When One Action Hits Every Client, Governance Decides the Outcome

Imagine a hypothetical that’s taught in law school every semester:

A delivery driver abandons his route to join a drum circle for three days. On his way back, he causes an accident. Who pays, the driver or the delivery company? To most people, the answer is obvious. The driver stepped outside the scope of his job. That’s on him.

Now change the facts slightly. Between deliveries, the driver stops at a convenience store to use the restroom and grab a coffee. On the way out, he hits another vehicle. Who pays now? That answer isn’t as clean. A short detour for a reasonable purpose still looks like part of the job.

Those are the easy cases. From there, the hypotheticals get more interesting. What if he bought a beer to drink later? What if he got into an argument at one of the stops? What if the stop was “quick”… but not that quick? At that point, the answers start to turn on scope, authority, and whether the behavior was foreseeable.

Now apply that same thinking to AI. Organizations are starting to deploy agentic systems to monitor environments, remediate alerts, deploy patches, adjust configurations, and automate administrative actions, often across multiple systems or clients.

Which means the real question isn’t just what the system can do. It’s what you’ve actually authorized it to do—and how far that authority extends.

Now that we have AI agents, we need to think through the tasks and guidelines that we give them.  MSPs are beginning to use agentic AI to monitor systems, remediate alerts, deploy patches, adjust configurations, and automate administrative actions across multiple client environments.

That shift has practical implications that are easy to underestimate, particularly as these tools move from limited use cases into core operational workflows.

When an MSP deploys an autonomous agent with cross-client visibility or administrative privileges, the risk profile changes. A mis-scoped permission, a flawed automation rule, or a poorly tested workflow doesn’t stay contained to one environment. It can affect many. In practical terms, an agent designed to “self-heal” endpoints or adjust firewall rules could propagate an error across dozens of client networks in a very short period of time. What would previously have been a localized mistake becomes a multi-client incident.

That's a different category of risk, both in scale and in how it is evaluated after the fact.

From a legal and insurance perspective, concentration matters. Recent ransomware and breach cases have focused heavily on whether organizations exercised “reasonable security.” The presence of autonomous systems doesn’t lower that standard. It raises expectations that access controls, logging, and risk review evolve alongside capability.

It also shifts how decisions are scrutinized. When authority is delegated to an autonomous system, the focus is no longer just on what happened, but on why that level of authority was granted in the first place, and under what controls. If an AI-driven automation contributes to a multi-client incident, the questions that follow are predictable.

Was the system subject to a formal risk assessment before deployment? Were permissions segmented by client, or broadly applied? Was activity logged in a way that allows reconstruction of events? Were safeguards tested and documented? Were there controls in place to prevent actions in one environment from affecting another?

Was there a clear understanding internally of what the system was authorized to do and, just as importantly, what it was not authorized to do?

These are procedural questions as much as they are technical ones. And procedural discipline significantly impacts how an organization is evaluated after the fact.

The cyber insurance market is evolving in parallel. Underwriters are already looking closely at governance maturity, third-party oversight, and documentation practices. The use of autonomous tools across client environments introduces aggregation risk, which insurers track carefully. A single misconfigured agent affecting multiple insureds won’t be viewed as an innovation issue. It will be viewed as a control issue. This is where many organizations run into trouble. The technology is implemented faster than the governance around it. Automation is treated as an efficiency decision, when in reality it is also a risk allocation decision.

Who — or what — has authority to act? Across how many environments? Under what conditions? With what limitations?

Those questions need clear answers before deployment, not after an incident.

None of this suggests MSPs should avoid automation. The operational benefits are clear. But autonomy must be treated with the same level of scrutiny as a human engineer with elevated privileges across multiple environments—and in some respects, more scrutiny, given the speed and scale at which automated actions can execute.

At a minimum, that means:

  • Client-segmented permissions rather than global authority
  • Controlled rollout and testing before broad deployment
  • Logging that distinguishes automated actions from human activity
  • Formal review and approval of AI-enabled workflows
  • Clear documentation of how autonomous systems are governed
  • Defined limits on what actions can be taken autonomously versus those requiring human review

MSPs that withstand litigation and insurance scrutiny most effectively are those that can demonstrate how they approached risk before an incident occurred. Not just that the technology was capable, but that its authority was intentionally limited and periodically reviewed. Agentic AI doesn’t create risk on its own. But it does amplify the consequences of poorly defined authority. When autonomy scales, governance has to scale with it.

MSPs that proactively account for these guardrails will differentiate themselves from those that assume automation reduces responsibility.