AI usage control (AIUC) is an emerging enterprise security category focused on discovering, monitoring, and governing third-party artificial intelligence tools and AI agents in real time. It determines which AI tools are allowed, what data users can share with them, which models can access sensitive systems, and what actions AI applications can perform.
These controls can be enforced through identity and access management, data loss prevention, model gateways, application permissions, logging, and approval workflows. The goal is to reduce risks such as sensitive data exposure, unauthorized AI use, and uncontrolled automated actions while still allowing approved AI applications to operate.
Types of AI usage controls include:
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Employees can adopt public AI services without security or IT approval, creating shadow AI. These tools may process company data outside approved environments, use unknown retention policies, or lack required security controls. Teams may also create accounts with personal credentials, making it harder to track which services are being used and what information is being shared.
How AI usage controls help: They help organizations identify unapproved services, restrict access where needed, and provide approved alternatives. They can also enforce sign-in requirements, block risky applications, and steer users toward sanctioned tools that meet security, privacy, and compliance requirements.
Users may paste source code, customer records, credentials, financial data, or internal documents into AI tools. AI applications can also access sensitive information through connected files, databases, or software-as-a-service platforms. This creates additional exposure paths because data can leave its original system through prompts, retrieved context, generated outputs, or tool integrations.
How AI usage controls help: They limit which data AI systems can receive and which sources they can access. Organizations can apply data classification rules, block sensitive inputs, redact specific fields, and restrict model access based on user identity. Logging and monitoring also help security teams investigate what data was sent to AI services and whether policies were violated.
AI agents can perform actions instead of only generating responses. They may send messages, modify records, execute code, call APIs, create tickets, or trigger workflows across connected systems. Because these actions can happen automatically and at machine speed, excessive permissions or incorrect instructions can have a larger operational impact than a typical user mistake.
How AI usage controls help: They can restrict the tools and systems available to an agent, limit the scope of its permissions, and require human approval for high-risk actions. Organizations can also set execution limits, isolate agents from sensitive environments, and record each action for auditing. These controls make it easier to identify who authorized an agent, what resources it used, and what changes it made.
Related content: Read our guide to agentic AI security
The first step is identifying which AI applications, models, browser extensions, APIs, and embedded AI features are being used across the organization. Discovery can combine:
This process should include both approved and unapproved services. It can also reveal AI features inside existing business applications that may not appear as separate tools. The resulting inventory gives security teams a baseline for assessing risk and deciding which services require additional controls.
Organizations need to know who or what is accessing AI services. This includes:
Identity information allows controls to be applied based on role, department, device posture, or access level. Agents should also have distinct identities rather than sharing human credentials. This makes their permissions easier to limit and their actions easier to audit.
Not every AI interaction carries the same level of risk. Asking a public chatbot to summarize public information is different from allowing an agent to access customer records or production systems.
Organizations can classify AI use cases according to the data involved, the model being used, and the actions the system can perform. Common categories include:
Higher-risk use cases can then receive stricter controls.
AI usage policies specify which tools and activities are permitted. Policies can define:
Rules should be specific enough to enforce technically. For example, a policy might allow employees to use an approved model for public and internal data while blocking customer records, authentication secrets, or regulated information from being submitted.
Once policies are defined, organizations can enforce them at identity, endpoint, network, application, and data layers. Controls may:
Data controls can inspect prompts and uploads before they reach an AI service. They can block, redact, or warn users when sensitive information is detected. Agent permissions can also be limited so that AI systems can access only the resources and actions required for their assigned task.
Monitoring provides visibility into how AI systems are actually being used. Depending on privacy and regulatory requirements, organizations can record metadata or inspect:
This telemetry helps teams understand which models are used, what types of data are shared, and where risky patterns occur. Monitoring should also include AI-generated outputs when those outputs can expose sensitive data, introduce unsafe code, or trigger downstream actions.
AI usage controls can evaluate activity against predefined security and compliance rules. Examples include:
Behavioral signals can also help detect unusual activity. A sudden increase in AI uploads, access from an unmanaged device, or an agent making an unexpected sequence of API calls can trigger alerts, additional authentication, or automatic restrictions.
When a violation occurs, security teams need enough context to determine what happened and what data or systems were affected. Investigation records can include:
Remediation may involve revoking access, blocking a service, rotating exposed credentials, deleting shared data where possible, or reducing an agent's permissions. Incident findings can also be used to update policies and controls so similar activity is prevented or detected earlier.
AI application access controls determine which AI services users can reach. Organizations can allow approved tools, block unapproved applications, or provide different access levels based on business need. These controls can be enforced through:
They are useful for reducing shadow AI and limiting access to services that do not meet security, privacy, or compliance requirements.
Identity-based controls apply AI permissions according to the person, group, role, or service account making the request. For example, developers may be allowed to use coding assistants while other users receive access only to general-purpose tools. Policies can also consider:
AI agents should use dedicated identities with narrowly scoped permissions so their activity can be separated from human activity and audited independently.
Data loss prevention controls inspect information before it is sent to an AI service. They can detect sensitive content such as credentials, customer records, financial data, source code, intellectual property, or regulated information. When a policy is triggered, the system can:
These controls extend existing DLP policies to AI prompts, file uploads, and other model inputs.
Browser and endpoint controls manage AI activity directly on user devices. They can:
These controls are especially useful when users access AI tools through a web browser rather than a managed application. Endpoint posture can also be used as a condition, allowing sensitive AI workflows only from compliant corporate devices.
Related content: Read our article about browser agent security risks
Tenant and account restrictions ensure users access AI services through approved organizational accounts instead of personal or unmanaged accounts. This helps keep activity within environments where administrators can apply:
Organizations can also restrict which external tenants or workspaces users can access. This reduces the risk of employees accidentally sending company information to personal AI accounts or third-party environments outside corporate control.
File upload controls limit which files users can submit to AI tools. Policies can be based on file type, size, sensitivity label, content classification, storage location, or ownership. Organizations can block:
Similar controls can restrict links, clipboard content, and data shared through connected cloud storage, helping prevent sensitive information from entering AI systems through indirect channels.
Organizations should consider the following measures to improve their implementation of AI usage controls.
AI controls should reflect the level of risk in each interaction. A user accessing a public chatbot with non-sensitive data does not require the same restrictions as an AI agent connected to production systems or customer records. Policies can combine user role, device posture, data classification, application trust, and requested action.
This allows organizations to apply stricter controls only where the potential impact is higher, instead of treating every AI use case the same. Risk-based controls can also change dynamically. For example, an organization may allow a user to access an approved AI service but block the same session if the user uploads regulated data or connects from an unmanaged device. This makes controls more precise and reduces unnecessary disruption.
Key actions:
AI applications and agents should receive only the permissions they need to perform their assigned tasks. Avoid broad access to files, databases, SaaS platforms, APIs, or administrative functions unless it is required. Permissions should also be time-bound or task-specific where possible. Regular reviews can identify unused integrations, excessive scopes, and inherited permissions that increase the impact of a compromised or misconfigured AI system.
Least privilege is especially important for autonomous agents because they can perform actions without continuous human input. Organizations should restrict which tools agents can invoke, which records they can modify, and which environments they can access. High-impact capabilities such as deleting data, changing permissions, or deploying code may require explicit approval.
Key actions:
Employees should use approved corporate accounts for business-related AI activity. Personal accounts can bypass enterprise logging, retention settings, access controls, and contractual protections. Organizations can enforce tenant restrictions, corporate single sign-on, and managed browser policies to keep work activity inside approved environments. Clear separation also makes it easier to investigate incidents and determine which data was shared.
This separation should extend to browser profiles, cloud storage connections, and AI plugins or extensions. Preventing corporate data from moving into personal AI workspaces reduces the chance that sensitive information remains outside organizational control after an employee changes roles or leaves the company.
Key actions:
Overly restrictive controls can push users toward unapproved tools or workarounds. Monitoring should focus on risky behavior, sensitive data movement, and unauthorized actions rather than blocking AI access by default. Organizations can use warnings, redaction, step-up authentication, and approval workflows before applying full blocks. This approach preserves useful AI workflows while still placing stronger controls around high-risk actions.
Monitoring should also provide enough context to distinguish normal business use from suspicious activity. Useful signals include the application used, data classification, user identity, upload volume, connected systems, and actions taken by an agent. This helps security teams focus on meaningful events instead of generating large numbers of low-value alerts.
Key actions:
AI products, models, integrations, and agent capabilities change quickly. A tool that was low risk when used only for text generation may become higher risk after gaining access to files, APIs, or automated actions. Organizations should review approved applications, permissions, data handling requirements, and enforcement rules on a regular basis.
Policy updates should also reflect new business use cases, incidents, regulatory requirements, and changes in vendor capabilities. Reviews should include changes to model behavior, new integrations, modified retention terms, and expanded agent permissions. Security teams should also use incident findings and usage data to identify controls that are too weak, too broad, or no longer relevant.
Key actions:
Airlock Digital Agentic Steering extends Airlock Digital's application control to govern what AI agents do on the endpoint. It applies company-defined security policies to agentic agent activity, evaluating commands before they execute. When an action is denied, Agentic Steering communicates the policy decision directly to the agent, along with context or an approved alternative, so the agent can adjust its approach and keep working within approved boundaries. Security and IT teams can define command rules and centrally review agent sessions, commands, policy decisions, and outcomes.
Key capabilities of Airlock Digital Agentic Steering: