Comprehensive Guide to AI Usage Control for Enterprise Security Teams

Elad Schulman

February 2, 2026

min read

Comprehensive Guide to AI Usage Control for Enterprise Security Teams

What Is AI Usage Control?

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AI Usage Control (AI-UC) is an emerging security and governance discipline focused on controlling how AI applications are used in practice, once they have become a part of day-to-day operations within the enterprise.
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In late 2025, Gartner formally introduced AI Usage Control as a distinct category, recognizing a gap that traditional security architectures have struggled to close. As Gartner defines it, AI usage control enables fine-grained categorization and intent-based policies that allow organizations to safely adopt third-party and AI-powered applications while mitigating security risk.

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Unlike traditional controls that operate before deployment or after an incident, AI usage control sits in the middle of live workflows. It evaluates context such as user role, data sensitivity, tool type, and intent, and applies policy in real time.

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Enterprises are accelerating AI adoption through browsers, copilots, extensions, and embedded features, often faster than security teams can inventory or approve them. AI usage control provides a purpose-built layer to manage that reality, rather than fighting it.

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Key takeaways

AI Usage Control is about governing usage, not access. It focuses on how AI tools are used moment-to-moment, rather than relying on static allowlists or deployment-time approvals.
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The category exists because traditional controls fall short. Network, endpoint, and legacy DLP tools lack the context and intent awareness required for GenAI interactions.
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AI-UC assumes AI adoption is inevitable. Instead of blocking tools outright, it enables safe use through real-time, risk-based enforcement.
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Control happens at runtime. Policies are applied as users interact with AI, by submitting prompts, sharing data, or using outputs.
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AI-UC is becoming foundational to enterprise AI governance. It connects visibility, enforcement, and auditability into a single control layer designed for GenAI.

AI Usage Control vs Traditional Security Controls

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Traditional security controls were designed for predictable systems with stable boundaries. AI breaks those assumptions. Interactions happen in real time, often through browsers and copilots, and risk emerges from how tools are used.

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AI usage control addresses this gap by shifting enforcement from static access rules to context-aware governance at the moment of interaction.
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	Traditional security controls	AI Usage Control (AI-UC)
Primary focus	Enforcing authorization and access to systems, applications, and data repositories	Governing behavioral use of AI tools, including how inputs are provided, how context is constructed, and how outputs are consumed
Control point	Network gateways, endpoints, or application boundaries, typically enforced before access is granted	The live user-AI interaction layer, including prompt submission, contextual augmentation, and response handling
Risk model	Deterministic and rule-based, assuming predictable inputs, fixed logic, and stable execution paths	Probabilistic and intent-driven, accounting for non-deterministic model behavior, dynamic context, and evolving usage patterns
Data protection	Reactive controls that rely on post-exposure alerts, logs, or forensic analysis	Preventive controls that inspect and enforce policy before sensitive data is transmitted to or generated by AI models
User intent awareness	Minimal awareness of user purpose beyond identity, role, or static policy	Central enforcement signal, derived from interaction context, data sensitivity, action type, and inferred user intent

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AI Usage Control is Critical for Enterprises

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GenAI risk rarely appears as a single, dramatic incident. More often, it shows up through everyday decisions made by employees trying to work faster and smarter. The examples below reflect common, real-world usage patterns enterprises encounter as GenAI becomes part of daily operations.

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Prevent Shadow AI Use

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Consider a product manager who installs a browser-based AI assistant to summarize customer feedback. It’s fast, helpful, and unapproved. Security teams aren’t alerted, policies aren’t enforced, and the tool quietly becomes part of the team’s workflow.

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AI usage control helps organizations move beyond simply discovering Shadow AI. It allows enterprises to define acceptable use and enforce it consistently, before unsanctioned tools become routine and difficult to unwind.

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Reduce Sensitive Data Exposure

Imagine a finance analyst pasting a draft revenue forecast into a GenAI chatbot to improve clarity before a leadership review. No malicious intent is involved. The data is simply shared in a moment where existing controls offer little protection.

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AI usage control focuses on preventing sensitive data from entering GenAI interactions in the first place. By applying context-aware policies in real time, organizations can reduce exposure before information leaves their control.

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Ensure Compliance and Regulatory Readiness

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During an internal audit, a compliance team is asked a straightforward question: Which GenAI tools are employees using, and what data is being shared with them? Answering it requires weeks of manual investigation.

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As AI regulations evolve, enterprises must demonstrate not only policy intent but operational control. AI usage control provides the visibility and enforcement needed to support audits, respond to regulators, and adapt as requirements change.

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Improve Enterprise AI Visibility

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A security team approves a small set of GenAI tools for internal use. Over time, usage expands across departments, copilots behave differently depending on context, and workflows begin to rely on AI in ways no one formally tracks.

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AI usage control restores visibility by treating GenAI interactions as first-class security events. This allows organizations to understand who is using which tools, how they’re being used, and under what conditions.

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Key Risks AI Usage Control Helps Manage

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AI usage control cannot eliminate risk altogether. Its real value is in recognizing where GenAI introduces new failure modes, and managing them at the point where they actually occur.

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The risks below reflect patterns enterprises are already encountering as GenAI becomes embedded in everyday work.

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Unauthorized AI Tool Usage (Shadow AI)

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Shadow AI often begins as a convenience. Employees within an organization discover a a tool in a browser, or a plugin or free service that a colleague recommended.

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Over time, these tools can quickly become a part of daily work, without ever going through approval or monitoring. The risk here is that decisions and workflows are quietly shaped by tools operating entirely outside governance boundaries.

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AI usage control helps surface and govern this activity before it becomes structural, enabling organizations to define acceptable use rather than chasing it after the fact.

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Sensitive Data Sharing in Prompts

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GenAI interactions blur a line traditional controls rely on: the difference between using data and sharing it. When users paste information into prompts, they often don’t perceive it as data transfer at all.

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This creates exposure across a wide range of data types, including:

Financial forecasts and internal reports
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Customer or employee personal data
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Source code, architecture notes, and internal documentation
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Legal drafts and contract language

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AI usage control focuses on preventing sensitive data from entering prompts in the first place, applying policy at the moment of interaction rather than relying on post-exposure detection.

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Inaccurate or Hallucinated Outputs

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Not all GenAI risk involves data leaving the organization. Sometimes the problem is what comes back.

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LLMs can produce confident outputs that are incomplete, outdated, or simply wrong. In low-stakes contexts, this may be an inconvenience. In regulated or operational environments, it can create downstream risk when outputs are reused without verification.

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AI usage control helps organizations apply guardrails around how humans use outputs, by flagging risky contexts for human review, or restricting reuse in sensitive workflows. Assuming that every response is trustworthy by default is an error no organization should fall into.

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IP and Copyright Concerns

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As GenAI becomes part of content creation and development workflows, questions of ownership and provenance become harder to answer. Teams may unknowingly:

Incorporate AI-generated content into customer-facing materials
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Reuse generated code without understanding its origins
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Blend proprietary and external content in ways that complicate ownership
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Without visibility into how GenAI is used, these risks surface late, often during legal review or after publication. AI usage control provides the oversight needed to understand where AI-generated material enters workflows and under what conditions, reducing surprises and supporting responsible reuse.

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Core Components of AI Usage Control

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AI usage control is not a single mechanism. It’s a set of coordinated controls that work together to govern how AI is used. The specifics vary by organization, but the core components remain consistent.

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Access Management and User Permissions

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In highly regulated industries such as financial services or the public sector, access to GenAI tools cannot be uniform. Analysts, contractors, and executives interact with different data types and operate under different accountability models.

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AI usage control builds on existing identity and access frameworks by extending permissions to how GenAI can be used. This includes limiting which tools are available to certain roles, restricting high-risk actions based on clearance levels, and ensuring that access decisions reflect both identity and context.

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Data Classification and Redaction Rules

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For sectors like healthcare, where protected health information is tightly regulated, data sensitivity is not optional or ambiguous. GenAI interactions must respect existing classification schemes and privacy obligations at all times.

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AI usage control enforces these requirements by recognizing data types in real time and applying redaction or blocking rules before information is shared. This allows organizations to enable GenAI for low-risk tasks while ensuring that regulated data never leaves approved boundaries.

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Monitoring, Logging, and Auditing

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In government and public sector environments, transparency and traceability are often as important as prevention. Agencies must be able to demonstrate not only that controls exist, but that they are actively enforced and reviewed.

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Monitoring and logging GenAI usage creates an auditable record of interactions, policy decisions, and enforcement actions. Teams can rely on this record for internal oversight and external oversight, rather than ad hoc reporting or manual reconstruction.

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Approval Workflows for High-Risk Use Cases

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In industries such as legal services or pharmaceuticals, certain GenAI use cases carry heightened risk due to intellectual property concerns, regulatory exposure, or reputational impact.

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AI usage control supports structured approval workflows for these scenarios. Instead of blocking usage entirely, organizations can require additional review, justification, or oversight when GenAI is applied to sensitive documents, external communications, or decision-making processes. This approach preserves flexibility while ensuring accountability where it matters most.

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Pros and Cons of AI Usage Control

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	Description	What it looks like in practice
Pros	Reduced risk of data leakage	AI usage control enforces policies at the moment users interact with GenAI tools, such as submitting prompts or reusing outputs. This helps to prevent sensitive data from being shared before it leaves enterprise control.
	Improved regulatory compliance	Continuous visibility into GenAI usage, combined with consistent policy enforcement, supports audit readiness and regulatory reporting by documenting how AI tools are used and what data is involved.
	Clear AI usage guidelines	Technical enforcement translates abstract AI policies into real-world guardrails, clarifying which tools are allowed, what data types are permitted, and how exceptions are handled across roles and teams.
Cons	Identity and access integration can be a gating factor	The effectiveness of AI usage control depends on how well it integrates with an organization’s existing identity provider (IdP) and access model. Teams may struggle to find tools that natively inherit user roles, group memberships, and contextual access signals.

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Challenges in Implementing AI Usage Control

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While the need for AI usage control is increasingly clear, implementing it across an enterprise is not a simple switch to flip. The challenges are less about technology alone and more about scale, consistency, and alignment with how people actually work.

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Shadow AI Adoption Across Departments

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GenAI adoption rarely happens evenly. Teams experiment independently, adopt tools that fit their workflows, and share recommendations informally. Marketing may rely on writing assistants, engineering on code copilots, and legal on summarization tools, often with little overlap or coordination.

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This decentralized adoption makes it difficult to establish a single source of truth. Without continuous discovery, security teams are left chasing usage patterns that change faster than traditional inventories or approval processes can keep up.

Policy Gaps and Enforcement Issues

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Many organizations already have AI policies in place. The challenge is translating those policies into enforceable controls.

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High-level guidelines—such as “do not share sensitive data with AI tools”—offer direction but little operational clarity. Without technical enforcement, policies depend on individual judgment and awareness, leading to inconsistent application across teams and tools.

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AI usage control requires closing this gap by aligning policy intent with real-time enforcement, while still allowing for nuance, exceptions, and evolution as tools and use cases change.

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Balancing Productivity and Security

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Overly restrictive controls tend to backfire. When GenAI tools are blocked outright or slowed down without explanation, users find workarounds. At the same time, permissive approaches leave organizations exposed.

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The challenge is not choosing between productivity and security, but calibrating controls to reflect context—who is using the tool, what data is involved, and how the output will be used. Achieving this balance takes iteration, feedback, and an acceptance that policies will need to evolve alongside usage.

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Integration With Existing Security Tools

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AI usage control does not exist in isolation. To be effective, it must integrate with identity systems, data classification frameworks, logging pipelines, and incident response workflows already in place.

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Without thoughtful integration, organizations risk creating yet another silo—one that generates alerts but lacks context, or enforces rules without visibility across the broader security stack. Successful implementations treat AI usage control as an extension of existing governance, not a parallel system.

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Best Practices for Implementing AI Usage Control

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Best practice	Why it’s important for AI usage control	What it looks like in practice
Start With Visibility Before Enforcement	Visibility establishes a baseline for informed policy decisions. Enforcing controls without understanding existing AI usage leads to blind spots and resistance.	Continuous discovery of AI tools, browser extensions, copilots, and embedded features Capture who is using what, how often, and in which contexts
Define Risk-Based AI Usage Policies by Role	Not all users, tools, or data types carry the same risk. Role-based policies prevent over-restriction while still protecting sensitive workflows.	Allow low-risk tasks (e.g. summarization, research) broadly Restrict high-risk actions such as pasting sensitive data into public tools Make the distinction based on role and data classification
Apply Controls at Runtime, Not Just Pre-Deployment	Many GenAI risks emerge during live interactions, not during tool approval or deployment. Static controls miss these moments.	Inspect prompts and responses in real time Implement policy steps like blocking, redacting, warning, or requiring approval before execution
Balance Productivity With Guardrails, Not Blanket Blocks	Heavy-handed restrictions push users toward Shadow AI and workarounds. Guardrails preserve productivity while reducing risk.	Allow AI usage within defined boundaries Pair with clear user feedback explaining why an action was blocked or modified
Continuously Review and Tune Policies as AI Usage Evolves	AI tools, features, and usage patterns change rapidly. Static policies quickly become outdated.	Refine policies by observing usage trends, new tools, regulatory changes, and feedback from security teams

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How Lasso Provides Enterprise-Ready AI Usage Control

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AI usage control is only effective if it operates where GenAI risk actually materializes: during live interactions, and at enterprise scale. Lasso was built specifically to address LLM and GenAI security challenges, rather than extending legacy controls into a space they were not designed for.

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Key capabilities include:

Always-on discovery of GenAI usage across browsers, copilots, and applications, providing continuous visibility into which tools are being used and by whom.
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Context-aware, real-time enforcement at the moment of interaction, enabling actions such as blocking, redaction, masking, or user guidance before data leaves enterprise control.
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Policy enforcement aligned to roles and usage context, rather than static allowlists or binary controls.
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Centralized logging and audit trails that capture prompts, responses, and enforcement decisions, supporting compliance and governance workflows.
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LLM-first architecture, built to handle non-deterministic behavior, evolving tools, and high-frequency interactions.
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This approach allows enterprises to move beyond visibility alone and toward practical, enforceable AI usage control that scales with how GenAI is actually used.

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Conclusion

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GenAI is no longer an emerging technology inside the enterprise, but a daily productivity layer. As a result, risk has shifted from deployment decisions to usage behavior.

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AI usage control addresses this shift by focusing on how GenAI is used, now that the question of whether they are allowed has become largely irrelevant. It enables organizations to reduce risk, improve compliance readiness, and regain visibility without reverting to blanket restrictions that hardly ever work anyway.

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For enterprises looking to operationalize AI governance in real-world conditions, usage-level control is the layer that connects policy, security, and productivity.

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Book a live Lasso demo to see effective AI usage control that’s built to work across enterprise AI environments.

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