Define the production boundary.
Explore the controls production AI teams need to specify. This is an architecture discussion, not a claim that enterprise controls are deployed or independently certified.
ENTERPRISE CONTROL / ARCHITECTURE PREVIEWProduction AI should
Production AI should
not fail silently.
A reference architecture for the controls production teams evaluate. The capabilities below are proposed—not included in the current benchmark workspace and not a security certification.
01 / PRIVATE NETWORKING
Network boundaries.
VPC and private-endpoint options to evaluate.
02 / DATA CONTROL
Data on your terms.
Retention and regional-processing policies to define.
03 / ACCESS
Governance by design.
RBAC, SSO and workspace controls to specify.
04 / OBSERVABILITY
Evidence beyond the UI.
Metrics, traces and log export in the reference design.
05 / POLICY ENGINE
Explicit rules.
Centralized inference constraints and decision policies.
06 / SUPPORT
Engineering context.
Discuss deployment needs without an implied support SLA.
DEPLOYMENT DISCOVERY
Questions worth answering early.
- Network and identity
- Which application identities may create requests? What trust boundary separates the application, control plane and provider? Which private-network paths are required?
- Data lifecycle
- Which fields may be retained, for how long, in which regions, and under which processing agreements? How are exports and deletion verified?
- Policy and reliability
- What constraints drive selection? What happens on timeout, rate limits or degraded quality? How are retries bounded and failures preserved?
- Operational evidence
- Which metrics, traces and alerts support the deployment? What assumptions sit behind an availability target or support commitment?