Managed service providers are used to helping customers tackle shadow IT. Employees were signing up for unauthorized file sharing platforms, collaboration tools, and cloud applications without IT department involvement, creating security risks that often went unnoticed until a problem occurred. Most MSPs responded by helping their customers improve visibility, establish governance, and take back control of their technology assets.
That challenge hasn’t gone away yet. But it has evolved.
Unmanaged AI applications are an increasingly common source of technology risk. Employees are connecting AI-powered meeting assistants to Microsoft 365, using generative AI tools to draft documents, summarizing reports, and analyzing spreadsheets, installing browser extensions that promise increased productivity, and allowing AI applications to access corporate email, calendars, and cloud storage. In many organizations, these tools are in use long before security teams have had a chance to assess risk.
The result is shadow AI, which presents an important new visibility challenge for organizations. Unlike shadow IT, organizations are dealing with both standalone AI applications and AI capabilities built into existing SaaS platforms. The pace of innovation means adoption often happens organically, so IT teams strive to keep up with what’s already in use across the business.
Employees are not trying to circumvent security policies or create unnecessary risks. They are simply looking for ways to work more efficiently. Generative AI helps you compose emails, automate repetitive tasks, analyze information, create presentations, and improve customer communications in seconds. For organizations under pressure to increase productivity, these benefits cannot be ignored.
However, technology adoption is moving much faster than governance. By the time many organizations begin evaluating approved AI platforms, employees have often already adopted consumer AI assistants or enabled AI capabilities within existing business applications. Many security teams have little understanding of how many AI-powered tools are already connected to their organization, what permissions those tools have been granted, and what business data they can access.
This lack of visibility is a big problem. Any AI application connected to a business environment brings new questions that organizations need to answer. What information does the application have access to? Is it authorized to read emails, calendars, files? Is sensitive customer information being uploaded to external services? Can sensitive intellectual property be kept outside of the company’s control? These are even more important in highly regulated areas where organizations need to demonstrate how sensitive data is protected.
The problem is not that AI is inherently insecure. Many enterprise AI platforms include robust security controls, auditing capabilities, and governance features. Greater risk arises when organizations do not know which AI applications are being used, who has authorized them, and how they interact with critical business systems. Without that visibility, it’s difficult to enforce consistent security policies or identify potentially dangerous behavior before it leads to an incident.
This changing landscape presents a huge opportunity for MSPs. Customers increasingly expect service providers to provide proactive security, rather than simply reacting when something goes wrong. According to WatchGuard2026 MSP Cybersecurity Trend ReportAlthough 75% of organizations experienced a cybersecurity incident in the past year, 44% are willing to pay more for AI detection and response, and 47% value 24/7 monitoring and rapid response enough to pay a premium. Shadow AI meets these expectations squarely, creating a new area where customers look to MSPs for guidance before security issues become business problems.
Rather than asking customers if they are using AI, MSPs are in a position to show them exactly where AI is already present in their environment. Identifying connected applications, checking OAuth permissions, monitoring cloud identities, and highlighting gaps in governance allows providers to have more meaningful discussions with their customers. Instead of talking about hypothetical risks, you can present evidence of real activities and recommend practical steps to reduce risks.
Rather than telling customers not to use AI, MSPs can help organizations use AI safely. By helping customers deploy AI with the right visibility and governance, businesses can reap productivity benefits while mitigating unnecessary security and compliance risks.
As AI adoption accelerates, cloud visibility has become a critical layer of modern managed security. By understanding which AI-powered applications are connected, identifying risky OAuth permissions, monitoring cloud identities, and detecting emerging SaaS threats, MSPs can support customers as they adopt AI without compromising security.
Manually tracking AI adoption is impractical, so solutions that provide continuous visibility across cloud environments are valuable. With new AI assistants, integrations, and SaaS capabilities emerging almost every week, automated discovery and monitoring are essential for organizations to maintain accurate visibility into their cloud environments.
AI is already embedded in many customer environments. For MSPs, this is an opportunity to provide proactive security, strengthen customer trust, and build higher-value managed services around one of today’s fastest-growing cloud risks.
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