Shadow AI isn’t just a security gap, it’s a visibility and control problem that directly impacts how managed service providers (MSPs) protect, advise and grow their clients. As AI tools become embedded in daily workflows, MSPs are losing sight of where sensitive data is going and how it’s being used. That lack of visibility creates risk, but it also creates a clear opportunity for MSPs to step in, take control and lead.
As more employees turn to AI to enhance productivity and hybrid working models continue to grow, this adoption is happening largely in the dark, often bypassing security controls designed to protect operations.
The challenge for business leaders and MSPs isn’t to ban AI entirely but to bring it out of the shadows and focus on developing frameworks that clearly define how businesses can use it responsibly.
AI is spreading faster than leaders realize
The pace of workplace AI adoption is now outpacing internal governance. In the UK, 25% of businesses are already using AI in some capacity; for organisations with 250 or more employees, this rises to nearly half (44%).
This should not surprise business leaders. Employees are under pressure to work faster, produce more and operate with limited resources. AI offers an obvious shortcut. The problem begins when these tools are adopted without visibility or approval.
The risks are not theoretical. Amazon reportedly discovered that ChatGPT responses closely mirrored internal proprietary data, prompting concerns that employees had uploaded sensitive information to external AI tools. As a result, Amazon has issued internal guidelines advising employees to not use third-party AI tools to complete work-related tasks.
Samsung also suffered a high-profile incident when employees used ChatGPT to review code and summarise internal meeting notes, inadvertently exposing proprietary source code and confidential information. This led Samsung to implement new policies on the use of generative AI at work and to investigate those involved.
For MSPs, this is the real issue: clients are already using AI without oversight. Waiting for a policy discussion means reacting after exposure rather than preventing it. Because internal governance in both instances was implemented in response to a crisis rather than as a proactive measure, shadow AI continues to outpace traditional oversight at an alarming rate.
AI readiness is the cure for shadow AI
AI readiness means more than allowing or blocking tools. It requires policies, visibility, controls and cultural understanding needed to use AI safely at scale.
Because shadow AI often operates through browser extensions, consumer platforms, and legitimate websites, it can sit outside traditional security layers. Its activity may appear as normal web traffic, while still creating serious exposure around sensitive data, compliance obligations and customer confidentiality.
While these risks are important, this doesn’t mean that business leaders and security teams need to fear AI and ban it altogether. In most cases, banning AI only drives it further underground, making it much more difficult to monitor. Instead, MSPs and business leaders should be focusing on placing guardrails in place before risks escalate.
In many industries, AI adoption has already helped businesses automate repetitive tasks, optimise workflows, enhance service delivery and improve data analytics. Simply dismissing AI because of its associated risks means that teams miss out on these opportunities.
When MSPs bridge the gap between adoption and help their clients align security activities with business outcomes, they close critical vulnerabilities and gain a competitive edge. By vetting and identifying approved tools and setting clear usage boundaries, MSPs can demonstrate expertise beyond security products and become trusted interpreters of risk.
More importantly, this ensures MSP and business leaders are actively developing working models that facilitate innovation and responsible enablement. Those that understand AI risk and proactively develop policies and governance frameworks to respond are much better positioned to adopt new technologies, demonstrate resilience and deploy innovative solutions safely.
At a time when cyber insurance underwriting and regulatory pressures are intensifying, shifting towards an AI-ready operational model will become a defining advantage for businesses and a major value proposition for the MSPs that support them.
Why complexity is fuelling security problems
Shadow AI is not only a technology issue, it’s a clear sign that employees believe existing systems are too slow, restrictive or disconnected from how they actually work. This is where MSPs can differentiate, not by adding more tools, but by simplifying how security works in practice.
When security models become overly complex, people look for shortcuts. If approved tools are difficult to access, unclear or unavailable, employees will often choose the path of least resistance. That does not make them malicious; it signals that the operating model is failing to balance productivity with control.
To manage AI effectively, MSPs need visibility across the environment where AI usage actually occurs, this means gaining consistent visibility across endpoint security, network activity and browser-level behaviour, not fragmented insights. Without this, they are advising on risk without evidence.
Rather than issuing blanket bans, MSPs can work with businesses to categorise tools into approved, limited or prohibited tiers based on the risk they pose to the business. This structured approach allows business leaders, security teams, workforces and MSPs to have a much more transparent discussion about AI.
Crucially, when employees are included in these discussions, educated about the underlying risks, and then supported with approved tools and usage guidelines, they are more likely to accept and uphold usage policies, thereby shifting them from potential security risks to active lines of defense.
Shadow AI can no longer be ignored
Shadow AI is not going away. The MSPs who succeed will be the ones who bring it under control, turning unmanaged risk into a structured, revenue-generating service.
AI readiness is how MSPs protect their clients, prove their value, and differentiate in an increasingly competitive market. For business leaders, this means working closely with partners to gain visibility, define acceptable use and turn unmanaged AI risk into a controlled, strategic advantage.
AI is already embedded in many modern workplaces; how businesses choose to manage it is entirely their choice, but those who choose to see it clearly, manage it simply and govern it responsibly will be the ones who thrive.
by Myles Bray, CEO of CyberSentriq
