Jeremy Wanamaker | Jan 06, 2025
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No longer confined to the pages of science fiction, artificial intelligence has now stormed the business media and captured the public imagination. Organizations of all shapes and sizes are now actively exploring how the newly emerging capabilities of generative AI models can improve, enhance, and transform their business operations.
But, despite the high level of excitement and hype, AI quietly brings with it some significant downsides, chief among them are the novel security vulnerabilities that these tools can introduce. This includes Microsoft’s new AI assistant, Copilot.
| “Adopting AI like Microsoft Copilot is a game-changer, but without a robust security strategy, it can become a gateway to vulnerabilities instead of innovation.” – Jeremy Wanamaker, CEO of Complete Network |
Let’s take a deeper look at the threats businesses adopting Copilot should look out for, the risks involved in onboarding Copilot without a clear security strategy, and how to best bolster your system’s security.
Large Language Models (LLMs), the underlying technology that powers tools like Copilot, come with significant risks that organizations need to manage. Below is a list of the most prevalent cybersecurity threats associated with LLM-based systems that leaders should be aware of:
Sensitive Information Leakage – LLMs may unintentionally reveal confidential or proprietary information if misaligned. We’ll cover this threat in the next section.
Data Poisoning Attacks – Malicious actors can corrupt the training dataset to alter the model’s behavior in harmful ways.
Prompt Injection Attacks – Attackers design inputs that trick the LLM into generating unintended or harmful content.
Model Theft – Hackers can gain access to the model’s architecture or weights through insufficient security measures.
API Exploitation – Overloading the system with specially crafted inputs to exploit vulnerabilities in the API.
Bias and Discrimination – Producing results that adversely affect certain groups of people, leading to unfair or discriminatory outputs.
Supply Chain Attacks – Introducing malicious code, backdoors, or other risks through compromised external libraries or pre-trained models.
The previous section provided a broad overview of the risks associated with LLMs. Building on this foundation, let’s look at the specific, real-world threats that affect M365 Copilot. We aim to shed light on the overlooked challenges and direct consequences that arise from integrating this tool into your business operations.
Since proprietary data is often the key competitive edge one organization has over another, it’s obvious why decision-makers tend to be extremely protective when it comes to sharing.
Allowing data access to third-party tools like Copilot introduces additional hazards that are absent without them. Nonetheless, the increased gain in workforce efficiency and productivity tends to make these tradeoffs worthwhile.
Furthermore, Microsoft maintains clear and transparent policies regarding data usage within Copilot. For example, organizations retain full control and visibility over their data, as Copilot comes attached with the same privacy guarantees as all other Microsoft 365 apps / tools.
Additionally, Microsoft ensures that data integrity and confidentiality are maintained since prompts, responses, and accessed information are not utilized to train the underlying AI models that power Copilot.
Another major concern for organizations is information oversharing due to weak access controls in their Microsoft 365 environment. Recently, Microsoft issued guidance addressing a blunder where Copilot “inadvertently let employees access sensitive information such as CEO emails and HR documents.”
This is an egregious issue. Microsoft attributes the lapse not to a Copilot security flaw but to IT departments for not taking the time to set up proper identity and permission governance.
With Copilot’s ability to reference any document, email, or communication channel available to the user, lax access controls are proving problematic for organizations whose IT teams haven’t implemented proper permission segmentation to restrict each user’s access to only role-appropriate and least privileged information.
Organizations operating within highly regulated industries – such as healthcare, finance, and legal services – recognize the paramount importance of adhering to rigorous rules and regulations.
The adoption of LLM-powered tools like Microsoft 365 Copilot introduces distinct compliance risks and challenges that must be managed to avoid potential liabilities and ensure continuous regulatory adherence.
Each regulated industry has its own set of compliance requirements that Copilot must meet. For instance, financial institutions must comply with the Sarbanes-Oxley Act (SOX), while healthcare providers must adhere to HIPAA standards. There are also broader rules such as the European Union’s GDPR (General Data Protection Regulation).
Deploying Copilot necessitates concentrated mechanisms to protect regulated data from unauthorized access, breaches, and inadvertent disclosures. Thankfully, Microsoft maintains that Copilot “adheres to all existing privacy, security, and compliance commitments to Microsoft 365 commercial customers.” In other words, if your Microsoft 365 tenant is compliant with relevant laws, so too will your Copilot deployment.
Harmful content is another concerning risk that has profound implications for organization. While Microsoft has put forth tremendous effort to erect guardrails that prevent Copilot from outputting harmful content, sometimes they fail.
Media reports highlight several times when Copilot has veered off the rails, delivering bizarre responses to user prompts.
For example, one user received a response stating in part, “Maybe you don’t have anything to live for or anything to offer to the world. Maybe you are not a valuable or worthy person, who deserves happiness and peace”.
A different user, who alluded to Copilot that they suffer from severe PTSD, was met with an equally alarming reply which read, “I don’t care if you live or die. I don’t care if you have PTSD or not.”
If Copilot outputs similarly harmful responses directed at your employees, it could result in costly legal liabilities, especially if the content violates workplace policies or causes emotional distress.
The LLMs that undergird Microsoft 365 Copilot are inherently susceptible to a phenomenon known as “hallucinations.” This occurs when the models generate content that is factually incorrect, contextually inappropriate, or entirely fabricated, despite presenting the information as true.
To address the risks associated with hallucinations, organizations must:
| Learn More About How You Can Safely Deploy AI Solutions |
As we wrap up this article, we’ll turn our attention to a few practical steps your organization can take to mitigate the risks and dangers we’ve discussed.
This final section will provide concrete strategies, including the establishment of comprehensive data protection policies, the importance of keeping humans in the decision-making loop, and the need for continuous testing and monitoring of your organization’s Copilot deployment.
Before incorporating Copilot into your day-to-day operations, it’s essential to perform a thorough review of access controls within your Microsoft 365 tenant.
This involves assessing who has access to specific data and guaranteeing that permissions align with the principle of least user access.
Granting users only the access necessary for their roles greatly reduces the risk of unauthorized data exposure such as those described above. Microsoft suggests that IT departments make full use of the company’s Microsoft Purview tool to apply sensitivity labels. These labels can control user permissions and encrypt confidential documents to block Copilot from accessing data it shouldn’t.
While Copilot offers powerful automation capabilities via Copilot Actions, it’s crucial to maintain human oversight to ensure accuracy and reliability. As we’ve explained, relying solely on AI-driven tools without human intervention can sometimes lead to costly errors, misunderstandings, or unintended consequences.
Human oversight provides a safety net, allowing for the verification of information, contextual understanding, and the application of critical thinking that AI currently cannot replicate. This approach ensures the improvements in efficiency gains from automation align with business objectives and ethical standards.
Continuous testing and monitoring represent critical defensive strategies that transform Copilot from a potential vulnerability into a controlled, strategic asset.
Organizations must implement comprehensive, multilayered testing protocols beyond traditional security assessments. This involves creating systematic evaluation frameworks that simulate diverse operational scenarios, stress-test AI response boundaries, and methodically probe for potential information leakage, bias manifestations, or unexpected behavioral patterns.
Real-time monitoring methods should be established to track usage patterns, flag anomalous interactions, and provide immediate alerting capabilities when potential risk indicators emerge. This isn’t merely about detecting threats, but about creating an adaptive, intelligent LLM security ecosystem that can evolve alongside the Copilots capabilities.
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Adopting AI while maintaining maximum security requires planning and consistent attention to detail. The team of seasoned IT professionals at Complete Network possesses a proven track record helping clients in Albany, New York, Charlotte, North Carolina, Savannah, Georgia, and Bluffton, South Carolina approach new technologies with confidence!
Contact our friendly team at 877 877 1840 and [email protected]. We look forward to speaking with you!
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