De-risking AI: How MSPs and IT teams can ensure secure AI adoption

AI is everywhere. Everyone is using AI today, whether to write an email, summarize a report, or analyze a spreadsheet. And with every use, data is fed into AI models. 

For MSPs and IT teams, that means client or employee information, internal documentation, and sometimes even sensitive credentials flowing into tools they don’t own or control, introducing risks of data breaches and other security concerns. So while IT leaders are sold on the potential and promise of AI to make their lives easier and more efficient, there is concern about the privacy and security aspects of agentic AI tools. 

This concern isn’t unfounded. Industry experts say that 69% of MSPs worldwide have been breached two or more times in the past year, and nearly half have been breached more than three times.

With the proliferation of AI, the surface area of risk is only going to get wider. How do you ensure that your data remains secure as you adopt AI? 

It’s all about embedding security into the foundation of your AI journey, building systems that are not only powerful and efficient but also safe by design.

Trust first: the foundation of secure AI adoption

Ensuring security and data privacy with AI is as much about trust as it is about technology. Given how new AI is, and with teams, users, and stakeholders all navigating this new world together, it’s essential for MSPs to keep clients involved throughout their AI adoption journey. When users understand how AI fits into their operations, it builds trust, strengthens relationships, and becomes a strong competitive edge. Here are a few things IT professionals should do as they begin adopting AI for their tasks: 

Be transparent and clear 

AI often works quietly in the background,  automating workflows, drafting reports, helping dispatch tickets, etc. But even if it’s behind the scenes, its effects are felt by customers and employees.

That’s why transparency matters. IT leaders must be upfront about how and where AI is used. Explain what you’re implementing, what it will do, and how it impacts both internal and external stakeholders.

AI adoption is still new for everyone, even IT teams and vendors are still learning about AI themselves. So keeping clients and staff involved and explaining the details to them helps ensure that the human touch remains. 

The trust that you can get by being open and honest with your clients about what you use and what how you do it is a competitive differentiator. It shows that you care about these things. Clients know that you as a company only use tools that are vetted, and see you as a company they can trust, setting you apart from other service providers.

- Sam Godfrey, Cofounder and Director of TaskGroup (CompuTask Ltd)

Reassure clients that humans are still in charge

For users, the biggest fear is having their IT support staff replaced by a chatbot or other AI tools. The concern is that AI will take over customer interactions, removing the human element entirely and there’s fear about losing the reassurance, empathy, and context that come with speaking to a human who understands their business.

That’s why it is critical to talk to your users regularly and reassure them that while AI assists, humans still deliver the service. Explain that agentic AI will handle repetitive, low-risk tasks and free up human experts to focus on complex issues, proactive problem-solving, and offer value-added services. 

The concern that people have is that I'm not even going to be handled by a person anymore. And we need to stop that worry for them. We need to explain that AI is a tool. AI helps us. It's not going to replace anything. It's not going to negatively impact your service. It's only going to make things better.

- Sam Godfrey, Cofounder and Director of TaskGroup (CompuTask Ltd)

Choose products that are secure by design

Security should start with your stack. Before adopting any AI tool, look into how it handles data. Is it SOC 2 compliant? What about AWS hosting certifications? Does it explicitly say whether your data is used to train external models?

It is important to ask questions around where the data is being stored, whether the data is being used to train any LLMs, etc. You also need to go down to the person who's going to be using it, and what data you're going to feed into it. These should be prioritized and should be dealbreakers.

At SuperOps, as we build our agentic AI platform, every model we use goes through a layered validation process. We check for basic security and privacy compliance, ensure data isolation, and confirm that any data used in AI interactions never trains external models. Only after these checks are passed does the model move into production.

If you’d like a clear framework and deeper insights into data security and de-risking your agentic AI adoption process, watch the recording of our recent webinar, where Sam Godfrey and Sivanand Sivaram, Head of AI Research at SuperOps, discuss privacy, security, and compliance in AI adoption.

Embedding security into your AI adoption framework

Often, security is an afterthought. But with AI, given the amount of data involved, it is important to prioritize security and privacy. Here are a few things to keep in mind:

Put formal processes and governance guardrails in place

AI tools are now a part of daily business life, and most employees use free AI tools at work. While the use of AI must be encouraged, it is essential to lay down clear boundaries on how it is done.

 Today, around 47% of companies that use AI don’t have a clear security practice in place. And without this, companies risk having sensitive information inadvertently fed into AI models. 

Organizations must have policies and guidelines on the list of pre-approved secure tools employees can use, the kind of tasks AI can be used for, and how much company information can be fed to the AI tools. The idea here isn’t to restrict use or further the fear of AI, but ensure that only secure tools are used even for the smallest of tasks. 

Such frameworks and processes might slow things down initially, given additional steps involved in getting a task done, but in the long run, it is going help with scaling confidently, responsibly, and transparently. 

Don’t overfeed the AI

One of the easiest ways to lose control over your data is by overfeeding AI tools. Often, users share more information than necessary, uploading entire documents just to draft an email or granting access to full inboxes for tone calibration. 

This increases exposure and risk as AI remembers context, links data, and can sometimes surface details you didn’t explicitly share. There have been cases of “AI creep” where information that you wouldn’t expect the AI to have suddenly shows up, or AI gives you more data and information that you had asked for. This is indicative of the AI having access to more data than necessary. 

Educate and train employees to only share with AI what it needs to complete the task, and only use secure, pre-approved tools. 

Take a layered approach to AI adoption

With AI models, there's a lot of hype around independent autonomous agents, but they aren’t yet ready and reliable for use in all kinds of areas and use cases. Considering this, adopting AI in stages and gradually moving from semi-autonomous to autonomous would ensure that adoption is smooth and secure. 

Gradual, staged adoption lets you maintain control while scaling AI’s impact across your workflows.

No matter how advanced AI gets, it’s crucial to have a human in the loop, someone to validate results and stand behind what goes to the client.”

- Sivanand Sivaram, Head of AI Research, SuperOps

Moving securely into the agentic AI era

It isn’t enough to simply adopt AI, but to do it securely and responsibly. For IT teams and organizations, data security shouldn’t just be a compliance checkbox. When security isn’t just bolted on but is embedded in the core, in your governance, processes, and culture, it becomes the backbone of scalable, confident growth.

Users and leadership expect IT service providers to protect their data as carefully as they manage their own, and as AI becomes woven into every workflow, the importance of building and maintaining this trust only grows. In the MSPs niche in particular, only those that establish credibility will be able to win more clients and scale their businesses. And a secure, robust IT service management platform is indispensable for this. 

At SuperOps, our unified PSA-RMM platform is secure by default. We ensure that every AI model we use, every workflow we design, every feature we build has the security and privacy aspects covered.  Whether protecting user data, keeping endpoints secure and up-to-date, or upholding compliance standards, our cybersecurity comes second to none. 

To get your agentic AI transformation started, and learn more about SuperOps’ unified PSA-RMM platform that is powered by Agentic AI, schedule a demo with our experts today!