Agency teams want AI to work on case files, benefits records, and personnel data, exactly the kinds of records they cannot afford to expose. And as government agencies move quickly beyond AI experimentation, the need to protect sensitive data in AI-enabled workflows is growing. According to the 2025 Federal Agency AI Use Case Inventory, 41 agencies reported 3,611 AI use cases, up roughly 70 percent from the 2,133 use cases in OMB's final 2024 inventory. Of those, 1,818 were in pilot or deployed status.
That growth signals a shift from experimentation to operational use. Federal, state, and local agencies are applying AI to workflows that support mission delivery, including citizen services, benefits administration, document review, software development, and knowledge management. But those workflows often depend on case files, citizen records, personnel information, financial data, and other sensitive information agencies are responsible for protecting.
This creates a practical tension. Leaders want AI to work with real operational data, but they cannot allow that data to be unnecessarily exposed to models, vendors, or downstream systems. To scale AI responsibly, agencies need controls that protect sensitive information across the full workflow while preserving the context AI needs to produce useful results.
AI Workflows Are Outgrowing Traditional Controls
Early conversations about AI adoption centered on model performance: which model was most accurate, produced the best responses, or automated the most work.
As AI becomes part of day-to-day operations, the focus is widening. Model quality still matters, but so do data governance, privacy, and control.
AI workflows extend well beyond the prompt. Sensitive information may move through applications, data sources, agents, tools, and models before a response is generated. Applying consistent governance across that entire path is increasingly difficult.
Traditional controls were not built for this environment. Many protect individual applications or isolated AI interactions, forcing organizations to recreate similar safeguards across workflows and environments. The question for technology leaders is no longer simply whether they trust a model. It is whether they can protect data consistently wherever an AI workflow carries it.
That need becomes more urgent as agencies seek flexibility across cloud-hosted, on-premises, and locally deployed AI environments. Scaling AI across those environments requires a more centralized approach to protecting sensitive information.
Why Removing Sensitive Data Is Not Enough
A common response to privacy concerns is to remove sensitive information before data reaches an AI system. That can reduce exposure, but it can also make the data less useful.
AI systems depend on context. They need to understand relationships among people, accounts, records, cases, and events. If sensitive values are deleted or replaced with generic placeholders, a model may lose the connections it needs to interpret the workflow accurately.
A March 2026 GAO report captured this tension between privacy protection and AI effectiveness. Experts convened by GAO noted that adjusting or removing data to improve privacy can come at a significant cost to AI performance.
For federal agencies, then, the goal is not simply to hide sensitive data. It is to protect that data while preserving the relationships and context required for useful AI output.
Protect the Data, Preserve the Context
This requirement is changing how organizations think about privacy controls. Instead of building a separate protection mechanism into every application, agencies can establish a shared protection layer between supported AI applications and the models, agents, and tools they use.
DMI Q Lock is designed to serve as that layer. It identifies and secures sensitive data so it is unavailable to external or unapproved AI models. Q Lock provides a protection layer between supported applications and large language models: it replaces detected sensitive and private data with consistent tokens before a request reaches the model, then restores eligible values before the response is returned to the user. Because protection happens between the application and the model, users can continue working in familiar tools without manually removing sensitive information from every request.
Before a request leaves the application, Q Lock detects supported personal, financial, account, access-key, and agency-defined identifiers. It replaces protected values with consistent, request-specific tokens. The same value receives the same token throughout the request, so the model can still distinguish among people, accounts, cases, and other entities and understand how they relate without receiving the original values. Eligible information can then be restored according to policy before the result returns to the application, while designated identifiers can remain removed.
The approach extends beyond simple prompts. Q Lock is designed to protect supported files, structured data, streaming responses, function calls, agent interactions, tool use, and Model Context Protocol workflows. It can also connect supported applications to approved cloud, on-premises, and local models, giving agencies a consistent protection approach across different AI environments.
Administrators can configure protected endpoints and masking policies for different teams or workflows, add organization-specific identifiers such as case or policy numbers, and review what entered Q Lock, what the model received, and what returned. This gives agencies a centralized, customer-controlled way to apply privacy policies rather than recreating protection logic in every application.
Building the Control Layer Federal AI Needs
AI adoption is accelerating, but as agencies move beyond isolated pilots, privacy and governance are becoming operational issues.
Recent GAO research found that OMB's government-wide AI guidance does not fully address all the privacy-related risks and challenges experts identified. The report cited limited direction on evaluating AI models that contain sensitive information, and on the tools agencies can use to protect sensitive data when using AI.
The implication for technology leaders is clear. Governing AI is not only about choosing the right model. It is about establishing controls that allow AI to interact safely with the data that powers mission and agency operations.
Agencies that scale AI successfully will need more than additional models. They will need governance architectures that protect sensitive information consistently while preserving the context required for meaningful work.
The central challenge is not finding data for AI. It is making sensitive data usable without making it unnecessarily visible. Solving that problem is essential to realizing AI's value in government. DMI Q Lock lets you use AI on sensitive data, without exposing it to the model.
Looking for a Better Way to Protect Sensitive Data Across AI Workflows?
For agencies evaluating how to use sensitive data with AI while maintaining privacy, context, and control, book a demo at dminc.com/q-lock or contact our team to discuss an approach built for applications, models, and deployment environments across the AI workflow.
