Artificial intelligence has quickly become a part of everyday work. Employees use AI tools to summarize documents, draft emails, analyze information, brainstorm ideas, research topics and perform countless other tasks. As these tools become more capable, they are also becoming more deeply connected to the systems and information businesses rely on every day.
That creates an important question for your organization: What happens to workplace information when it enters an AI system? And as AI continues to evolve in its capabilities, new questions are emerging about what it can do with the information it’s given.
Understanding the data security risks associated with AI use can help employees and organizations make more informed decisions about adopting these emerging technologies.
What Information Is Going Into AI?
When people think about sensitive information, they may immediately think of things such as Social Security numbers, passwords or credit card information. But workplace information can be sensitive in many other ways.
Consider an employee who uses an AI tool to rewrite an email to a customer. The employee might paste the entire email thread, including the customer’s name, contact information and details about the customer’s account. Another employee might ask AI to summarize a business document containing financial projections, an upcoming product announcement, an internal strategy or a contract.
These situations don’t necessarily involve malicious behavior. The employee is simply trying to use AI to complete a task more efficiently. But the information included in the prompt or uploaded document may be information the organization would not ordinarily share outside the company.
The potential sensitivity of workplace information can extend to:
- Customer or client information
- Employee and personnel information
- Financial information
- Contracts and legal documents
- Business strategies and plans
- Proprietary processes and intellectual property
- Unreleased products, projects or announcements
- Source code and other technical information
The important point is that “not public” and “highly confidential” aren’t the only two categories of information. Organizations may have many types of internal information that employees may not immediately recognize as sensitive.
Where Does the Information Go?
One of the challenges with AI is that employees may not know exactly what happens after they enter information into a tool.
That doesn’t mean AI systems are inherently unsafe. Rather, different AI services can have different policies and practices concerning data retention, model training, human review, storage and other aspects of information handling. Those practices can also differ between consumer accounts and organizational or enterprise versions of the same service. For that reason, “AI” shouldn’t be treated as a single category of technology.
An employee using a personal account with a consumer AI service may be operating under different terms and protections than an employee using an AI assistant provided and managed by the organization.
The same principle applies when AI is built into other workplace software. An AI feature inside a company’s existing technology environment may have access to information based on the permissions and systems associated with that environment.
In other words, understanding AI data security requires asking not only which tool is being used, but also how that particular tool handles information and in which environment it operates.
The Rise of “Shadow AI”
Another challenge is that AI adoption isn’t necessarily happening through an organization’s official technology channels. Employees can easily discover and begin using AI tools on their own. An employee who needs to summarize a lengthy document or analyze a spreadsheet may find an AI service online and begin using it within minutes. This type of unsanctioned or unmonitored AI use is sometimes called “shadow AI.”
The concern isn’t necessarily that employees are doing something wrong. In many cases, they’re responding to a legitimate need and finding a tool that helps them do their jobs more efficiently. The challenge for an organization is visibility.
Does your organization know:
- Which AI tools employees are using?
- What types of information are being entered?
- Whether employees are using personal or organizational accounts?
- Whether the AI tools being used are connected to other workplace systems?
Research from Harmonic Security, examining more than 22 million enterprise AI prompts and file uploads in 2025, found significant use of tools outside organizations’ official AI subscriptions. Its analysis also found that a portion of uploaded files contained sensitive information.
The broad takeaway from these findings is straightforward: AI use can become widespread before an organization has a complete picture of how its employees use it.
From Chatbot to Assistant
There’s another development that complicates the issue: AI is increasingly moving beyond the traditional chatbot model. With a conventional chatbot, a person enters a question or provides information, and the AI generates a response.
Newer AI assistants and “agents,” broadly referred to as “agentic AI,” can be connected to other applications and information sources. Depending on the system, an AI agent might be able to work with and thus access email, calendars, documents, databases or business applications. And it can happen with the click of a button.
Given these advancements, “What information did someone give AI?” is no longer the only question worth asking. Organizations increasingly need to ask, “What information can AI access?” and eventually, “What can AI do with that access?”
By and large, AI agents don’t have unrestricted access to an organization’s information. Access depends on the specific system, permissions and configuration. Even so, understanding that AI agents require access to certain systems is important as AI becomes less of a standalone tool and more of an intermediary between employees and their organization’s data.
The National Institute of Standards and Technology’s (NIST) guidance on generative AI risks and the Open Worldwide Application Security Project’s (OWASP) work on AI security both recognize that these systems introduce considerations involving privacy, information disclosure, access and the use of AI with other tools and systems.
AI Changes the Way Information Moves
Traditional data-security discussions often focus on who can access information and where that information is stored. AI adds another dimension: how information can move through a system while someone is trying to accomplish a task.
An employee might take information from an internal document, enter it into an AI tool, receive a summary and then incorporate that summary into another document. Another employee might ask an AI assistant to find information across several files or applications.
While these capabilities are part of what makes AI useful, they make it increasingly important to understand the path information can take when AI is involved.
Consider these three questions:
- What information am I giving AI?
- How does the system handle that information?
- What information can this AI system access or act on?
Those questions don’t require an employee to understand how an AI model works under the hood. They simply encourage greater awareness of how information interacts with the technology.
New Technologies, Old Problems
It’s also worth remembering that many AI-related data-security concerns are extensions of issues organizations have dealt with for years. Businesses have always had to consider who can access sensitive information, how information is shared with third parties, how long information is retained and how employees handle company data. AI creates new ways for those familiar issues to arise.
A confidential document can be emailed to the wrong person. It can be uploaded to the wrong website. It can be saved in an inappropriate location. And now, it can potentially be submitted to an AI system while an employee is trying to improve, summarize or analyze it.
The technology is new and evolving quickly, but the underlying principle is familiar: Organizations need to understand where their information is going and how it is being used.
Establishing an AI Use Policy
For organizations that haven’t yet established an AI use policy, the process doesn’t need to be daunting. Start by addressing a few fundamental questions about appropriate AI use and clarifying your expectations.
- Define the Purpose: Explain how AI can benefit employees and the organization, while establishing reasonable boundaries. A policy should promote responsible use rather than focusing on prohibiting certain activities.
- Identify Prohibited Information: Clearly identify sensitive information requiring additional security, such as customer or employee data, confidential business information, financial or legal documents, intellectual property and credentials.
- Clarify Approved Tools: Different AI services have different terms, features and data practices. Consider identifying approved tools or establishing a process for evaluating new services before employees use them for work.
- Address “Shadow AI” Usage: Acknowledge that employees may already be using AI tools independently. Establish clear expectations for personal accounts and provide a means for employees to ask questions about new or unapproved tools.
- Consider Access and Integrations: As AI becomes integrated with email, documents and other applications, consider what information AI can access and what actions it can perform.
- Establish Responsibility: Identify who is responsible for maintaining the policy and establish a process for reviewing it. AI technology and workplace practices are changing quickly, making regular policy reviews crucial.
A good AI use policy doesn’t have to anticipate the future. It only needs to establish a foundation for responsible use and leave room for the rules to adapt as the technology continues to evolve.
Conclusion
AI’s growing presence in the workplace makes a blanket approach difficult to sustain. Employees are finding legitimate ways to use these tools to save time, work with information and improve their output.
The more useful conversation is, therefore, not simply about whether employees should use AI. It is whether employees and organizations understand how they are using it.
That means recognizing that an AI tool can be more than a place to ask a question. It can be a destination for information, a source of information and, increasingly, a way of interacting with other workplace systems. As AI becomes a more ordinary part of the workday, understanding those distinctions will become just as important as understanding the technology’s capabilities.