Today’s “AI Everywhere” reality is woven into daily workflows across the enterprise, embedded in a world of SaaS platforms, browsers, copilots, extensions, and shadow tools that are rapidly expanding faster than security teams can track them. However, most organizations still rely on traditional controls that operate far from where the AI interactions actually occur. As a result, governance gaps widen and AI usage increases exponentially, but visibility and control do not.
As AI becomes central to productivity, enterprises face new challenges in enabling business innovation while maintaining governance, compliance, and security.
A new buyer’s guide to managing AI usage argues that companies fundamentally misunderstand where AI risks exist. Discovering the uses of AI and eliminating “shadow” AI will also be covered in an upcoming virtual lunch and learn.
The surprising truth is that AI security is not about data or apps. It’s a matter of interaction. And traditional tools aren’t built for that.
AI everywhere, visibility nowhere
Ask any average security leader how many AI tools their employees use and you’ll get the answer. I ask how they know, and the room goes silent.
This guide reveals the uncomfortable truth that AI adoption will outpace AI security visibility and control by years, not months.
AI is being built into SaaS platforms, productivity suites, email clients, CRMs, browsers, extensions, and even employee-side projects. Users switch back and forth between their corporate and personal AI identities, often within the same session. Agent workflows chain actions across multiple tools without clear attribution.
But the average company lacks a reliable inventory of its AI usage. Much less can you control how prompts, uploads, identities, and automated actions flow throughout your environment.
This is an architecture issue, not a tool issue. Traditional security controls fail at the point where AI interactions actually occur. This gap is why AI usage controls have emerged as a new category specifically built to control the behavior of real-time AI.
AI usage controls allow you to manage AI interactions
AUC does not enhance traditional security, but is a fundamentally different layer of governance at the point of AI interaction.
Effective AUC requires both moment-of-interaction detection and enforcement, leveraging contextual risk signals rather than static whitelists or network flows.
In other words, AUC does more than just answer “What data was leaked from the AI tool?”
Answer the questions, “Who is using AI? How are they using it? What tools are they using? In what sessions? With what IDs? Under what conditions? What happened next?”
The shift from tool-centric controls to interaction-centric governance is where the security industry needs to catch up.
Why most AI “controls” aren’t really controls
Security teams always fall into the same trap when trying to secure the use of AI.
Treating AUC as a checkbox feature within CASB or SSE Relying solely on network visibility (which misses most AI interactions) Over-indexing on discovery without enforcement Ignoring browser extensions and AI-native apps Assuming data loss prevention is sufficient
Each of these creates a dangerously incomplete security posture. The industry has been trying to retrofit old controls with entirely new interaction models, but it just hasn’t worked.
AUC exists because no traditional tools were built for it.
Controlling AI usage is more than just visibility
In AI usage control, visibility is only the first checkpoint, not the destination. While knowing where AI is being used is important, the real differentiation lies in how solutions understand, manage, and control AI interactions the moment they occur. 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 believe that discovery defines the full scope of risk. In reality, visibility without interaction context often leads to shoddy responses such as exaggerated risk perceptions and widespread bans on AI. Recognize interactions: AI risks occur in real-time while filling out prompts, automatically summarizing files, or when agents run automated workflows. We need to move beyond “which tools are being used” to “what users are actually doing.” Not all AI interactions are dangerous, most are harmless. Understanding prompts, actions, uploads, and outputs in real time is what separates innocuous use from real exposure. Identity and context: AI interactions often occur through personal AI accounts, unauthenticated browser sessions, or unmanaged extensions, bypassing traditional identity frameworks. Traditional tools miss much of this activity because they assume identity and control. Modern AUC must connect interactions to real-world identities (corporate or personal), assess session context (device state, location, risk), and apply adaptive risk-based policies. This allows for subtle controls such as “Allow marketing summaries from non-SSO accounts, but block financial model uploads from non-corporate IDs.” Real-time control: This is where traditional models break down. AI interactions do not fit into the allow/block mentality. The most powerful AUC solutions work with nuances like edits, real-time user alerts, bypasses, and guardrails that protect your data without stopping your workflow. Architectural suitability: The most underrated but crucial stage. Many solutions require agents, proxies, traffic rerouting, or changes to the SaaS stack. These deployments are often stopped or bypassed. Buyers quickly realize that winning architectures are ones that fit seamlessly into existing workflows and enforce policies at the actual point of AI interaction.
Technical considerations: The head guides, but ease of use moves the heart
While technical suitability is paramount, non-technical factors often determine the success or failure of an AI security solution.
Operational Overhead – Can it be deployed in hours? Or does it require weeks of endpoint configuration? User Experience – Are the controls transparent and disruption minimized? Or do they generate workarounds? Future Proofing – Does the vendor have a roadmap to adapt to new AI tools, agent AI, autonomous workflows, and compliance regimes? Or are they buying static products in a dynamic space?
These considerations are about sustainability, not a “checklist,” and ensure that the solution scales for both organizational adoption and the broader AI environment.
The Future: Interaction-centric governance is the new frontier for security
AI is not going away, and security teams must evolve from perimeter control to interaction-centric governance.
The Buyer’s Guide for AI Usage Control provides a practical, vendor-neutral framework for evaluating this emerging category. For CISOs, security architects, and engineers, it says:
What features really matter? How to differentiate marketing from substance. And why real-time, contextual control is the only way to scale.
AI usage control is not just a new category. It’s the next step in secure AI deployment. Reframe issues from data loss prevention to usage governance and align security with business productivity and enterprise risk frameworks. Companies that master AI usage governance can confidently unlock the full potential of AI.
Download the Buyer’s Guide for AI Usage Management to explore the standards, capabilities, and evaluation frameworks that will define secure AI deployments in 2026 and beyond.
Join us for a virtual lunch to discover how to use AI and eliminate “shadow” AI.
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