As AI becomes embedded across browsers, SaaS platforms, extensions, copilots, and emerging agentic workflows, organizations require a new governance layer that operates at the moment of interaction.
That requirement changes what “good” looks like in AI governance. Vendor evaluation needs to move past broad promises and focus on concrete, comparable criteria across discovery, contextual risk assessment, policy-based governance, real-time enforcement, auditability, operational fit, and future readiness.
Our RFP Guide for Evaluating AI Usage Control Solutions is designed to help security, compliance, and IT leaders systematically evaluate AI Usage Control (AUC) solutions in a consistent, side-by-side way.
Why use an RFP template for AI Usage Control vendor evaluation?
AI usage control is not one feature. It is a set of capabilities that have to work across how AI is accessed and used, then hold up under real operational constraints.
This guide helps accelerate research, strengthen decisions, and enable safe AI adoption across the organization by standardizing what vendors must answer, and how they must answer it.
What should an AI Usage Control platform be evaluated on?
The guide is organized into eight sections, each mapping to a distinct requirement area in an enterprise AI governance program.
- AI Discovery and Coverage
How AI usage is continuously discovered and monitored across all access paths and environments. - AI Risk Assessment & Contextual Awareness
How the solution assesses AI risk in real-time by analyzing prompt content, data sensitivity, identity, and access context. - Policy-Based AI Usage Governance
How granular, context-aware policies are defined and enforced to allow, restrict, or block risky AI actions. - Real-Time Enforcement at Interaction Time
How controls are applied at the moment of AI interaction, before sensitive data is exposed or risky actions are completed. - Monitoring, Alerting & Auditability
How AI activity is logged, monitored, and audited to support security operations, compliance, and incident response. - Architecture Fit & Operational Readiness
How AI controls are applied at the point of interaction without architectural or operational burden. - Deployment & Management
How the solution is deployed, scaled, and managed across users, browsers, devices, and environments with minimal operational overhead. - Vendor Readiness & Futureproofing
Assesses vendor support, scalability, and ability to adapt to evolving AI risks, tools, and governance requirements.
What does “AI Discovery and Coverage” mean in practice?
The objective is simple. Establish complete and continuous visibility into how AI is used across the organization.
In practice, the guide pushes vendors to prove coverage across environments, browsers, and access paths, not talk around them. It asks whether a vendor can discover AI usage across browsers, SaaS applications, extensions, native apps, IDEs and agentic workflows.
The guide asks whether a vendor supports breadth where AI now shows up, including:
- Multi-browser coverage, including Chrome, Edge, Safari, Brave, Arc, and others
- AI browser detection and coverage, including Atlas, Dia, Genspark, Comet, and others
- Side panel control in AI browsers
- The ability to distinguish between, and control, both user and agent actions
- Embedded SaaS AI detection inside platforms like CRM, email, and collaboration tools
- Browser-based AI detection for tools such as ChatGPT, Claude, and Gemini
- Desktop-based AI detection for native tools such as ChatGPT and Copilot
- IDE plugin detection and control
- Extension detection, including AI-powered browser extensions acting as intermediaries
Then it moves into governance basics that often get skipped during evaluation, even though they change the risk profile:
- Sanctioned vs. shadow AI (BYOAI), and how unauthorized tools are detected
- User attribution for AI activity
- Identity mapping and differentiation, including corporate vs. personal identities, and authenticated vs. unauthenticated identities
- Identification of account type (business vs. personal) and whether data is subject to model training
- Incognito and private mode support
- Conversation visibility, including past and active AI conversations, prompts, and responses
How do you evaluate risk and policy without guessing?
The guide separates risk assessment from policy enforcement, then asks vendors to explain both.
For risk assessment, the objective is clear.
Prioritize AI governance based on dynamic risk rather than static assumptions.
That means evaluating whether the solution can account for how AI is accessed (browser, extension, embedded SaaS, API, agent), detect risky or anomalous AI usage patterns based on behavior and context, and factor in user role, identity type, device posture, and session context.
It also includes extension risk assessment as a defined requirement.
Can you analyze all AI-powered browser extensions installed by users and block risky ones?
For policy, the objective is also explicit.
Translate governance intent into enforceable, real-world controls.
This section tests whether policies reach the actions where exposure happens, including prompts, uploads, copy/paste, and responses. It also tests sensitive data blocking for PII, PHI, and IP, plus the ability to detect and block prompt injections in AI browsers.
It does not stop at a single enforcement action. It asks whether vendors support multiple enforcement modes such as Allow, Monitor, Warn, Bypass with Justification, Block, and Redact, and whether policies can be applied consistently across browsers, SaaS, extensions, and agents.
How do you test real-time enforcement at interaction time?
The guide treats interaction-time control as a distinct evaluation area, with a distinct objective. Control AI risk at the moment it occurs.
It asks whether a solution can inspect prompts, inputs, uploads, and responses in real-time, and whether enforcement can account for intent, identity, and session context.
It also tests operational realities that determine whether controls work in practice:
- Anomaly detection and enforcement for misuse, policy violations, or anomalous behavior
- Non-disruptive controls that do not break workflows or degrade performance
- Bypass-proof controls that minimize user attempts to bypass policies or adopt unmanaged workarounds
- User guidance through real-time warnings, explanations, or guidance when actions violate policy
How do you turn vendor answers into a defensible decision?
The guide includes a simple evaluation process designed for side-by-side comparison.
- Review each section to understand the requirements.
- Distribute the RFP to the shortlisted AI Usage Control vendors.
- Request that each vendor complete the Response column for every requirement with:
Yes or No response
Detailed description of the capability and provide references, where applicable - Score and compare responses to identify the vendor that best meets your governance, security, operational, and productivity needs.
The practical value is consistency. Every vendor answers the same requirements, in the same format, across the same domains, with references.
That is how you move from impressions to evidence.
What should you do next?
If AI is embedded across browsers, SaaS platforms, extensions, copilots, and emerging agentic workflows, then governance has to operate at the moment of interaction.
This guide standardizes evaluation criteria so vendors can be assessed consistently, side-by-side, against the real requirements of enterprise AI governance, with discovery, contextual risk assessment, policy-based governance, real-time enforcement, auditability, operational fit, and future readiness.
Download the RFP Guide for Evaluating AI Usage Control Solutions
