According to one recent study, about 77% of organizations say that they are currently using artificial intelligence in their business, or plan to start exploring what that would look like in the near future.
Not only that, but about 83% of enterprise leaders say that AI is a "top priority" in their current business plans.
AI tools like ChatGPT, Microsoft Copilot, and others have quickly become so attractive to organizations thanks to the sheer volume of benefits they bring with them.
They help wade through massive amounts of data so that essential insight can rise to the top, leading to better decision-making.
They help improve the customer experience in an area like finance that demands a personalized approach to every interaction.
They help achieve better outcomes for patients in healthcare.
The list goes on and on.
But as is true with anything this powerful and with a scope this far-reaching, AI tools can become a liability if left unchecked.
Keep in mind that even though 99% of companies say they already have data protection solutions in place, about 78% of them also admit that they've had sensitive data breached, leaked, or exposed in the past.
To put it another way, the potential reward of artificial intelligence in the workplace is incredibly high.
Never let yourself forget that the associated risk is, too.
That's why it's so critical to move into this "brave new world" with the best practices of AI governance by your side.
If nothing else, they give you, your organization, and your customers/clients the best chance of being able to leverage artificial intelligence in a way that cements your competitive advantage in an increasingly crowded marketplace moving forward.
To get an idea of just what an impact AI tools like Microsoft Copilot are poised to have, consider the fact that as of 2022 there are 345 million paid users of Microsoft 365.
That year, the suite generated $63.36 billion in revenue and enjoyed a market share of approximately 47.9% of enterprise users.
Microsoft Copilot is integrated into Microsoft 365 - meaning that those millions of users are about to have an innovative AI tool built into the computer they're already using whether they realize it or not.
Again, every industry is different, but the situation is particularly dire when you talk about a field like healthcare.
Not only is healthcare one of the most commonly targeted industries for data breaches, but most breaches involve healthcare providers in some way.
They make attractive targets because of the value of personal patient information that can be stolen.
That was already true before you added a powerful interconnected AI-driven system on top of everything, creating yet another enormous vulnerability just waiting to be taken advantage of by someone who knows what they're doing.
This alone should make a compelling argument for why establishing Copilot controls and AI protocols must become a top priority.
Several potential risks are associated with AI use, with data misuse being chief among them.
If a patient's confidential file is kept in a locked filing cabinet in a doctor's office, you can control access to that information.
Whoever has the key has access.
If that same data is digitized and stored on a computer that is connected via an AI platform like Copilot that all users have access to, it becomes more difficult to fully understand who has access to what information and how.
If someone who shouldn't have access to that information were to suddenly gain it, what harm might come to the patient?
This segues into another one of the major risks associated with AI use - privacy concerns.
What type of sensitive data can safely be exposed to the system without worrying about the system itself getting compromised?
Finally, unintended bias is always a risk when it comes to something that ingests as much data as AI.
Selection bias in particular is what happens when the crucial data that is being used to train an AI system isn't representative of the reality that it is meant to model.
If you were to take an AI system that is intended to treat all patients equally and only train it on the data pertaining to a select few, the bias in your sampling (and other problems, like incomplete data) might lead to a data output that doesn't represent the audience that tool is trying to serve.
As you begin putting together an AI governance framework for your organization, you must focus on a few core areas - the policies that should be developed, the standards you should follow, the roles each person has to play, and the responsibilities of both the individual user and the organization as a whole.
In this context, policies are the rules that everyone involved in an organization must follow regarding how an AI tool can be used and, more importantly, how it can't be.
Users are always the first line of defense when it comes to making sure protected information stays that way.
Standards are also essential, as they make sure that data is created in a uniform way that makes it easy to enforce those larger rules.
It's one thing to say that patient medical records or test results need to be kept away from prying eyes.
But if those test results are stored in a non-standard format that doesn't make it clear what is actually inside a file, the policies you've created are essentially impossible to enforce.
Roles and responsibilities refer back to that larger idea that "only people who need access to certain types of data to do their job should have it."
When talking about a client file containing sensitive financial information at a place like a community bank, which roles within the organization need access to that information?
How are they allowed to use that information?
These are essential questions that you need to answer moving forward.
During the development of this framework, you'll want to involve various stakeholders to help make sure that coverage is as comprehensive as possible.
It shouldn't be organizational leadership or IT departments making arbitrary decisions in a vacuum.
What do you collectively need this system to be able to do and what protections should be in place to prevent misuse?
Consider everyone's feedback during deployment.
This will also help make sure that the framework aligns with your broader business goals, too - not to mention compliance requirements like HIPAA that you need to stay aware of.
When it comes to Microsoft Copilot in particular, there are several ways that access can be controlled based on not only someone's role within an organization but also data sensitivity, as well.
As it pertains to role-based access, the use of Copilot should be granted based on job functions and responsibilities.
For example, only designated team members or departments directly involved in projects requiring Copilot assistance should have access.
Not every employee in a business needs something as powerful as an AI assistant to do their job every day.
The same concept is true of data sensitivity classifications.
This means that data accessed or processed by Copilot should be classified based on its sensitivity level (with examples including "public," "internal use only," "confidential").
Access controls should be aligned with these classifications to prevent unauthorized exposure of sensitive information.
When you start to touch on using Copilot in the most ethical way possible, the discussion gets a bit broader.
Copilot users must adhere to strict ethical principles such as respect for intellectual property rights, avoidance of bias, and consideration of privacy concerns.
This includes "common sense" items like not using Copilot to generate content that infringes on copyrights or violates confidentiality agreements.
Finally, no discussion of AI governance would be complete without touching on the importance of monitoring and reporting regularly.
Think of a framework like this as a "living document."
The AI tools that it is meant to allow oversight into will constantly evolve.
The version of Copilot that you'll be using five years from now will not resemble the one in use today.
How, then, can the same rules be expected to apply to both equally well?
At an organizational level, always put effective reporting mechanisms into place for the sake of transparency and accountability.
You need to look at not only the way artificial intelligence is being used, but you should also conduct regular reviews and audits as well.
In the unfortunate event that a data breach does occur, you need a plan in place to respond as quickly as possible.
Especially in high-target industries like healthcare or personal finance, it is only a matter of "when" you become a target, not "if."
The number of reported data breaches in the United States alone was up 78% in 2023 from the previous year.
That's a trend that shows no signs of slowing, especially not with the arrival of AI tools into our lives.
From a certain perspective, artificial intelligence indeed is one of the most powerful tools to come along in history.
This is true regardless of the type of business you're running or even the industry that you're operating in, but it's especially key for organizations in fields like clinical healthcare and finance, to name a few.
Those fields depend on maximizing every second of every day in a quest to provide a more personalized level of care and attention to detail to audiences, and AI can help you do it.
But at the same time, AI governance cannot be an afterthought.
As tools like Copilot become more essential to the workplace, they become more intertwined in enterprises everywhere.
That means that there isn't necessarily a limit to the damage that can be caused if these tools are deployed improperly, or if they're allowed to fall into the wrong hands.
Microsoft Copilot controls provide a great example of what this looks like when properly executed.
Access to Copilot can be controlled based on someone's role within an organization, the sensitivity of the data they're trying to work with, and more.
Regular reviews and audits increase not only accountability but transparency, as well.
All this helps to make sure that people can enjoy all the benefits and potential of artificial intelligence with as few of the possibly catastrophic downsides as possible, which in and of itself is the most important goal of all.
If you'd like to find out more information about AI governance and the importance of establishing controls and protocols with a tool like Microsoft Copilot, or if you have any additional questions that you'd like to go over with someone in a bit more detail, please don't delay - contact us today.
