AI Governance
What Is AI Usage Control?
- Per-app decisions
- Point-of-use enforcement
- Evidence
Short answer
AI usage control is the technical enforcement of your AI rules at the point where people use AI: which apps are allowed, which accounts may be used, and what data may be sent. It turns a policy document into decisions made on real traffic, such as allow, mask, warn or block, with a record of each one.
Maskflare Engineering Updated
What does AI usage control look like in practice?
A finance analyst opens a free note-taking app with AI summaries, pastes in a supplier contract and asks for the key dates. Nobody reviewed the app. The contract names two people and a bank account. A policy PDF can't see any of this happen. Usage control can, because it sits where the request is made.
The term is newer than "data loss prevention" and narrower than "AI governance". It answers three questions at the moment of use: which AI app is this, whose account is it, and what is being sent? Each answer maps to a decision.
What are the four layers of AI usage control?
| Layer | Question it answers | Typical decision |
|---|---|---|
| Discovery | Which AI apps are people using? | Add the app to the inventory |
| App policy | Is this app allowed, and with which account? | Approved, tolerated or blocked; company workspace yes, personal account no |
| Data policy | What may be sent to an allowed app? | Mask, redact, warn, block or only log |
| Evidence | What happened, and who decided? | Audit trail, justifications, exports |
Discovery without data policy gives you a nice inventory and no protection. Data policy without discovery protects the three tools you remembered and misses the thirty you didn't. Evidence without enforcement is a diary.
How is it different from a ban, a policy or DLP?
- A ban is one decision made once. It is easy to get around with a phone or a personal account, and it gives you no visibility. See what is shadow AI for why bans rarely hold.
- A written policy states intent. Usage control makes it enforceable and measurable. The two belong together: see what is an AI acceptable-use policy.
- Classic DLP was built for email, file shares and endpoints. It often blocks a whole message, which teaches people to work around it. AI usage control prefers responses that keep the work moving, like masking a value or asking for a reason. More in what is data loss prevention.
- AI governance is the wider management discipline: risk assessment, ownership, model inventory, oversight. Usage control is one of the technical tools inside it. See what is AI governance.
What responses should usage control offer?
A control with only "allow" and "block" gets switched off the first time it blocks the CEO's slide deck. Useful usage control has a graded set of responses so you can match the response to the risk:
- Monitor: record what would have happened, change nothing. The right start for any rollout.
- Mask: replace personal data and secrets with tokens and send the prompt. Good for identifiers the AI doesn't need.
- Warn with justification: hold the prompt, say what was found, let the person decide. Good for data people sometimes legitimately need to share. See what is warn and justify.
- Block: refuse. Right for secrets, card numbers and apps you have decided against.
How does Maskflare fit in?
Maskflare implements the usage-control layers for people on managed devices. AI app discovery lists the AI apps seen on devices with the desktop agent, with users, devices, first and last seen, upload volume and a risk score, and you mark each app approved, tolerated or blocked. It records metadata only: no prompts, no page addresses, no general browsing. It can tell company workspaces from personal accounts in ChatGPT and Claude, so you can allow one and treat the other more strictly.
For the data layer, the Workforce agent routes AI traffic through Maskflare, where rules built on 574 detectors mask personal data and secrets before the request reaches the provider. User coaching adds the warn response, and the console holds rules, four-eyes approvals and the audit trail.
What can't AI usage control do?
- It can't see unmanaged devices. Discovery and coaching need the desktop agent on managed devices. Personal phones and unmanaged laptops are out of reach.
- It doesn't read what was typed during discovery. Discovery records which app was used, not the content. Masking and coaching act on content only for covered AI traffic.
- It isn't a model inventory or risk register. Maskflare doesn't track the AI systems you build, assess their risk class or manage vendor contracts. That work sits in your governance process.
- It doesn't replace training or judgment. A control that explains itself helps people learn, but it isn't a substitute for them knowing the rules.
A sensible rollout starts in monitor mode, reviews the inventory, decides per app, then moves the categories that matter from monitor to mask, warn or block. For agents and tool servers the same idea applies; see what is MCP security.
Frequently asked questions
Is AI usage control the same as AI governance?
No. AI governance is the whole management discipline, including risk assessment, ownership and oversight. Usage control is the technical enforcement of rules on how people use AI tools day to day.
Does AI usage control mean blocking ChatGPT?
Not necessarily. Most teams allow the tools people need and control the data going in, with masking or warnings. Blocking is reserved for apps you have decided against and for secrets.
Can usage control work without installing anything on laptops?
Partly. Gateways and SDKs control applications you build. Controlling what employees do in consumer AI apps needs something on the device or browser, such as the Maskflare desktop agent on managed devices.
Does Maskflare record what employees type?
App discovery records metadata only: the app, device, user, time and upload volume. When masking applies, Maskflare handles the prompt content in flight to find sensitive values, but the console shows counts and categories, not the values.
Where should we start?
Start in monitor mode on a pilot group, review which apps appear, decide approved, tolerated or blocked per app, and then turn on masking or warnings for the data categories that matter most.