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Shadow AI creates a real governance problem—and a practical opening for managed service providers (MSPs) to help customers understand and manage AI use. A useful AI readiness assessment goes beyond finding unauthorized tools: it helps a business set acceptable-use rules, train staff, choose suitable licensed tools, and review security and compliance risks.
Why Shadow AI is a channel issue
The source article describes “Shadow AI” as employees using AI tools that IT has not approved or paid for. It also points to a tension inside organizations: leaders may encourage speed and output while IT lacks visibility into which tools staff use and what information they enter. That framing is useful for channel conversations, though organizations may define unsanctioned use differently.
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The article attributes an estimate that 79% of cybersecurity leaders have evidence of unsanctioned AI use to Gartner. Separately, N-able says a Gartner survey of 302 cybersecurity leaders conducted from March to May 2025 found that 69% of organizations suspected or had evidence of employees using prohibited public generative AI. These figures use different wording and stated contexts; they should not be combined or treated as directly comparable. The 69% figure is N-able’s rendering of a Gartner result, not an independent N-able survey. N-able’s announcement describes the survey context.
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Why blocking or buying licenses is not enough
Blocking access can reduce some exposure, but blanket restrictions do not resolve why employees turned to AI in the first place. If common work tasks remain unsupported, users may seek unsanctioned alternatives. Conversely, simply purchasing a business license does not establish which data may be entered, who can use the tool, or how the organization will monitor and review use.
The Cloud Security Alliance (CSA) recommends making acceptable-use policy visible and providing sanctioned alternatives, alongside inventory and governance. Its 2026 research note reports that 18.5% of employees in cited survey evidence were aware of any company AI policy. It also reports an 89% reduction in unauthorized use when sanctioned tools are made available. Those figures are conveyed through the CSA note and its references; without the underlying study populations and conditions, they should not be presented as universal benchmarks. See the CSA research note for its framing.
What good readiness actually looks like
A readiness engagement should connect discovery to policy, people, licensing, and risk. Its scope should reflect the customer’s business, data sensitivity, existing contracts, and operational needs.
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1. Assess current AI use
Establish what tools are in use, by whom, for which tasks, and with what kinds of information. The CSA recommends building an inventory from multiple sources, including network telemetry, browser-extension scanning, and SaaS API audits. No single source necessarily reveals every route of use, so treat the inventory as a working view that needs validation.
2. Set a clear AI-use policy
Help the customer distinguish approved tools and acceptable tasks from prohibited or restricted uses. The policy should explain how to handle confidential, personal, regulated, or customer data; who can approve exceptions; and where staff can ask questions. A policy that is not communicated and reinforced is unlikely to guide everyday choices.
3. Train users beyond a presentation
Training should use realistic tasks and show staff how to check outputs, avoid entering sensitive information into unsuitable tools, and identify when human review is required. Reinforce the policy at relevant points in the workflow, and give employees a clear route to request an approved tool or use case.
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4. Optimize licensing
Review existing subscriptions and access before recommending new purchases. Identify overlapping tools, unused seats, and functions already covered by products the customer owns. Match licensing choices to actual users and approved use cases rather than assuming every employee needs the same access.
5. Review security and compliance
Map likely data flows and assess the customer’s obligations and risk tolerance. Consider how each tool handles submitted data, account identity, retention, access controls, audit evidence, and integrations. The outcome should be practical recommendations and documented decisions—not a blanket claim that a tool is safe or compliant for every use.
A practical sequence for an AI readiness engagement
- Agree on scope. Identify business units, user groups, data types, and systems in scope, along with the decisions the customer needs to make.
- Build and validate an inventory. Combine available network, browser, endpoint, SaaS, and identity signals. Record the tool, account context, observed use, and confidence in each finding.
- Classify use cases and risk. Separate approved business tasks from uses that need restrictions, further review, or an exception. Consider data sensitivity and operational impact.
- Publish and reinforce policy. Explain the rules in accessible language, communicate them through channels staff use, and make ownership and exception paths clear.
- Offer approved alternatives. Provide suitable tools for common tasks and explain how staff can request additional capabilities. The CSA recommends this alongside communication and inventory, not as a standalone fix.
- Apply proportionate controls. Use controls that fit the customer’s environment and needs, such as access rules, user coaching, or restrictions for particular tools and data. Avoid treating indiscriminate blocking as the only governance option.
- Review and update. Revisit the inventory, policy, licensing, and control effectiveness as tools, business use, and obligations change.
How to evaluate tools and partner routes
Technology can help MSPs find and govern AI use, but vendor descriptions establish what a company says its product offers—not independent performance or equivalence between products. For example, N-able says its Shadow AI Visibility capability across N-central, N-sight, and Adlumin identifies and classifies AI tools. It describes potential MSP work including usage assessments, risk reviews, compliance reporting, and policy recommendations. Confirm current packaging and customer eligibility with N-able before proposing it.
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Other vendor pages 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 descriptions are not a shared feature test, and partner eligibility or commercial terms should be verified directly.
Rather than ranking these options from vendor claims, evaluate them against the customer’s environment and the MSP’s delivery model:
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- Context: Can the system distinguish personal from corporate accounts and provide useful information about users and use cases?
- Governance: Does it support policy enforcement, user coaching, or both? Can controls be applied appropriately to different users and data?
- MSP operations: Is multi-tenant management available, and does it fit the provider’s support and escalation workflows?
- Evidence: What reporting and audit records can it produce, and do those meet the customer’s needs?
- Implementation and data handling: What deployment effort, permissions, integrations, and data processing are involved?
- Commercial fit: What are the costs, partner requirements, and ongoing responsibilities? Confirm current terms with the vendor.
Vendor pages provide starting points rather than independent answers: N-able, Aona, AIBound, and ShadowLock. Their descriptions should be verified near the time of a purchase or partnership decision.
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From tool reseller to trusted adviser
An AI readiness assessment can give channel providers a reason to engage customers on governance before a security incident or compliance review forces the issue. The value is in helping the customer understand actual use, make defensible choices, and keep those choices workable for staff. That can support advisory and managed services as well as technology decisions, but it is a customer-specific opportunity—not a guaranteed revenue forecast.
Nicole Reineke, N-able’s Chief AI Officer, said: “Before organizations can govern AI, they need to understand where it’s being used.” This is a vendor statement, but it captures the practical starting point: establish visibility, then help the customer decide what responsible use means in its own environment.
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