Businesses worldwide are adopting AI at a pace that often outstrips their ability to manage the risk. Today, roughly 75% of knowledge workers report using AI in their workflows, and more than 1 billion people engage with the top AI tools each week. This rapid, often decentralized adoption—including the rise of “shadow AI” and use of unsecured platforms—has introduced significant security gaps. In 2025, IBM found that 13% of organizations reported breaches of AI models or applications they used. Of those organizations, 97% shared that they lacked proper AI access controls in place.
A study from Vanta and Sepio Research also points to an alarming trend, as around two-thirds of IT and business leaders said their use of agentic AI is outpacing their understanding of the technology.
In the era of AI and AI security risks, organizations are facing a three-part problem: the rise of Shadow AI, employees’ blind trust in unverified AI outputs, and the misguided impulse to prohibit AI wholesale.
Shadow AI is exploding
As AI has grown in popularity, so has the use of unsanctioned or unapproved tools—often driven by employees independently adopting tools like ChatGPT, Claude, or Gemini to improve productivity. This is what’s known as Shadow AI.
In many cases, employees are using these tools through personal accounts outside of IT visibility, meaning sensitive business data may be entering systems the organization does not control.
While these tools are not inherently dangerous, the risk increases when they are used without clear policy, guidance, or safeguards.
This happens most frequently when employees upload sensitive information—such as contracts, source code, or customer data—to AI tools or connect business apps to AI systems, putting company IP, client data, and compliance-regulated information at risk.
The ‘blind trust’ problem
Widespread AI adoption has also led employees to place blind trust in unverified AI outputs, which can result in poor-quality or inaccurate results. This is driven in part by AI’s rapid integration into everyday business tools and services. CRMs, Microsoft 365, marketing platforms, and other core software now enable AI capabilities by default.
As a result, employees have access to AI features but may not fully understand how to use them effectively and safely. They also may not follow the processes needed to validate outputs, maximize the tool’s value, and ensure accuracy.
Why blanket AI bans can backfire
Finally, some businesses recognize these risks and respond by trying to block AI tools outright. However, this approach may drive AI usage even deeper into the shadows. This is especially true if employees are finding value in these tools.
A full-scale ban may result in savvy users adopting workarounds or migrating to even less secure AI platforms to support their workflows.
While these three challenges can seem difficult to address, there is a practical framework that helps MSPs and businesses move forward: AI readiness.
What Is AI Readiness for MSPs?
AI readiness refers to the combination of governance, education, and culture to reduce AI-related risk while still supporting the innovation AI enables.
In practice, it’s a three-step process:
- Establishing clear policies and guardrails for AI tools and usage
- Training employees on those guardrails and how to use AI tools effectively
- Creating a culture where employees feel safe using AI, with a shared understanding that these tools are meant to help them succeed
Overall, AI readiness involves a holistic approach that prepares an organization to use AI safely, confidently, and effectively. Let’s dive deeper into each step, one by one:
Implementing clear AI policies and guardrails
First and foremost, organizations should establish an AI Acceptable Use Policy. This document should clearly define which AI tools are approved and which are off-limits.
Alongside that, the policy should specify what data is considered confidential or regulated in the context of AI use. It should also spell out when human review is required, especially when sensitive information is involved.
Just as important, the policy should define the scenarios where sensitive data may be used with AI tools and the safeguards required to do so securely.
Training employees on how to use AI
Once an acceptable use policy is in place, employees need training on the new guidelines. That training should be both continuous and regular. This approach helps account for evolving tools and workflows, and while also reinforcing the behaviors that reduce risk.
The type of training implemented should primarily reduce complexity by giving employees a simple way to understand the do’s and don’ts of using AI. One effective approach is a green, yellow, and red framework:
- Green: Data that can be used in any AI tool
- Yellow: Data that can be used only in approved AI platforms
- Red: Data that should never be used in AI under any circumstances
Ongoing training reinforces these rules over time, keeping expectations clear and AI use consistent as tools and policies change.
Building a safe and empowered AI culture
Lastly, organizations should foster a culture where employees feel safe using AI and understand these tools are meant to help them succeed, not replace them.
That means encouraging safe experimentation without fear of punishment or job displacement. When employees worry about getting in trouble or being replaced, they’re more likely to avoid AI entirely or resort to using it in the shadows.
A healthy AI culture supports innovation, allowing employees to innovate and experiment while also giving leadership clear evidence that AI use is yielding real return on investment.
The Next MSP Margin Opportunity: Building an ‘AI-Era’ Security Culture
On the MSP side, we believe that AI readiness and this move toward an “AI-era security culture” are poised to become the next margin opportunity in the channel. In essence, security awareness is already table stakes for most clients, and ordinary training is not expected to change behavior or truly reduce AI-driven risk.
More and more, AI is shifting compliance expectations from “Do you have a policy?” to “Can you prove it’s working?”
As established, it’s not enough to simply publish an AI acceptable use policy. Organizations need evidence of secure operating environments and behavior alignment, including whether shadow AI tools are still being used. Auditors and regulators are also increasingly likely to expect proof that controls are not just documented, but actively adopted and enforced.
All of this means that implementing an AI-ready framework is a significant opportunity for MSPs to deliver high-value services to their customers.
How MSPs Can Scale AI Readiness with Breach Secure Now
While recognizing AI readiness as a meaningful opportunity is important, the real challenge is turning it into a scalable offering. The key is building an AI readiness program that is structured, repeatable, and measurable, so that MSPs can scale across clients while demonstrating real risk reduction and business value.
Breach Secure Now supports that shift by helping MSPs turn AI readiness into a structured service rather than a one-off conversation.
It provides assessments to establish a baseline, policy guidance to set clear guardrails, employee training to drive safer usage, and ongoing reinforcement to support lasting behavior change. Most importantly, Breach Secure Now makes the process measurable through reporting and score-based insights, allowing MSPs to show progress and deliver consistent quality across each client.
Ultimately, AI readiness starts with people, not technology, and Breach Secure Now helps MSPs deliver it as an ongoing managed service.
Connect with Breach Secure Now to turn AI readiness into a scalable managed service that protects clients and drives AI-powered growth.
