The Buyer’s Guide to AI Usage Control

Today’s “AI everywhere” reality is woven into everyday workflows across the enterprise, embedded in SaaS platforms, browsers, copilots, extensions, and a rapidly expanding universe of shadow tools that appear faster than security teams can track. Yet most organizations still rely on legacy controls that operate far away from where AI interactions actually occur. The result is a widening governance gap where AI usage grows exponentially, but visibility and control do not.

With AI becoming central to productivity, enterprises face a new challenge: enabling the business to innovate while maintaining governance, compliance, and security.

A new Buyer’s Guide for AI Usage Control argues that enterprises have fundamentally misunderstood where AI risk lives. Discovering AI Usage and Eliminating ‘Shadow’ AI will also be discussed in an upcoming virtual lunch and learn.

The surprising truth is that AI security isn’t a data problem or an app problem. It’s an interaction problem. And legacy tools aren’t built for it.

AI Everywhere, Visibility Nowhere

If you ask a typical security leader how many AI tools their workforce uses, you’ll get an answer. Ask how they know, and the room goes quiet.

The guide surfaces an uncomfortable truth: AI adoption has outpaced AI security visibility and control by years, not months.

AI is embedded in SaaS platforms, productivity suites, email clients, CRMs, browsers, extensions, and even in employee side projects. Users jump between corporate and personal AI identities, often in the same session. Agentic workflows chain actions across multiple tools without clear attribution.

And yet the average enterprise has no reliable inventory of AI usage, let alone control over how prompts, uploads, identities, and automated actions are flowing across the environment.

This isn’t a tooling issue, it’s an architectural one. Traditional security controls don’t operate at the point where AI interactions actually occur. This gap is exactly why AI Usage Control has emerged as a new category built specifically to govern real-time AI behavior.

AI Usage Control Lets You Govern AI Interactions

AUC is not an enhancement to traditional security but a fundamentally different layer of governance at the point of AI interaction.

Effective AUC requires both discovery and enforcement at the moment of interaction, powered by contextual risk signals, not static allowlists or network flows.

In short, AUC doesn’t just answer “What data left the AI tool?”

It answers “Who is using AI? How? Through what tool? In what session? With what identity? Under what conditions? And what happened next?”

This shift from tool-centric control to interaction-centric governance is where the security industry needs to catch up.

Why Most AI “Controls” Aren’t Really Controls

Security teams consistently fall into the same traps when trying to secure AI usage:

Treating AUC as a checkbox feature inside CASB or SSE
Relying purely on network visibility (which misses most AI interactions)
Over-indexing on detection without enforcement
Ignoring browser extensions and AI-native apps
Assuming data loss prevention alone is enough

Each of these creates a dangerously incomplete security posture. The industry has been trying to retrofit old controls onto an entirely new interaction model and it simply doesn’t work.

AUC exists because no legacy tool was built for this.

AI Usage Control Is More Than Just Visibility

In AI usage control, visibility is only the first checkpoint not the destination. Knowing where AI is being used matters, but the real differentiation lies in how a solution understands, governs, and controls AI interactions at the moment they happen. Security leaders typically move through four stages:

Discovery: Identify all AI touchpoints: sanctioned apps, desktop apps, copilots, browser-based interactions, AI extensions, agents and shadow AI tools. Many assume discovery defines the full scope of risk. In reality, visibility without interaction context often leads to inflated risk perceptions and crude responses like broad AI bans.
Interaction Awareness: AI risk occurs in real-time while a prompt is being typed, a file is being auto-summarized, or an agent runs an automated workflow. It’s necessary to move beyond “which tools are being used” to “what users are actually doing.” Not every AI interaction is risky, and most are benign. Understanding prompts, actions, uploads, and outputs in real-time is what separates harmless usage from true exposure.
Identity & Context: AI interactions often bypass traditional identity frameworks, happening through personal AI accounts, unauthenticated browser sessions, or unmanaged extensions. Since legacy tools assume identity equals control, they miss most of this activity. Modern AUC must tie interactions to real identities (corporate or personal), evaluate session context (device posture, location, risk), and enforce adaptive, risk-based policies. This enables nuanced controls such as: “Allow marketing summaries from non-SSO accounts, but block financial model uploads from non-corporate identities.”
Real-Time Control: This is where traditional models break down. AI interactions don’t fit allow/block thinking. The strongest AUC solutions operate in the nuance: redaction, real-time user warnings, bypass, and guardrails that protect data without shutting down workflows.
Architectural Fit: The most underestimated but decisive stage. Many solutions require agents, proxies, traffic rerouting, or changes to the SaaS stack. These deployments often stall or get bypassed. Buyers quickly learn that the winning architecture is the one that fits seamlessly into existing workflows and enforces policy at the actual point of AI interaction.

Technical Considerations: Guide the Head, But Ease of Use Drives the Heart

While technical fit is paramount, non-technical factors often decide whether an AI security solution succeeds or fails:

Operational Overhead – Can it be deployed in hours, or does it require weeks of endpoint configuration?
User Experience – Are controls transparent and minimally disruptive, or do they generate workarounds?
Futureproofing – Does the vendor have a roadmap for adapting to emerging AI tools, agentic AI, autonomous workflows, and compliance regimes, or are you buying a static product in a dynamic field?

These considerations are less about “checklists” and more about sustainability, ensuring the solution can scale with both organizational adoption and the broader AI landscape.

The Future: Interaction-centric Governance Is the New Security Frontier

AI isn’t going away, and security teams need to evolve from perimeter control to interaction-centric governance.

The Buyer’s Guide for AI Usage Control offers a practical, vendor-agnostic framework for evaluating this emerging category. For CISOs, security architects, and technical practitioners, it lays out:

What capabilities truly matter
How to distinguish marketing from substance
And why real-time, contextual control is the only scalable path forward

AI Usage Control isn’t just a new category; it’s the next phase of secure AI adoption. It reframes the problem from data loss prevention to usage governance, aligning security with business productivity and enterprise risk frameworks. Enterprises that master AI usage governance will unlock the full potential of AI with confidence.

Download the Buyer’s Guide for AI Usage Control to explore the criteria, capabilities, and evaluation frameworks that will define secure AI adoption in 2026 and beyond.