In the past year, I have seen cybersecurity conversations flip from “the operational pain points for MSSPs and security providers” to “AI is everywhere, we are lagging in adoption, AI is cutting into Tier 1 cybersecurity jobs, and customers want outcomes and not another AI promise.”
While AI is moving into MSSP operations, we are past the point of discussing its hype. More crucial is discussing where AI can take work off analysts, the risks it is creating, and what MSSPs, MSPs, and security providers need to have in place before they trust it inside client environments.
That was the focus of the MSSP Alert on-demand webcast,
“AI for MSSPs: Everything You Need to Know to Thrive in 2026.” The session looked at how MSPs and MSSPs are using AI today, why data quality matters, and where human oversight still needs to stay firmly in the workflow. The discussion kept coming back to a practical point: AI can help MSSPs and security providers move faster, but only if the basics are already in place.
AI Is Already Helping with the Work Analysts Hate
For MSSPs, the first useful AI use cases are already clear. AI can help with alert triage, investigation support, phishing classification, log normalization, report writing, ticket summaries, and knowledge retrieval. These are the repetitive tasks that slow down analysts and technicians every day.
Pinar Ormeci, CEO of Lexful, said AI is already changing SOC work, especially around triage and reporting.
“There’s no doubt about that, especially when it comes to triage, compression, collapsing the time between alerts and decisions, writing the reports that nobody wants to write,” Ormeci said.
That matters for MSSPs because response time, consistency, and analyst workload are all tied together. If AI can reduce the time it takes to understand an alert, it can help providers improve service delivery without putting every problem on human analysts first.
But Bad Data Makes AI Worse, Not Better
Still, Ormeci warned against treating AI as a cure for messy operations. “AI does not fix bad data. If your data is garbage, AI will just give you faster and more confident garbage,” she said.
That is one of the biggest issues for providers. Many providers still have client data spread across PSAs, RMMs, SIEMs, documentation tools, ticketing systems, and separate customer environments. If that data is incomplete or hard to connect, AI will struggle to produce reliable answers.
Brian J. Weiss, CEO of ITECH Solutions, made a similar point from the service provider side. He said the starting point is not buying AI for every tool. It is looking at the tools already in use and figuring out how data flows across the business.
“The first thing I think about is all the different tools we use and how spread out the data is that we use in our day-to-day workflows,” Weiss said.
For MSPs and MSSPs, AI readiness often starts with reducing tech debt, cleaning up data and deciding which business systems should become the core operating layer. That work is not flashy, but it is what determines whether AI becomes useful or just adds another layer of noise.
Documentation Is a Practical Starting Point
One of the clearest opportunities is documentation. MSPs and MSSPs often solve the same issue more than once because the fix never makes it back into a runbook, SOP, or knowledge base.
Ormeci said AI can help by watching ticket activity and turning resolved work into reusable knowledge. “You solve a ticket, and then unfortunately you end up solving the same ticket over and over across the MSP because those resolutions do not get captured into documents,” she said.
That is a real operational issue. If AI can help capture fixes, update documentation, and surface the right context next time, technicians can move faster. It also gives newer analysts a better starting point when they are working across unfamiliar client environments.
Client onboarding is another practical use case. Many MSSPs inherit messy documentation, missing asset details, and uneven client histories. AI can help identify gaps and organize that information so technicians understand the environment sooner. That matters because onboarding quality affects response speed, compliance readiness, and the customer’s early trust in the provider.
MSSPs Need to Tie AI to Business Outcomes
Theresa Lanowitz, Chief Evangelist at LevelBlue, said MSSPs need to connect AI adoption to business outcomes. For her, AI should help providers improve SLAs, margins, and talent strategy.
“You can get improved margins as well, because you’re reducing the amount of headcount that you’re doing. You are not solving the same problem over and over,” Lanowitz said.
The point is not that AI removes people from the business. It is that it can reduce the low-value repeat work that keeps skilled staff stuck in the same problems. That gives MSSPs more room to use senior staff on higher-value work, including incident response, advisory services, threat hunting and customer strategy.
Lanowitz also said MSSPs need to remember their role with clients. “As an MSSP, you are really a strategic extension of that client’s team,” she said.
That means AI cannot be treated as an internal shortcut only. Providers need to understand how AI changes the service experience, how it affects trust, and where customers need transparency around how their data is being handled.
AI Agents Need Clear Boundaries
The biggest caution is around AI agents. Assistants can summarize, draft and suggest. Agents can take action. That creates more value, but it also creates more risk.
Lanowitz said providers should be careful about how much authority they give AI inside security workflows. “You don’t want agents to be able to go off, for example, and change policy,” she said.
That is where human oversight matters. Policy changes, incident decisions, compliance actions, and client-facing recommendations still need review. MSSPs can use AI to prepare the work, organize the evidence, and speed up response, but humans still need to own the outcome.
Weiss also tied AI value to measurable SOC outcomes.
“In a SOC, it’s all about mean time,” he said, pointing to mean time to detect, acknowledge, contain, respond, and remediate.
If AI can give analysts the right context faster, it can help reduce those timelines. But that depends on clean data, clear workflows, and strong guardrails. Without those pieces, AI can create more alerts, more review work, and more operational confusion.
Start With People and Process, Then Add the Tool
The people side matters too. Weiss said organizations should start with people and process before choosing technology. “I’ve made mistakes in the past of trying to lead with technology, ’cause I’m an engineer, and I love technology,” he said.
For MSSPs, that is a useful warning. AI tools will not fix unclear workflows, weak documentation, or inconsistent service delivery. They will expose those problems.
Lanowitz put it simply: “Begin with the end in mind.”
MSSPs need to define what success looks like before they roll AI into daily operations. That means deciding which workflows are safe to automate, which metrics should improve, what data can be used, and when humans must approve the outcome.
The MSSP Opportunity Is Real, But It Starts With Discipline
AI can help MSSPs move faster, cut down on repetitive work and build new services around AI security and governance. It can also help providers support more customers without piling every new task onto analysts who are already stretched thin.
But the MSSPs that get the most value from AI will be the ones that do the hard work first. That means cleaning up data, documenting processes, using secure models and keeping humans involved where judgment matters. Those are the pieces that turn AI from another feature into something that actually improves service delivery. For MSSPs and providers, the point is simple: AI can become a real operating advantage, but only when the foundation is ready.
