Organizations are facing a new and urgent threat to their data security, and it doesn’t come from external hackers or sophisticated malware. Instead, it comes from shadow IT, the practice of workers using unauthorized artificial intelligence tools without IT oversight. When that happens, sensitive data can end up on third-party servers or even be leaked to the public.
Shadow AI Has Become a Major Data Security Problem
With the release of ChatGPT in November 2022, anyone with an internet connection could access the world’s most sophisticated large language model, and it took just a few days for it to reach a million users.
Today, employees can choose from an array of state-of-the-art AI models, including the latest version of OpenAI’s GPT, Anthropic’s Claude, Google’s Gemini, or xAI’s Grok. There are also countless tools that take advantage of these models, including Microsoft Copilot, which brings various OpenAI models directly into Office applications and the Windows operating system.
This accessibility, while transformative for productivity, created the perfect conditions for shadow AI to flourish. When an employee faces a tight deadline or complex problem, the temptation to paste that troublesome spreadsheet or confidential meeting notes into an AI tool becomes almost irresistible, especially because many AI tools are free, instantly available, and remarkably helpful. While AI adoption itself isn’t inherently problematic, it becomes a serious threat when it happens outside IT’s visibility and without proper security controls because it can lead to:
- Permanent data exposure: Information shared with consumer AI tools may be stored indefinitely, used to train future models, or retained in conversation logs accessible to the AI provider’s employees.
- Compliance violations: Sharing customer data, financial records, or health information with unauthorized AI platforms can violate CMMC, CCPA, HIPAA, GDPR, and other privacy regulations.
- Intellectual property theft: Proprietary code, trade secrets, business strategies, and competitive intelligence pasted into AI tools becomes part of that platform’s data ecosystem.
- Supply chain vulnerabilities: If an AI provider experiences a breach, your company’s sensitive information becomes collateral damage, even though you never had a direct relationship with that provider.
- Loss of control: When employees use AI tools without any approval, IT loses all visibility into what data is being shared, which platforms are being used, and who has access to company information. As a result, it becomes impossible to respond to audits, deletion requests, or security incidents.
These aren’t theoretical risks. In April 2023, Samsung discovered this firsthand when engineers at its semiconductor division inadvertently leaked sensitive source code and internal meeting recordings to ChatGPT while seeking help with their work. In response, Samsung banned all generative AI tools company-wide while scrambling to develop internal alternatives.
Moving From Shadow AI to Secure AI
Modern AI tools have become incredibly useful for businesses, and they’re likely to become just as essential for staying competitive as computers were in previous decades. The fact that so many employees are already embracing AI tools on their own is compelling evidence of this transformation. The challenge for organizations isn’t to stop this momentum but to channel it safely so that AI doesn’t live in the shadows.
Step 1: Understanding Your Compliance Obligations
Before implementing any AI solution, organizations must understand their specific compliance obligations and how these affect their AI options. Different industries and data types come with varying levels of regulatory requirements, and what works for one business may be completely off-limits for another.
For organizations with these stricter compliance obligations, most consumer AI tools are simply not an option. Free versions of ChatGPT, Claude, or Gemini lack the necessary security controls, audit trails, and data handling agreements required by these regulations. Even paid versions may fall short of requirements for data residency, encryption standards, or third-party assessments.
The good news is that compliance-focused AI solutions do exist. For example, government contractors who must meet NIST SP 800-171 standards and achieve CMMC certification to handle Controlled Unclassified Information (CUI) can turn to Microsoft Security Copilot instead of regular Copilot. While Security Copilot isn’t a certified compliance solution by itself, it provides critical tools and functionalities that support compliance efforts:
- Access Control Enforcement: Security Copilot monitors user activities, identifies unauthorized access attempts, and suggests corrective actions. It integrates with Microsoft Entra ID to manage Conditional Access policies, helping meet NIST’s Access Control (3.1) requirements.
- Threat Detection and Response: By analyzing data across Microsoft 365 and Azure Government environments, Security Copilot provides insights into potential security incidents involving CUI to support System and Information Integrity (3.14) requirements.
- Policy Management: Administrators can create and manage endpoint policies through Security Copilot and make sure devices accessing CUI comply with organizational security standards.
Importantly, these capabilities operate within Microsoft’s assessed and compliant cloud environments, such as Azure Government and Office 365 U.S. Government Community Cloud (GCC), so they provide the in-scope infrastructure necessary for organizations to meet CMMC compliance requirements.
Step 2: Discover What AI Tools Your Employees Are Already Using
Most leaders would be shocked to learn how many AI tools their employees are already using. A recent survey found that 75% of knowledge workers use AI in their daily work, but many do so without official permission. That’s why it’s important to understand your current AI footprint before you can secure it.
You can start with a non-punitive employee survey. Frame it as an opportunity to understand which tools help your team be more productive, not as an investigation. Ask specific questions about which AI tools they use, what types of information they input, and which tasks these tools help them complete.
You should also work with your IT team to check for technical indicators of AI usage. Review browser histories on company devices and monitor network traffic to popular AI platforms. Many organizations discover that actual AI usage is three to five times higher than management assumed.
The goal isn’t to punish employees but to understand reality so you can manage it effectively. Most workers don’t realize they’re creating security risks; they’re simply trying to do their jobs better.
Step 3: Establish Clear Guidelines for Safe AI Use
Once you understand your compliance requirements and current AI usage, it’s time to create clear policies that protect your business while allowing employees to benefit from AI’s productivity gains. Here, the most important thing is to define what types of data can never be shared with AI tools. Be explicit about categories like:
- Customer personal information (names, addresses, SSNs, credit card numbers)
- Employee records and HR data
- Financial statements and internal reports
- Proprietary source code or algorithms
- Strategic plans and confidential business information
- Any data covered by CMMC, HIPAA, CCPA, or other regulations
Next, specify which AI tools are approved for use and under what circumstances. Include practical examples employees can relate to, such as:
- Acceptable: Using approved AI like Microsoft Copilot in Word to help write a general marketing blog post about industry trends.
- Not Acceptable: Uploading customer purchase history and personal details to have AI analyze buying patterns and create sales forecasts.
Of course, no policy can cover every possible scenario, and shadow AI use cases evolve rapidly. That’s why it’s important to establish clear consultation channels where employees can get quick answers before using AI in new ways. All you really need to do is designate specific people—whether in IT, legal, or management—as AI advisors who can respond to questions promptly.
Finally, you should outline the consequences for policy violations, but focus on education over punishment so that employees view policies as protection rather than restriction.
Conclusion
The risks of shadow AI are real, but the solution isn’t to ban AI entirely without even considering the competitive disadvantage that would create. Smart organizations recognize that AI tools are becoming as essential as email or spreadsheets. The question is how to adopt them safely, and the answer is the steps outlined in this guide. If you need help implementing them, then you can contact us at OSIbeyond today and schedule a free consultation.