Operational Governance
In DevelopmentMake AI operations accountable.
A planned operating model for AI activity, supported control and attributable cost.
Build on Organizational Alignment, supported runtime controls and decision evidence. Network-oriented integrations and cross-organizational operational reporting are still in development.
- Managed pathAI Egress
Identity, destination and policy at the execution boundary.
- Approved sourcesShadow AI Intelligence
Activity, attribution and coverage for accountable review.
- Usage and ratesTokenomics & AI Spend
Consumption, cost assumptions and investment context.
From adoption to accountable operations
A connected view of how AI operates.
AI decisions cross engineering, security, finance and business teams. The planned model connects supported request paths, observed activity and attributable usage. It is not a live enterprise dashboard.
Agent scale, throughput, latency and operating reliability require workload-specific measurement and customer qualification. Contract fixtures are not production benchmarks.
01 / AI Egress
Enterprise network harness integrations are planned. The existing Gateway / inline proxy governs supported routed requests; it does not establish organization-wide network coverage.
Give approved AI a path you can govern.
Make the supported route between a workload and its AI provider an explicit governance boundary. Connect application identity, permitted destinations and policy to the request—and retain the decision and the action that followed.
- IdentifyManaged workload
Known application or service identity and an explicit route.
- EvaluatePolicy boundary
Evaluate the supported request against the permitted scope.
- ExecuteApproved provider
Forward eligible requests and retain execution and release evidence.
The control boundary is the supported managed path. VPN or network routing alone does not expose plaintext TLS prompts. Content inspection requires a qualified integration with authorized access to the request. Other traffic remains outside that control boundary.
Make the boundary explicit.
Agree on the workload, destination, data scope and accountable owner. A supported integration makes those choices meaningful at the point of execution.
Keep decisions and actions together.
Distinguish permission to proceed, actual forwarding, provider completion and output release. Record an intervention at the stage where it happened.
02 / Shadow AI Intelligence
Source ingestion, discovery and attribution require qualified integrations. No source provides complete visibility into every AI tool or workload.
Turn scattered signals into accountable review.
The planned capability uses enterprise-approved sources to surface possible unreviewed services and ownership gaps. Coverage, attribution and supported sources must be qualified before any detection claim.
- Activity and attribution
- Connect source observations to known workloads or teams, with identity confidence in view.
- Policy designation
- Distinguish sanctioned, under-review and unknown use against your organization's decisions.
- Coverage and freshness
- Keep source scope, collection timing and visibility gaps alongside the activity being reviewed.
Proxy and secure web gateway Network telemetry Endpoint and cloud activity
An observation describes what a source saw. Policy status and an enforced action need their own evidence.
03 / Tokenomics & AI Spend
Cross-organizational usage, allocation and cost reporting are planned. Provider usage metadata at a supported boundary is not a complete enterprise FinOps capability.
Connect consumption to investment decisions.
The planned reporting model will connect attributable provider usage with stated rates and ownership. Existing provider-reported metadata is limited to supported integrations; allocation, forecasting and reconciliation are not released enterprise capabilities.
- Attributable usage
- Keep provider, model and workload context attached to the usage that supports each decision.
- Transparent estimates
- Connect measured usage to a stated rate version and visible cost assumptions.
- Financial reconciliation
- Keep estimated spend, forecasts and reconciled provider charges distinct, with their sources intact.
Consumption Rate assumptions Ownership Coverage
Missing usage stays unknown. Incomplete coverage is visible, so it cannot be mistaken for zero consumption or zero cost.
Evidence that serves a decision
Different responsibilities. A shared operating picture.
These conceptual perspectives describe questions the developing capability aims to support. They are not measured customer results.
For Leadership
A clearer basis for where AI should grow.
Connect operational activity to organizational priorities and accountability. Understand where an owner, a managed path or better evidence would change an investment decision.
Prioritize investment with a common view of opportunity, responsibility and control.
- The decision
- Which workloads should we expand, review or bring into a governed path?
- The evidence
- Attributed activity, ownership, policy outcomes and the coverage behind each conclusion.
For Engineering
Find the control point. Understand the outcome.
Follow the relationship between a workload, its supported request path and the resulting evidence. Give teams a concrete basis for improving integrations and resolving exceptions.
Turn an operational question into a traceable engineering investigation.
- The decision
- Which workload, route or policy needs attention—and what happened at the boundary?
- The evidence
- Application, provider and model context; policy version; execution stage; and the action receipt.
For Finance
Explain the cost before committing the budget.
Connect provider-reported consumption to workload ownership and explicit rate assumptions. Compare cost drivers while keeping estimated and reconciled amounts distinct.
Support investment and allocation decisions with evidence that can be explained.
- The decision
- Where is attributable spend concentrated, and which workload or model choices should we examine?
- The evidence
- Known usage, rate versions, allocation context, provider charges and the gaps in coverage.
Start with the operating question
Bring your next AI decision into focus.
Start with a workload, the teams responsible for it and the activity or cost question that matters. Build the governance conversation around that decision.