Data processing and privacy boundaries for AI security work.
Commercial data processing must describe what is processed, where scans run, what leaves the environment, how evidence is retained, and how deletion works.
Data routes
AI security buyers need clear answers about prompts, repositories, traces, artifacts, evidence bundles, and reports.
Partners
OEM, scanner-provider, MSSP, reseller, consulting, private-label, and technology partner programs.
Licensing
Enterprise, embedded, OEM, white-label, offline, air-gapped, academic, startup, and usage-credit licensing.
Operations
Deployment, support, SLA, security, data-processing, audit, success, and implementation operations.
Commercial Contact
Start an OEM, reseller, MSSP, enterprise, private-label, procurement, or deployment conversation.
Keep data boundaries explicit
Different deployment models have different data behavior. SaaS, local worker, OEM sidecar, and air-gapped execution must be described separately.
Local execution options
Keep sensitive code and traces local where required.
Artifact retention controls
Define what evidence is uploaded, retained, exported, or deleted.
Partner-controlled egress
OEM and MSSP partners may control what data reaches their own systems.
Build the right commercial path
Use a focused pilot to align the technical integration, licensing structure, support model, and customer-facing packaging.