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Shadow AI is a governance gap—and a chance for MSPs to help customers use AI with clearer rules, safer tools, and better visibility. An AI readiness assessment can move the conversation beyond simply blocking apps or selling another license.
What Shadow AI means for channel providers
The article behind this topic uses “Shadow AI” to describe employees’ use of AI tools that IT has not approved or paid for. It also points out that leaders pressing for speed and output can encourage this behavior, even when formal governance lags.
The scale is a reason to ask customers about the issue, not a substitute for understanding their own environment. The article attributes to Gartner an estimate that 79% of cybersecurity leaders have evidence of unsanctioned AI use. Separately, N-able’s 2026 announcement says a Gartner survey of 302 cybersecurity leaders conducted from March through May 2025 found that 69% of organizations suspected or had evidence of employees using prohibited public generative AI. The figures use different wording and contexts; they should not be treated as directly comparable.
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N-able’s Chief AI Officer, Nicole Reineke, said, “Before organizations can govern AI, they need to understand where it’s being used.” That is a vendor statement, but it captures a practical starting point: first establish what is happening, then decide what should change.
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Why blocking AI is not a readiness strategy
A blanket block may reduce access to some tools, but it does not answer which uses are valuable, which data is sensitive, or what employees should use instead. The source article also warns that restricting paid access can push staff toward unsanctioned options. A stronger approach pairs proportionate controls with approved alternatives and clear guidance.
The Cloud Security Alliance (CSA) research note recommends building an inventory of AI tools, actively communicating acceptable-use policy, and providing sanctioned alternatives. It reports that 18.5% of employees were aware of any company AI policy and cites evidence of an 89% reduction in unauthorized use when sanctioned tools were made available. Those are figures reported through the CSA note; without the underlying study population and conditions, they should not be presented as universal benchmarks or promised outcomes.
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What good readiness actually looks like
An MSP can frame readiness as a set of connected services rather than a one-off product deployment. The right scope depends on the customer’s work, data sensitivity, regulatory obligations, and tolerance for operational friction.
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Establish which AI services and features employees use, for what tasks, and through which accounts or devices. The CSA note suggests combining network telemetry, browser-extension scanning, and SaaS API audits to build an inventory. A discovery exercise should distinguish personal accounts from corporate use where the available signals permit it, and document the limits of visibility rather than implying every use can be detected.
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2. Set an understandable AI-use policy
Work with the customer to define approved tools, permitted tasks, prohibited data, review requirements, and a route for requesting exceptions. A policy needs active communication and reinforcement; publishing a document alone does not make employees aware of it. The rules should be specific enough to guide daily work and flexible enough to reflect different roles and data classes.
3. Train people for real decisions
Training should go beyond a slide presentation. Use role-relevant examples to help staff recognize sensitive information, check AI-generated output, understand tool-specific data handling, and know when human review is required. Give employees a practical way to ask questions or request an approved option when a policy blocks a task.
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4. Optimize licensing and approved alternatives
Review existing subscriptions and available AI features before proposing new purchases. Identify tasks for which sanctioned tools can meet user needs, and clarify who owns access, configuration, and ongoing review. An approved alternative is most useful when it works for common tasks; a nominally permitted tool that employees cannot use effectively may leave the underlying demand unresolved.
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Map the customer’s AI use to its data risks, identity controls, contractual commitments, and applicable compliance obligations. Consider what information may be entered, how accounts are managed, what records are needed, and how incidents or policy exceptions will be handled. Avoid promising compliance from a tool alone: governance depends on the customer’s policies, implementation, and operating practices as well as technology.
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A practical sequence for an AI readiness engagement
- Inventory use. Combine available network, endpoint, browser, identity, and SaaS signals to identify AI tools and the contexts in which they are used.
- Prioritize exposure. Separate higher-risk uses—such as handling sensitive data or using unmanaged accounts—from lower-risk activity, and confirm findings with the customer.
- Agree on policy. Define approved services, acceptable tasks, data restrictions, exception handling, and the people responsible for decisions.
- Communicate and train. Explain the policy in the channels employees use, provide role-specific examples, and establish a route for questions.
- Provide suitable alternatives. Match sanctioned tools to common workflows so employees have a workable route for legitimate AI needs.
- Apply proportionate controls. Use technical controls and user coaching that reflect the customer’s risk and operational requirements rather than relying on blanket blocking.
- Review and update. Revisit tool use, policy exceptions, licensing, and control effectiveness as the customer’s needs and available AI services change.
How to evaluate tools and partner routes
Software can support discovery and governance, but vendor descriptions do not establish that products are equivalent or independently validated. N-able says its Shadow AI Visibility capability across N-central, N-sight, and Adlumin identifies and classifies AI tools and can support MSP work such as assessments, risk reviews, compliance reporting, and policy recommendations. Product packaging and customer eligibility should be confirmed before making commitments.
Other vendors describe different partner approaches. Aona lists referral, resale, managed-service, advisory and implementation, and technology-alliance routes. AIBound describes opportunities for VARs, MSSPs, and technology or platform partners, including enablement and deal registration. ShadowLock markets a multi-tenant platform for MSP delivery with discovery, endpoint and browser controls, and reporting. These are first-party descriptions, not independent assessments; partner eligibility and commercial terms are not established here.
Use operational fit—not a vendor’s category label—to assess options:
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- Account context: Can the tool distinguish personal accounts from corporate ones where that distinction matters?
- Actionability: Does it support policy enforcement and user coaching, or mainly report activity?
- Service delivery: Can the MSP manage multiple tenants and produce the audit evidence customers need?
- Data and operations: What data is collected, how is it handled, and what deployment and maintenance burden will the customer take on?
- Commercial fit: What are the verified costs and partner terms, and do they support the customer’s service model?
The vendor pages reviewed do not provide a shared independent benchmark across these dimensions, so a feature list alone is not a basis for ranking products.
Make the opportunity customer-specific
For channel providers, Shadow AI creates a reason to move from transactional reselling toward ongoing advice: discovery, policy, training, licensing, and risk reviews can become a coherent readiness engagement. The business case is strongest when it starts with a customer’s actual workflows and risks, not a forecast of guaranteed recurring revenue. The aim is to help the customer make informed choices about AI—and to give employees a governed way to do useful work.
Quick Recap
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