What is AI Usage Control?
AI Usage Control, or AI-UC, is a Gartner-defined market for technologies that discover employee use of third-party AI applications, assess the risk of that usage, and enable security policy enforcement. Gartner defines it as follows: “AI usage control tools discover AI usage, assess risk and enable security policy enforcement for employee use of third-party AI applications.”
In practice, AI usage control can include discovering and cataloging AI applications, including shadow AI; assessing the risk of tools and usage patterns; inspecting content for sensitive data; defining granular policies for how AI can be used; enforcing those policies through browser, endpoint, identity, network, or other inspection points; and generating alerts when AI activity falls outside expected policy.
This is more than an approved-tool list. The point is to connect discovery, risk, and enforcement so security teams can operationalize policy.
Why AI Usage Control is becoming its own market
Employee AI adoption moved faster than most enterprise-control programs. Teams began using consumer and enterprise AI applications before security had a stable inventory, and new AI features now appear in tools already approved for other reasons.
Traditional discovery tools can identify applications, but they often lack AI-specific context. A browser or CASB signal might show that someone visited an AI service. It may not explain which model was used, what data was shared, which AI feature was enabled, or whether the activity introduced a different class of risk.
Gartner formalized AI Usage Control as a dedicated market because those questions are now common enough to require specialized controls, not a generic SaaS-discovery workflow.
AI Usage Control vs. AI Workspace Security
AI Usage Control and AI Workspace Security overlap substantially, but they should not be treated as synonyms.
- AI Usage Control: Gartner defines AI Usage Control around employee use of third-party AI applications, with discovery, risk assessment, content and data controls, and policy enforcement at the center.
- AI Workspace Security: Pluto’s AI Workspace Security vision includes the usage-control problem and then follows activity further into two additional areas: the ecosystem beneath AI tools, such as MCP servers, skills, plugins, and extensions, and the artifacts employees generate, such as applications, agents, and automations.
That is Pluto’s view of how the market is evolving, not a limitation Gartner places on every AI Usage Control product. Buyers should evaluate the actual coverage of each vendor rather than assume the category label alone defines its technical boundary.
What to look for in an AI Usage Control product
A useful evaluation should go beyond whether a product can block a website.
- Discovery depth: can it identify AI usage beyond a browser domain, including embedded AI features and unsanctioned tools?
- Risk context: does it understand why a particular use is risky, or only whether the application is on an approved list?
- Data controls: can it identify sensitive content moving into or out of AI applications?
- Policy granularity: can security allow one workflow while blocking another, or is enforcement binary?
- User experience: what happens when a policy blocks an action? A clear explanation and approved alternative usually creates less workaround behavior than a silent denial.
- Integration model: does enforcement require another endpoint agent, or can it use infrastructure the organization already runs?
FAQs
1. Is AI Usage Control the same as an AI acceptable use policy?
No. An acceptable use policy is the written rule. AI Usage Control provides technical capabilities to discover activity, assess risk, and enforce that rule in practice.
2. Is AI Usage Control the same as DLP?
No. DLP is focused on protecting sensitive data. Data inspection can be part of AI Usage Control, but the category also includes AI discovery, risk assessment, usage policy, and enforcement.
3. Does AI Usage Control cover shadow AI?
Yes. Automatic discovery and cataloging of unapproved AI tools is one of the core l use cases associated with AI usage control.
4. Who buys AI Usage Control?
Typical stakeholders include CISOs, security architecture and engineering teams, IT, privacy, compliance, and risk leaders responsible for enabling employee AI use without losing control of sensitive data or policy.