Data boundaries
Separate source systems, retrieval scopes, retention rules, and sensitive fields before an agent can act.
Security controls for AI systems people can trust.
AICube designs the governance layer around every AI product: permissions, evals, audit trails, routing, monitoring, and incident paths.
Trust control console
Production AI boundary
Identity
SSO / RBAC
Data scope
Scoped retrieval
Runtime
Tool limits
Request path
Scoped before the agent can use data or tools.
01
Request
user + role
02
Policy
scope check
03
Context
approved data
04
Action
tool gate
05
Audit
trace saved
Risk monitor
Low exposure
Policy gate
Enforced
Eval suite
Passing
Audit stream
Live
Control surface
Governance is designed into the product
Security is not a final checklist. It shapes the data model, agent behavior, release process, and operator experience.
Permission model
Role-based access, least-privilege tools, approval gates, and environment-specific credentials.
Audit trail
Trace prompts, retrieved context, tool calls, human approvals, outputs, and release changes.
Quality monitoring
Track accuracy, refusal behavior, latency, cost, drift, and task completion rates after launch.
Model governance
Route by risk, cost, latency, and sensitivity. Test model or prompt changes before rollout.
Incident readiness
Fallback paths, kill switches, escalation rules, and review queues for production operations.
Security review
Prepared for the questions enterprise teams ask
We structure implementation decisions so product, security, data, and engineering leaders can review the same system.
1
Access
SSO-ready roles, scoped tools, environment permissions, approval queues
2
Data
PII handling, retrieval boundaries, source freshness, retention choices
3
Evals
Golden sets, regression tests, hallucination checks, release thresholds
4
Operations
Tracing, cost visibility, latency budgets, escalation and rollback
A safer path from prototype to production
We define what the AI can know, what it can do, when a human must approve, how changes are tested, and how incidents are handled. That operating model becomes part of the product, not a separate document.
Security
Design the trust layer before the AI scales
Tell us where AI will touch data, tools, and users. We will map the controls needed for a production launch.