As the year winds down and the first days of December arrive, many organizations begin reflecting on their technology strategies. This is often the moment where leaders examine what worked, what needs improvement, and what requires immediate attention before the new year. One area that needs a closer look is the way employees are using generative AI.

In 2025, tools like ChatGPT, Gemini, and Microsoft Copilot became deeply embedded in everyday workflows. They make tasks faster and more efficient, which is why adoption has grown so quickly. However, this convenience has created a new category of data exposure that most organizations cannot see happening. AI tools are now responsible for a significant portion of workplace data leaks, often without any malicious intent from employees. The risk comes from how the tools function and how easily sensitive information can be shared with them.

How AI Data Leaks Happen Inside Organizations

Most data leaks do not begin with a cyberattack. They start with an employee who is trying to solve a problem or save time. A user might paste a client budget into ChatGPT to reword it, or upload donor information to personalize outreach. A clinician may try to summarize patient notes with AI, or a legal assistant may use a chatbot to review sections of a contract.

These actions feel productive in the moment. The danger lies in what happens after the information is submitted. Public AI tools store input on external servers. This data cannot be retrieved, deleted, or audited by the organization. It may also be used to train future models if proper protections are not in place. Once the information leaves your network, it is no longer under your control.

This creates a serious problem for sectors that work with sensitive or regulated data. Many organizations are experiencing quiet, continuous data exposure without knowing it is taking place.

Why Traditional Cybersecurity Tools Cannot Detect AI Leaks

The reason AI data leaks are difficult to identify is that they look like normal web activity. When an employee uses an AI tool, the traffic moves through a standard browser connection. Most data loss prevention tools are not designed to inspect or block content that is pasted manually into a text field.

There is no warning to the IT team. There is no log of the interaction. There is no simple way to reverse the action. As a result, many organizations are relying on security systems that simply are not built for the new patterns created by generative AI.

The Risk for Regulated Industries

Organizations that operate in regulated environments face even more serious consequences from unmonitored AI use. Examples include:

Healthcare: Any transfer of patient information to an external AI tool can create a HIPAA violation.
Education: Student data is protected under FERPA, which restricts disclosure without consent.
Finance: GLBA and SEC requirements demand strict supervision and retention of client information.
Legal: Entering client data into AI can jeopardize confidentiality and privilege.
Nonprofits: Donor and beneficiary information often contains sensitive personal data.

In each of these sectors, an employee’s attempt to increase efficiency can unintentionally create compliance failures and long term exposure.

Why AI Governance Matters

Banning AI entirely is not an effective response. Employees will continue using AI because it improves productivity, and organizations that embrace AI responsibly will have a competitive advantage. The real solution is stronger AI governance. This includes:

Clear and written AI acceptable use policies
Training for employees on what information should never be shared with AI
Proper configuration of tools like Microsoft Copilot
Guardrails for data handling and retention
Monitoring that aligns with regulatory requirements

With the right policies and controls in place, AI can be used safely and strategically.

A Year End Opportunity to Assess Your AI Risk

December is an ideal moment to review your organization’s AI posture. Many teams have been using AI tools informally throughout the year, and the end of the year offers a natural opportunity to evaluate what is working and where risks may be forming.

Network Outsource is helping organizations understand their exposure through an AI Risk Gap Analysis. This assessment reveals how AI is currently being used, what information may be at risk, and which policies or configurations are needed to ensure safe and compliant AI adoption.

You will receive a clear view of your vulnerabilities and a prioritized roadmap for improvement.

If you want to begin 2026 with more confidence in your AI practices, this is the right time to take a closer look at your current usage. You can schedule an analysis here