Security Operations Centres process some of the most sensitive data available within an organization. Logs may expose internal addresses, user activity, system configurations, vulnerabilities and details about active incidents. This creates specific requirements for any AI capability introduced into the SOC.
aSIEMmetry addresses this challenge through a self-hosted approach. The project’s models are intended to operate in environments where organizations maintain control over data processing, storage and access.
Keeping operational data under local control
Public AI services can be useful for many general tasks, but security telemetry requires stronger safeguards. Organizations need to know where their data is processed, whether it is retained and which third parties may have access to it.
Self-hosting enables the project components to run within an organization’s approved infrastructure. This can support internal security policies, contractual requirements and regulatory obligations while reducing dependence on external model endpoints.
Designing the complete AI pipeline
A self-hosted model is only one part of the solution. The surrounding pipeline must collect, prepare and route security data securely. It must also manage model versions, access rights, output storage and operational monitoring.
The aSIEMmetry architecture combines the security entropy multi-agent model with the specialized LLM SOCagent. Both components require controlled access to relevant data while remaining separated from information that is not necessary for their function.
Balancing performance and infrastructure requirements
Local deployment also introduces practical constraints. Model size, processing latency, hardware resources and update procedures must be considered. The project must find a balance between analytical capability and the infrastructure available to the target SOC environment.
Operational teams also need a clear method for deploying improvements, validating new model versions and reverting changes when required. These lifecycle controls are necessary for maintaining stable security operations.
By developing self-hosted AI pipelines, aSIEMmetry supports a deployment model in which organizations can benefit from advanced analysis without giving up control over sensitive telemetry. This approach is central to the project’s objective of creating trusted and operationally relevant AI capabilities for private and national SOCs.