Managed AI Runtime Security & Cost Governance
Govern the AI You Already Run — Without Shutting Innovation Down.
One governed runtime path that brings cost and security under control at the moment of action — without slowing AI down.
Book a DemoWhy AI Spend and Risk Get Away From You
Token and inference charges become material and hard to attribute. By the time finance sees the invoice, the runaway session, the expensive model, and the repeated prompt context have already been paid for — with no per-user, per-agent, or per-model breakdown to act on.
Lightweight, repetitive tasks run on premium models, and repeated prompt prefixes are paid at full input rates. Without workload-specific model roles and preserved prompt caching, avoidable inference cost compounds quietly across every team.
A valid user can still trigger an unsafe action. Agents cross model, identity, data, and tool boundaries, and a model's answer alone should never authorize an external, financial, or destructive tool call. Authentication says who logged in — not whether this exact request should act.
How it works
Every onboarded request rides one governed path. It reaches a deterministic policy checkpoint carrying verified identity, budgets, and approvals — which returns allow, require approval, or deny before any model or tool runs, then emits metadata-first evidence and immutable usage facts.
How the Co-Managed Lifecycle Works
Onboard & Observe
We explicitly register the in-scope AI experiences, model endpoints, MCP servers, tools, and identity paths, then mandate the integrated gateway and managed-harness route. Native harness artifacts target Windows x64 and macOS x64/arm64 for your MDM to distribute. We establish token, model-choice, cache, cost, and security baselines for governed traffic — without changing execution. Unregistered assets and direct traffic stay out of scope by design.
Recommend
We filter Cost & Usage by date, user, agent, client, and model, correlate daily spend trends with security events, then propose model-role assignments, the hosted managed catalog, session budgets, deterministic policies, approval boundaries, and response procedures — validated with your application and security owners before anything is enforced.
Enforce & Optimize
Hosted chat exposes only the configured managed catalog and permits idle-session model switches that preserve the conversation. Managed-harness roles route eligible work to the economy model route while higher-capability work stays on the standard route. Short-lived, budget-capped credentials enforce the allowed routes, and deterministic policy returns allow, deny, or approval-required before any model or tool runs.
Respond & Tune
We triage findings, coordinate your containment, and review the authenticated tenant's estimated savings alongside security evidence. Rate cards and role assignments are refined against workload-quality evidence, policy is tuned, and correlated records flow to Microsoft Sentinel and, when you configure it, Google SecOps.
What this covers
- Explicit onboarding and inventory of models, agents, MCP servers, tools, identities, and governed routes
- Identity lineage from human requester through workload, model, tool, target, and outcome
- Deterministic policy at the model and tool gateways: allow, deny, require approval, or audited fallback
- Exact-request approvals with resource, parameters, identity, and context intact
- Preserved provider-native prompt caching and workload-specific economy/standard model roles
- Per-session budget caps and policy-defined output-token and temperature limits
- Tenant-wide Cost & Usage: invocations, tokens, cache reads, and estimated cost, cache, and model-choice savings
- Metadata-first, tamper-evident evidence and immutable usage facts — no prompts or completions retained
- Native Windows x64 and macOS x64/arm64 harness delivery for your MDM, plus a hosted chat and management console
Governed Runtime Path vs. Ungoverned AI
- Agents, keys, and tools vary team to team
- Spend attributed only after the invoice
- Premium models run routine work
- Model output effectively authorizes tool actions
- Fragmented logs make reconstruction slow
- One governed path for onboarded model and tool activity
- Usage facts created transactionally with each event
- Eligible work routed to the economy model role
- Deterministic policy decides before anything executes
- Correlated, tamper-evident evidence to Sentinel
Who this is for
The AI Security Control Plane is built for teams that prioritized AI adoption and now need to replace fragmented experimentation with a governed operating model — without shutting useful workflows down. The current pattern is especially relevant to professional-services teams handling client data, communications, approvals, or financial processes. The initial implementation is an Azure development pilot using synthetic data: savings are configured-rate-card estimates, not invoices or guarantees, and production scope is defined only after a readiness assessment.
See cost and control on one governed path.
Book a demo and we will walk the co-managed lifecycle end to end — the hosted managed catalog and idle-session switching, deterministic allow/deny/approval decisions, and the tenant-wide Cost & Usage view with its savings breakdown — using synthetic pilot data.
Book Your AI Security Control Plane Demo
Tell us about the AI workflow you want to govern first. We will tailor the demo to your estate — hosted chat, deterministic policy, exact-request approvals, and the Cost & Usage savings view — and reply within one business day. The current pilot runs on Azure.