Solution · AI Governance & Operations
Runtime AI Control Plane: Get Visibility, Identity, and a Kill-Switch Over Every Agent in Your Tenant
Every sanctioned agent behind SSO, policy, model routing, and a tamper-evident audit trail — with an emergency stop per user, agent, connector, and tool.
What this solution does
A runtime AI control plane puts every sanctioned agent behind four layers — identity, policy, model routing, and execution telemetry — so a CIO can answer 'what's actually running, who has access, and what controls are enforced at runtime?' in one place. We inventory current AI usage, force SSO and tenant accounts, route sanctioned calls through a gateway, classify tools by action level, require human approval for high-risk classes, log every call to a tamper-evident audit store, and add kill-switches per user, agent, connector, and tool.
The buyer problem
- ›AI usage spreading faster than central IT can inventory or govern
- ›Users signing agents into tenant data with personal accounts
- ›No answer to 'what's actually running, who has access, what controls are enforced at runtime?'
- ›No emergency stop when a specific agent, connector, or tool starts misbehaving
- ›Audit and compliance evidence scattered across vendor consoles
Good-fit signals
- 300–2,000 employees with Microsoft 365 or Google Workspace and SSO already in place
- A handful of sanctioned or unsanctioned AI tools already in the tenant
- Security or GRC lead escalating shadow-AI risk to the CIO
- Willingness to route sanctioned agent traffic through a gateway
Pilot scope
In scope
- +AI tool + agent inventory across the tenant (sanctioned and shadow)
- +SSO + tenant-account enforcement for approved tools
- +Agent gateway with policy engine and model router
- +Tool classification (read / recommend / write / execute) with per-class approval rules
- +Tamper-evident audit log wired into SIEM / observability
- +Kill-switches per user, agent, connector, and tool scope
Out of pilot scope
- −Replacing existing SSO / identity provider
- −Rebuilding first-party SaaS analytics
- −Ownership of end-user tool training and change management
Timeline: 10–16 weeks · Investment: Fixed-fee program; sized after Workflow Triage
Systems we typically integrate
- Entra ID / Okta (SSO + device trust)
- Microsoft 365 / Google Workspace
- Slack / Teams
- MCP servers + custom tool adapters
- SIEM / observability (Splunk, Datadog, Elastic)
- Model providers (Anthropic, OpenAI, Azure OpenAI, local)
Where humans stay in the loop
- Write / execute tool classes require human approval by default
- Cross-tenant or external-send actions gated by a named approver
- Model-routing exceptions escalate to platform owner
- Every kill-switch action logged with actor, reason, and revert path
KPI scorecard
| Metric | Typical baseline | Pilot target |
|---|---|---|
| Sanctioned agents behind central gate | <20% | >95% |
| Tool calls with complete provenance | Partial | 100% |
| Mean time to disable a risky agent | Hours–days | <5 minutes |
| Blocked unauthorized actions / month | Unknown | Measured & trending |
Risks and the controls we put in place
Incomplete inventory leaves bypass channels
Discovery combines identity-provider logs, network egress, expense data, and admin console exports; re-run quarterly.
Approval fatigue drives shadow workarounds
Tool classification tunes approval thresholds by risk tier; low-risk read actions stay frictionless.
Inconsistent identity across tools
SSO enforcement + service-account registry; personal-account sign-ins blocked at the gateway.
Audit trail tampering
Append-only log with hash chaining; separate write scope from read scope; SIEM ingestion within minutes.
How we help
Inventory-first
We start by mapping every AI entry point in your tenant — sanctioned and shadow — before designing the gate.
Reference architecture we've shipped
SSO → gateway → policy → router → tool adapters → audit is not a diagram; it's the pattern we install.
Kill-switch drills
We rehearse revocation for user, agent, connector, and tool scope so your first drill isn't an incident.
Continuous red-teaming
We stand up policy tests that run continuously against your gateway so drift is detected, not discovered.
Frequently asked questions
How is a control plane different from a copilot management console?+
Vendor consoles only see their own tool. A control plane sees every sanctioned agent — Copilot, Claude, GPT-based tools, custom agents — through one identity, policy, and audit surface.
Do we have to route all AI traffic through the gateway?+
Only sanctioned agents. Shadow tools are surfaced by the inventory and either sanctioned + routed, or blocked at the identity provider.
How does this affect end-user experience?+
For read-class tools, negligibly. Write and execute classes prompt for approval — that's the point. Low-risk paths stay frictionless.
How does the gateway handle MCP servers?+
MCP is a first-class citizen. Every tool exposed via MCP is classified, gated, logged, and reversible like any other tool adapter.
What audit standard does the log support?+
Append-only, hash-chained, exportable to SIEM. Fields align with NIST AI RMF Measure/Manage and ISO/IEC 42001 evidence expectations.
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