The whole AI program on one screen
AI interactions, agent runs, chat messages, and active users, in one live view. Adoption stops being a survey question: the numbers show which teams are getting value and where enablement should go next.
Total spend, API calls, cache savings, and budget utilization sit beside adoption in the same console. Every request carries user, team, department, cost center, model, and provider, so every line of spend has an owner.
Useful chat and agent outputs become reusable examples, shared across the team as workflows, findings, and tools. Everything shared stays inside the workspace, under the same policy as the work that produced it.
Spend is live in the same console
Spend is measured on the governed path as it happens and attributed to the people, teams, and agents who created it. Budgets enforce inline, before the model call, so a cap is a decision made at enforcement time rather than a line on next month's invoice.
Security decisions double as adoption data
These dashboards are not a separate analytics pipeline. They are built from the decisions AI Security Runtime™ records as it inspects every interaction, so usage, spend, and security are views of the same record.
| OBSERVE | Every interaction counted as it happens on the enforcement path, not reconstructed from provider logs afterward |
| DETECT | The Semantic Security Engine™ reads each interaction for meaning and intent; every judgment becomes a data point |
| ENFORCE | Budget caps land in the same inline decision; allow, redact, hold for human review, or block for everything else |
| TRACE | Usage, adoption, and spend dashboards fed live by runtime decisions; audit evidence exports to your SIEM |
