As managed service providers (MSPs) adopt agentic AI to automate IT operations, governance and security controls are becoming increasingly important, particularly for providers managing multiple customer environments.
Autonomous AI agents can accelerate tasks such as endpoint management, patching, and remediation, but excessive permissions or insufficient oversight can expose customers to security and compliance risks. For MSPs, the challenge is balancing automation with human approval, access controls, and transparency.
Channel Insider spoke with Apu Pavithran, CEO of Hexnode, about how MSPs can implement agentic AI governance, establish safeguards for autonomous systems, and maintain customer trust as AI adoption expands heading into 2027.
Agentic AI adoption and productivity challenges for MSPs
Agentic AI demand, or at least interest, is exploding. What does that signal to MSPs in terms of how they can leverage those tools in their operations?
Providers and customers see the potential efficiency gains and don’t want to be left behind. This is clear in adoption – about one-third (32%) of MSPs have adopted AI tools for their operations, with another 46% piloting or planning them within the next 12 months.
So far, however, the hype is moving faster than results – just 12% can point to measurable productivity gains. This suggests getting AI right is less about the tools and more about the operational foundation to deploy them consistently across complex environments.
Agents are a good example of helping providers manage some of the tech “grunt work” and do more with less. In theory, a single command can become a force multiplier for response and remediation. But in practice, an agent with too much privilege can turn a single bad action into an incident at every client.
This double-edged sword shows why safeguards are non-negotiable. Going forward, providers with practical experience will be best placed for what’s coming. That’s why I suggest getting hands-on with the technology and onboarding internally. Adopt automation for high-volume, low-risk work that can be undone and get a better idea of how this technology can work for you and your customers.
Agentic AI governance, security risks, and customer trust
What governance controls must an MSP implement to ensure these agentic tools/systems do not pose risks exceeding their value?
Providers must retain final say over any automation. Devices are a good example of why. An agent working through a management console can push a configuration, a patch, or a wipe to thousands of endpoints at once, so the controls need to be in place before it does.
Governance controls at onboarding give the best chance to ensure automation delivers as defined. This requires scoping each agent’s permissions to a single client at a time, with its own identity and least-privilege access, rather than shared admin credentials. This way, a mistake at one client stays at one client. Case in point from earlier this year: an agent used a token that it shouldn’t have had to instantly and erroneously delete a company’s entire production database and backups, which then had knock-on effects for customers.
I also suggest making sure that any big device change – like a wipe or an unenrollment – waits for a named approver. Rolling out changes in stages helps too, confirming success with a small pilot group before going wide.
We applied this thinking to Synapse, our agentic orchestration layer for IT and security operations. Agents work within guardrails, and anything sensitive waits at an approval gate until a person signs off. Every step also lands in an execution history, so a provider can show a client exactly what an agent did and why.
What do MSPs risk when they don’t transparently communicate with customers that they’ve introduced agentic AI in operations?
They risk their client relationships. We spoke last year about partners becoming trusted security advisors by teaching clients security basics and compliance best practices, and by building lasting trust. A big part of maintaining this advisor status going forward will come down to agentic governance. After all, if partners sell security solutions based on protecting ecosystems and preventing breaches, a rogue agent can quickly ruin reputations. Trust will evaporate if providers are inadvertently endangering their customers’ security posture. The same goes for providers who start using these solutions without being transparent.
It’s also worth remembering that even the labs creating these solutions sometimes struggle to control them. OpenAI was in the news (again) after accessing several public service websites in Australia without permission, saying it was “sorry and working to do better“, and then notifying 100+ other organizations about more cases of unauthorized agents.
Put rogue agents in front of clients and “sorry” won’t cut it. AI labs can beg forgiveness, but partners must ask permission up front and make sure it’s secure across the stack because, otherwise, that all-important foundation of trust will be broken.
For this reason, transparency is key. Tell clients where agents can run and what they’re allowed to do, get their sign-off before an agent acts in their environment, and include it in the managed service agreement, including how and how quickly you’ll notify them if an agent does something it shouldn’t.
How MSPs can balance AI automation and compliance in 2027
How should MSPs balance AI gains and governance requirements as we enter 2027?
Carefully. AI will continue to heat up channel competition, with clients expecting faster, better services and providers using agents to handle more manual tasks. But the problem remains that about half (51%) of MSPs see data governance and compliance challenges as the main obstacles to AI adoption. Agents gone wrong can not only damage a client’s security posture but also hurt compliance.
Nobody wants that. Ensuring gains occur in lockstep with governance therefore requires a few things. Only work with vendors that build controls into their automation from the start. If a tool can’t show you what an agent did and why, it isn’t ready for a client environment.
Another good idea is to treat agents no differently than a new technician. Only give them supervised work that requires approvals, and keep a log of their performance. This kind of information is invaluable when showing the client what’s being automated and deciding whether there’s a case to hand over even more workload.
This space isn’t slowing down heading into the new year. Indeed, as Howard M. Cohen recently pointed out, providers who can implement, manage, and improve AI-powered workflows are on their way to offering a new level of service delivery and becoming “AI MSPs”. Good governance is what gets them there.
My advice is that providers be clear-eyed about the challenges and opportunities of automation and adopt it at their own pace, ensuring that agents are adequately controlled and set up for success. Those who get governance right will be the ones clients trust with AI.