AI that runs where your security operates.

Why security teams choose private AI

Security telemetry is sensitive twice over. It contains your customers' data, and it reveals your defensive posture. Sending alerts and indicators to a third-party model API creates two attack surfaces: the data exposure itself, and the model provider's own security posture.

We build AI that runs inside your environment. Air-gapped if you need it. On-prem if you operate that way. VPC if cloud is your default. The model provider does not see your alerts.

Where we focus

• SOC: alert triage, false-positive reduction, analyst summarization
• Threat hunting: conversational queries against SIEM, EDR, NDR
• Detection engineering: rule authoring, playbook generation, MITRE mapping
• Customer-facing AI inside security products without changing data flow
• Internal copilots for security ops, GRC, and policy teams

Why deployment shape matters here

Air-gapped, on-prem, and customer-VPC deployment isn't an aesthetic choice in security — it's the only deployment shape some buyers will accept. We've shipped on all three patterns, and the architecture decisions cascade from there:

• Air-gapped: open-weight model, customer-hosted inference, no telemetry, signed model artifacts.
• On-prem: dedicated GPU infrastructure, customer-controlled MLOps, private model registry.
• Customer VPC: model in your cloud account, customer-managed keys, no egress to vendor SaaS.

How we engage

Security customers usually start with an AI Pilot on a real alert backlog (post-anonymization). From there, Private AI Deployment establishes the pattern that your security architecture team can sign off on. Managed AI maintains the model against shifting threat landscapes and emerging adversarial techniques.