Navigating the Security Landscape of AI in Microsoft 365

Man using laptop in public facility

According to one recent study, the average cost of a cyber attack (and the resulting losses) on a business hit $4.45 million in 2024.

The bad news is that this is an all-time high.

The healthcare industry alone suffered data breaches that hit an average loss of $10.93 million annually.

The worse news is that this problem is only going to get more severe as we venture into the technological "Wild West" that is our artificial intelligence-driven future.

If it seems like AI is everywhere these days, you're essentially correct. In early 2023, Microsoft announced Copilot - an AI assistant that would be built directly into apps like Word and Excel that was poised to change the way we work, forever.

There are many doctors, private practices, community banks, and credit unions that rely on Microsoft 365 every day to be as productive as possible.

They're also among the most targeted industries for cyber attacks thanks to the value of the financial data that can be stolen.

That's why AI cybersecurity is a topic that absolutely everyone should be talking about.

You're about to take organizations that are already inherently vulnerable to cyber threats and make their lives potentially even more dangerous in the name of also making them more convenient or productive.

Navigating the security landscape of AI in Microsoft 365 is not something that will happen automatically.

But with the right approach, Copilot data protection can be a lot easier to achieve than one might think.

The Importance of Data Security in AI Tools

As is true with any tool but especially with one built on generative AI like Copilot, there are a number of risks associated with artificial intelligence - particularly when it comes to data handling.

One of the biggest of them involves the privacy violations that come with unauthorized use.

Think about it - in a completely normal enterprise, if someone is able to access sensitive patient information or financial documents without authorization, your organization would be subject to fairly hefty fines and penalties.

In the case of HIPAA, this could include legitimate jail time.

None of that changes just because someone was able to "trick" Copilot into giving them access to data they shouldn't have been able to see thanks to a particularly clever prompt or command.

Another major risk associated with this type of artificial intelligence has to do with an overall lack of transparency.

In AI systems powered by deep learning models in particular, it can sometimes be difficult to know exactly where your data is at any given moment.

Everything you need is at your fingertips, yes - but what is really going on behind the scenes?

Again, there are certain industries like healthcare that are literally forbidden by law to store data in certain locations.

How can you be sure you're in compliance if you don't actually know where your data is?

How can you know exactly what risks you're exposed to if you have no transparency into the data itself?

Algorithmic bias is another potential risk of AI-based solutions that is absolutely worth discussing.

Remember that large language models like the kind that power Copilot need to be trained on existing data.

The tool is ultimately only as "good" (or at least, as effective) as the data you choose to feed it.

If the data the algorithm is being trained on is biased for or against certain groups of people, any actions executed by a tool like Copilot will be as well.

This might not represent a potential security risk in the same way that an actual data breach would, but it could have long-term negative implications regarding how a business operates and interacts with the people it is supposed to be serving.

These are just a few of the many reasons why data security is a critical component in any AI implementation.

Just because a tool like Copilot comes from Microsoft 365 - a tool you're already familiar with and are comfortable using - doesn't mean these things are handled automatically.

It still requires proactive steps on behalf of the user to avoid these and other potential pitfalls in the future.

Security Features of Microsoft 365's Copilot

Thankfully for Microsoft 365 Copilot users, it's clear that the technology giant is taking AI cybersecurity very seriously.

The company's own support documentation indicates that Copilot is compliant with all existing Microsoft 365 privacy and security requirements, including ones like HIPAA, the General Data Protection Regulation (GDPR), and the European Union (EU) Data Boundary.

One major example of this has to do with how prompts, responses, and even any data being accessed through Microsoft Graph will not be used to train the large language models (LLMs) upon which Copilot is built.

That data remains protected, even internally, to dramatically reduce the risk that it might become unintentionally compromised.

As is true with Microsoft 365 security in general, Copilot uses sophisticated service-side technology to make sure that customer content is always encrypted both at rest and in transit.

"At rest" encryption has to do with whenever data is sitting on a cloud-based server somewhere, regardless of whether it is being accessed.

In these instances, Microsoft employs sophisticated physical security protocols and background screening techniques, in addition to multi-layered encryption, to keep that information safe.

Even when data is being accessed (and is either being uploaded to or downloaded from the Internet), technologies are still used like BitLocker, Transport Layer Security (TLS), and Internet Protocol Security (IPSec).

This encrypts data on a per-file basis, so even in the unlikely event that something is compromised, the exposure is still far smaller than it otherwise would be.

Common Security Threats and How to Mitigate Them

One of the most common types of security threats to be aware of when talking about AI technology like Microsoft 365 has to do with unintentional system exploits.

These are when a system is used in a way that essentially leads to "unintended consequences."

Something called "reward hacking" is a prime example of this.

Some AI models are trained to maximize rewards.

In that case, users could manipulate the system to achieve certain rewards without actually performing the intended task.

Another common security threat has to do with something called "poisoning."

This is when a system is essentially trained on corrupted data, sometimes intentionally.

If low quality or otherwise incorrect data is exposed to the system by someone with malicious intent, they can essentially build a "backdoor" into the model.

You're training an AI tool to perform tasks that run contrary to goals like preserving privacy, data integrity, and security.

At that point, all you have to do is sit and wait for an opportune moment to strike before the target even realizes they have a problem at all.

One of the major ways to mitigate threats like these is, somewhat ironically, found in AI itself.

AI cybersecurity scanners exist that use machine learning algorithms to wade through massive volumes of data to specifically look out for risks that you might be exposed to.

They learn from identifying new threats and attack strategies, putting a stop to them before they have a chance to be exploited later on.

If nothing else, they're a great way to help on your organization's quest for continuous improvement.

They can help you identify certain issues, even small ones, that may have otherwise gone undiscovered.

Best Practices for Secure AI Utilization

To meet your AI cybersecurity needs, one of the most important things to do is to sit down and develop a series of guidelines for the secure deployment of any AI tools like Microsoft 365 Copilot your organization will be using.

All key stakeholders need to educate themselves about how the technology works.

Don't begin and end your research with the marketing collateral.

Really make an effort to understand the principles, limitations, and potential biases that artificial intelligence will always bring with it.

Likewise, any content or actions generated by Copilot need to be carefully tested and reviewed.

Figure out what works and, more importantly, what doesn't.

Implement any changes based on objective facts.

Beyond that, always use tools like Copilot in a way that adheres with AI cybersecurity best practices.

Copilot should only access those data sources and other resources that you expressly give it permission for.

Those permissions should be determined based on logical user access controls, as well as any regulations that your organization may need to adhere to.

For the best results, you should also institute regular security assessments and employee training sessions moving forward.

Regular security assessments help you understand where you're protected in terms of cybersecurity and where you might come up short.

You can't fix problems if you're unaware that they exist in the first place.

Security assessments help to clear up those blind spots.

Employee training helps to make sure everyone is on the same page regarding how Copilot should and should not be used.

If you don't want people to utilize Copilot in a way that puts the entire organization at risk, they need to understand the types of threats that they'll be exposed to and how to take steps to avoid them.

In essence, never let yourself forget that AI cybersecurity is not something you "do once and forget about."

As the technology evolves, so will the skills of the people trying to exploit it.

At that point, it is up to you and your people to evolve, too.

You need to be proactive about staying one step ahead of those who wish to do you harm.

Doing so is the only way you'll be able to protect everything you've worked so hard to build up to this point.

The AI-Driven Future Is Here

According to one recent study, the global AI market will grow from a value of $241.8 billion in 2023 to a staggering $740 billion by 2030.

It's been estimated that artificial intelligence could literally add $25.6 trillion to the worldwide economy.

Statistics like these paint a vivid picture of not just how far we've come, but where it's all likely headed as well.

But nothing in the technology world is infallible - least of all artificial intelligence.

There are still very real (and unfortunately common) threats that people need to take steps to protect themselves from.

Potential issues like data theft, deliberate attacks, insider threats, and even adversarial machine learning.

All this is especially concerning when you consider that solutions like Microsoft 365 and Copilot are about to increase our collective risk surface exponentially.

Because of that, these tools always need to be deployed with extreme care and caution - even when providers like Microsoft promise to make it "effortless."

It's not.

Don't let yourself fall into the trap of believing it is.

Your organization must sit down and come up with a set of forward-thinking guidelines for the secure deployment of AI tools like Copilot.

You must conduct regular security assessments so that you know what your risks are and how to mitigate them in the event of a "worst case scenario."

You must invest in employee training because your workers are and will always be your first line of defense in these matters.

Key strategies and best practices like these might not totally eliminate the risk of living in the AI-driven world.

But they can help you enjoy all the benefits of this period with as few of the potential downsides as possible.

If you'd like to find out more information about navigating the security landscape of AI in Microsoft 365, or if you'd just like to discuss artificial intelligence in general and what it might mean for your organization, please don't delay - contact us today. 

New Call-to-action

Read On