI tools or just starting to explore their potential, a clear AI company policy is no longer just a “nice-to-have” but an absolute necessity to keep your business secure.

The Risk of Rushed AI Adoption

Clearly, the hype around AI is everywhere. Media and industry experts keep talking about how AI can cut costs, improve innovation, and help businesses stay ahead. However, this rush to “keep up” can lead companies to adopt AI tools without adequate precautions.

The harsh reality is that AI’s risks are often invisible until they’re catastrophic. Picture employees copying private customer details into public chatbots like ChatGPT, or teams trusting AI reports full of errors. This can result in more than just awkward moments; these slip-ups can lead to lawsuits, fines, and reputational damage.

Here are some key concerns to consider:

Accuracy and Accountability Problems

While human workers make mistakes, their errors tend to stay contained. Think about it—an employee can only act within his or her specific responsibilities, and their work is usually approved by a superior.

AI errors, however, scale exponentially. Poor data leads to bad outputs and an algorithm trained on flawed data can misprocess thousands of transactions, misclassify customer feedback, or generate inaccurate forecasts.

When the entire organization relies on AI-powered operations, things can quickly spiral out of control.

In fact, a 2024 Salesforce survey highlights this concern: 56% of AI users struggle to get accurate outputs, and 54% distrust the data used to train AI systems.

This “garbage in, garbage out” problem is amplified by AI’s opacity. When an AI tool makes a mistake, who’s accountable? The developer? The user? The data source? Without a clear policy, situations like this can lead to blame shifting and operational chaos in your business.

The Inherent Biases in Large Language Models (LLMs)

Another big concern with AI is how it can unintentionally carry forward or even amplify biases present in the data on which it’s trained.

For example, a hiring tool trained on resumes from a male-dominated industry might downgrade female applicants. Alternatively, when it comes to healthcare, biased algorithms could misdiagnose conditions in underrepresented groups.

Real-life examples already show this issue. According to a study by researchers at the University of Chicago, the University of California, Berkeley, and Partners HealthCare in Boston, racial bias was detected in the software widely used in the healthcare industry. Clearly, these biases must be accounted for and mitigated before they become liability.

AI Hallucinations

AI hallucinations are another tricky issue to tackle. Almost all generative AI tools on the market tend to “hallucinate,” which means making up facts, citations, or statistics with confidence. It’s a bit like a person telling you something that’s completely wrong, but they’re doing it with such confidence that you almost believe it.

While companies try to minimize this, even occasional hallucinations can easily mislead users. Therefore, it’s important to be cautious and double-check any AI-generated info, especially when the stakes are high. For most users, AI is reliable in everyday use. That’s why it’s easy to let your guard down and miss the occasional misstep.

Cybersecurity, Compliance, and Legal Risks Employees Bypassing Security Controls

Out of habit or simple oversight, it’s easy for employees to bypass security controls when using AI.

A 2023 study by Cyberhaven found that 11% of data pasted into ChatGPT contained sensitive information. This could mean proprietary code, client passwords, or even internal strategy documents slipping out of the company. If that data gets into the wrong hands, the risks could be huge.

The problem is that most public AI tools don’t have strong data protection, so what seems like harmless experimentation could quickly turn into a serious security breach.

Regulatory Risks

AI regulations still lag behind the rate of adoption. However, as this space evolves rapidly, regulations are bound to catch up.

There are already a few regulations in place now, though they don’t apply to Ohio businesses. This includes the EU’s AI Act, which classifies tools by risk level, banning certain applications, such as social scoring. Some U.S. states, including Colorado and Illinois, have mandated transparency in automated hiring tools.

Non-compliance can mean fines, legal battles, or forced AI shutdowns. It’s important to ensure your enterprise use still falls within the regulatory purview when it comes to privacy and security laws.

Implementing an AI Policy: A Practical and Actionable Approach

If your business is utilizing AI-powered tools in its operations, it’s essential to have a clear AI policy in place to ensure that it’s working for you, and not against you.

Define AI’s Role and Boundaries

Start by mapping out where AI can bring value. It might be very useful for automating routine tasks, such as meeting transcriptions and data, while also being able to improve operations in places like predictive analytics and customer insights. These use cases must be mapped and authorized.

Since not all use cases carry the same risk, classify tasks as low, medium, or high risk, and develop separate protocols for each. Next, clearly define where AI can and cannot be used. For example, specify that AI is permitted for low-risk administrative tasks but banned from making final hiring decisions or managing customer financial data.

Set Clear Ethical Guidelines

Setting ethical guidelines for AI use helps you better handle the inherent downsides of AI. Whether it’s avoiding biases or having transparent processes, it’s important to promote fairness and make employees part of the policy development process to ensure their buy-in and understanding.

After all, they’ll be the ones using AI tools, and it’s important for them to be able to verify and challenge outcomes that don’t seem right. It’s also important to define a clear line of accountability in case of AI mistakes and advise best practices to mitigate them.

Establish Strong Security Measures

As useful as AI can be, it does open your business to new risks. It’s important to address these risks before you can fully leverage their advantages. For example, employees should never input proprietary data, client details, or internal documents into public AI tools.

If your use case does demand such applications, then at least make sure the platform is secure; it may be worthwhile to consider tailor-made tools that come with better security. You also need to audit your AI systems at regular intervals to check for any security issues or leaks and set up monitoring to catch any suspicious activity or breaches.

Consult Industry Standards

You can construct your AI policy by following industry frameworks like the NIST AI risk management framework and the ISO 42001 standards, both of which offer a structured approach to identifying and managing AI risks.

Applying these frameworks will help you identify risks across the AI lifecycle—development, deployment, and operation. It also helps you develop strategies to mitigate these risks. These measures include implementing automated failsafes for AI systems in high-stakes sectors like healthcare, finance, or manufacturing.

Additionally, establish a clear AI governance structure with defined roles for risk management, and create an oversight committee comprising key decision makers from IT, security, legal, and HR. By applying these principles, you can systematically address AI concerns.

Prepare to Upskill and Train Your Staff

AI has the potential to be a game changer, and with that comes disruption to traditional workflows. As with any new technology, some roles, especially those reliant on repetitive tasks, may see significant automation, while others may evolve rather than disappear.

These changes can leave employees anxious as they navigate new challenges. Some of the less tech-savvy workers are likely to feel particularly intimidated by the learning curve.

According to a survey by Gallup, a third of employees said they are very uncomfortable using AI in their roles.

Similarly, a 2023 KPMG report, produced in partnership with the University of Queensland, found that 61% of respondents globally were ambivalent about or unwilling to trust AI. To address this, businesses must invest in upskilling and training programs to help employees gain confidence and adapt to AI-driven changes.

Develop an AI Policy with a Trusted IT Partner

The Astute Technology Management team has a long track record of helping businesses in Columbus and Cincinnati adopt new technologies like artificial intelligence. If your businesses wants to embrace AI while mitigating the cybersecurity and compliance threats it presents, contact us anytime at [email protected] or (614) 389-4102. We look forward to speaking with you!