Swimlane Adds AI Agents and Custom Detection to Turbine
The platform now supports natural-language app building and Model Context Protocol for broader integration.
Updated on Oct. 28, 2026 in Artificial Intelligence
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Swimlane has released new capabilities for its Turbine platform, enabling security teams to implement custom detection logic and two-way Model Context Protocol (MCP) support. Additionally, the company introduced a Hero AI agent that allows users to build security applications using natural-language prompts.
Why it matters
The integration addresses the gap between standardized security tools and the need for enterprise-specific detection logic, particularly against emerging AI-centric threats like prompt injection. It enables teams to extend platform functionality beyond vendor-defined roadmaps.
The updated platform handles tens of thousands of records in a single big data action across parallel compute nodes. The system now supports two-way Model Context Protocol, allowing Turbine to function as both a host for third-party tools and a server for external AI assistants.
The players
Swimlane
A security automation company providing the Turbine platform for automated incident response and application building.
The details
Turbine processes data using parallel compute nodes to scale evaluation against custom-defined detection logic. When building applications, the Hero AI agent interprets natural-language requests to generate security workflows. All AI-generated application changes require human review before they are committed to the system.
Timeline
October 2026: Swimlane released new Turbine platform capabilities.
The Tech Race
This move positions Swimlane within the broader movement to standardize data access for AI agents via the Model Context Protocol. It contrasts with proprietary, closed-loop security systems that limit how third-party AI assistants can interact with enterprise data volumes.
Security teams can now design custom detection logic that automates responses to threats not covered by pre-built vendor rules. Users should note that while the Hero AI agent accelerates application building, human verification of the code remains a mandatory prerequisite before deployment.
The takeaway
The move enables more granular control over automated threat detection, reflecting a shift toward AI-assisted development in security operations. Security managers should monitor how this human-in-the-loop requirement evolves as these AI agents gain the ability to handle increasingly complex security applications.
Further reading
For broader trends in enterprise automation, visit our Artificial Intelligence section.
Live Poll
Do you prefer security tools that allow for custom detection logic over vendor-defined settings?
