Swimlane has launched an
AI security operations center platform for MSSPs, giving them a central place to automate alert triage and investigations across multiple customer environments. Swimlane AI SOC for MSSPs is built on the company’s Turbine automation platform and includes case management, cross-tenant threat intelligence, AI-assisted investigations, dashboards, and integrations with service management tools.
The launch comes as MSSPs face rising alert volumes and pressure to support more customers without adding analysts at the same rate. Providers are also watching whether AI security vendors plan to support their managed services or eventually compete with them.
Swimlane says MSSPs that use the platform will keep control of the customer relationship, service model and customer data.
Cody Cornell, CEO of Swimlane, told MSSP Alert, “Our commitment isn’t just a statement; it’s built into how the product works. MSSPs who run their AI SOC on Swimlane Turbine keep the customer relationship, keep the data, and keep full ownership of the service they deliver.”
Cornell said Swimlane will provide the underlying AI and automation while leaving the managed service to the MSSP.
“Our business is providing the agentic AI and automation underneath, not the service on top of it, and that’s a structural choice, not just a policy we could reverse,” he said.
One command center for multiple customers
MSSPs often manage separate tools, policies, and workflows for each customer. Analysts may need to move between several consoles, ticketing systems, and security products while keeping customer environments separate.
Swimlane AI SOC brings cases from connected customer tenants into one command center. This includes open investigations, unassigned cases, and high- and critical-severity alerts.
Analysts can use the system to manage human-led investigations, automated actions and AI-driven workflows across customers. Case information is synchronized with the central console, which can help MSSPs apply more consistent processes.
The platform also includes tenant isolation and role-based access controls. These features are designed to keep customer data separate and limit analysts to the information they are authorized to access.
AI triage and investigations
The platform normalizes incoming alerts, adds threat intelligence, and checks them against related activity. It then produces a verdict, maps the activity to the MITRE ATT&CK framework, and creates an investigation plan. Swimlane says the system explains how it reached its findings, giving analysts more context than a risk score or recommendation alone.
This reduces the time analysts spend collecting evidence for routine alerts. Cases that require judgment, customer communication, or a higher-risk response can still be sent to an analyst.
Cornell said MSSPs should expect automation to increase in stages.
“Partners should expect a phased ramp to AI SOC onboarding, with help from Swimlane every step of the way,” he said. “Most reach meaningful automation, with high-confidence, lower-risk actions running without a human touch, within their first 90 days.”
Some smaller providers may move faster. Cornell said certain MSSPs have migrated their existing security orchestration, automation, and response playbooks to Swimlane AI SOC in as little as 30 days.
He also cited one MSSP that is automating more than 100 use cases and has reduced incident response time by 70%. Swimlane did not name the provider.
Threat intelligence across customer tenants
Swimlane also includes a cross-tenant threat intelligence layer that collects observables, enrichment results and verdicts from connected customer environments. When an alert is investigated for one customer, the threat information can be used to support investigations for other customers. This could help MSSPs identify repeated attack patterns, infrastructure or indicators across their client base.
The approach also raises questions about what data moves between customer environments.
Cornell said the system shares only unattributed threat indicators and verdicts, similar to the data found in a shared threat intelligence feed.
“The only data that moves between client environments is unattributed threat indicators and their verdicts, with no customer identity attached,” he said.
He said case details remain inside each customer’s tenant and are visible only to the MSSP.
“Case-level detail stays in each client’s own tenant and only ever surfaces to the MSSP, never to another client,” he said. “The intelligence layer sits on top of tenant isolation, and role-based access controls that keep each analyst scoped to only the data they’re authorized to see.”
This allows MSSPs to reuse threat information without sharing customer-specific details. Providers will still need to document how the information is handled and explain those controls to customers with strict privacy or compliance requirements.
Swimlane says the platform also reduces the need for custom professional services work by giving MSSPs a repeatable way to onboard and manage customers. The command sync layer sends case information to the central system. Integrations with ServiceNow and Jira allow incident workflows to remain inside the tools providers already use.
The platform includes dashboards for active cases, analyst workloads, customer engagement and overall portfolio health. Weekly reports show how many cases were created, closed, left open or triaged by AI.
More customers per analyst
Swimlane says the platform can help MSSPs improve margins by allowing each analyst to support more customers. Because the service runs on a single multi-tenant platform and does not charge per managed customer, providers can add clients without increasing headcount at the same rate.
“Adding another client doesn’t require a proportional add in analyst headcount, which is where the margin upside comes from: more customers served per analyst, not fewer analysts doing the same work,” Cornell said.
That could give MSSPs more room to grow while keeping labor costs under control. The actual margin impact, however, will still depend on pricing, alert volume and automation usage. Providers may charge customers a fixed monthly fee while their own AI and infrastructure costs change based on the number of alerts, integrations, and investigations.
