Data Governance and AI
Artificial intelligence is quickly becoming a mainstream and indispensable productivity tool for enterprises of all sizes. Across nearly every industry, employees are adopting AI-driven chat agents to answer questions, generate reports, write code, transform text and help them perform tasks with unprecedented speed. While the value is real, the risks to corporate data are now emerging more clearly than ever. In our conversations with clients, we repeatedly hear the same pattern: employees experiment with AI freely, leadership assumes every one is being careful, and no one realizes just how much sensitive information is quietly flowing into publicly accessible large language models (LLMs).
Organizations must take a strategic approach to AI use in the enterprise to safeguard their critical information. The same security controls that were applied cloud platforms, email, storage, endpoints, network and identity must now be applied to AI. Without a disciplined AI governance strategy, enterprises risk disclosure of proprietary information, running afoul of legal and regulatory statutes, and revenue loss. This is no longer a theoretical concern. It is already happening in ways that most companies do not see.
To understand the risk, it helps to step into the mindset of an employee interacting with a chat agent. The LLM is helpful, fast, forgiving and available at any hour. The experience feels anonymous. The responses are instant. Employees quickly begin using it the same way they would talk to a coworker. Instead of thinking about data classification or confidentiality, they think about getting the fastest answer. A developer pastes a block of source code into a chatbot because a compiler error is blocking their progress. A project manager uploads a customer contract because they want a concise summary for an afternoon meeting. A finance analyst feeds next quarter’s forecast into an assistant to get help preparing a board slide. These actions happen every day inside companies of every size.
The problem is that most organizations have not created any boundaries around this behavior. Employees can use any AI tool they like. Browser extensions, mobile apps, embedded AI inside search engines and even social media assistants all become potential destinations for sensitive data. The organization loses visibility because the traffic looks like normal web usage. Even more concerning, many LLMs store input data for quality improvement, abuse detection or future training. Some provide opt-out options, but users rarely enable them. In many enterprise environments, the default assumption is that employees will simply behave responsibly.
We are now seeing the consequences. Security teams are beginning to uncover incidents where sensitive content was pasted into public chat agents. Some involve proprietary architecture diagrams. Others involve confidential contracts or personal employee details. One well-known case involved a manufacturer where engineers pasted proprietary source code into ChatGPT three separate times in one week. Another case involved a healthcare organization whose staff inadvertently shared deidentified but still sensitive medical narratives with a public model. Most of these incidents only came to light because someone happened to notice unusual behavior. Without monitoring tools, most companies would have no idea.
This is why AI data loss prevention has become a critical new discipline. It is not enough to secure files, databases and emails. Organizations must now secure the conversations employees are having with AI systems. The first step is process. A company needs a clear AI acceptable‑use policy that defines what data may be shared with AI tools, which tools are approved, what restrictions apply, how users should escalate concerns, and what the consequences are for misuse of AI tools. Policies need to be written in plain language and reviewed regularly. They should explain not just the rules, but the reasons behind them, because employees will not follow policies they do not understand.
The second step is governance. Companies should establish an internal AI review committee or steering group responsible for evaluating new AI tools, approving their use and providing ongoing oversight. This group should include IT, security, legal, HR and operations. They should define risk tiers for different types of AI applications and apply consistent review criteria, such as data retention assurances, logging availability, privacy controls and contractual protections.
The third step is technical enforcement. Fortunately, major cloud platforms have now released tools that can help. Organizations using Microsoft 365 can leverage Purview to classify and restrict data, monitor risky AI usage patterns and even block unsanctioned AI websites. Purview’s data loss prevention capabilities can detect when employees attempt to paste sensitive information into browser-based chat tools and either warn them or block the action. Conditional Access App Control in Microsoft Defender for Cloud Apps can block or monitor interaction with unauthorized AI services. For companies using Google Workspace, data protection and context-aware access controls can limit what data is accessible from AI-enabled features, and Google’s security dashboards can help track interactions with unapproved AI systems.
Companies can also deploy secure enterprise-grade AI platforms, such as Microsoft Copilot for M365 or Google Gemini Enterprise, which offer strong contractual protections, identity integration and strict data isolation. These platforms ensure that company data is not used to train public models, and that employee queries are retained only within the organization. When organizations provide sanctioned AI tools with clear protections, employees are far less likely to seek out uncontrolled public alternatives.
Another emerging risk comes from AI integration into search engines. Tools like Bing, Google Search Generative Experience and others embed generative responses directly into search results. Users may not even realize they are interacting with an LLM. This creates scenarios where sensitive queries end up in a search engine’s model rather than a controlled enterprise chatbot. Organizations need to consider whether search-based AI features should be controlled, blocked or allowed only through enterprise accounts with data protection guarantees.
Beyond these steps, companies must also anticipate the next wave of AI risks. Browser-based embedded chat agents will soon appear in productivity suites, web applications and even chat platforms. Some AI agents will be able to take actions on behalf of users. Others will be integrated into operating systems. Each of these developments increases the potential surface area for data leakage. Governance must be forward-looking, not reactive.
For organizations looking to mature quickly, an AI risk assessment is a powerful starting point. This assessment identifies where data is currently flowing, which AI tools employees are using, what controls already exist and where the gaps are. From there, companies can prioritize actions such as implementing DLP policies, deploying enterprise AI tools, rolling out employee training and establishing a governance structure.
AI is becoming an integral part of the modern workplace, but without guardrails it can become a silent source of data loss. Companies that take action now can safely embrace AI innovation while protecting intellectual property, client confidentiality and regulatory obligations. Those that delay will eventually find themselves reacting to incidents that could have been prevented.
The message for every organization is clear. AI is here to stay, and employees will continue using it whether policies exist or not. It is the responsibility of leadership to guide this adoption safely. The sooner companies define their AI governance framework, deploy technical controls and educate their workforce, the more confidently they can take advantage of AI’s transformative capabilities.
Forrest Palamountain – Information Security Manager, Tech Heads Inc. – CISSP