A framework for AI your enterprise can actually trust
Most teams are experimenting with AI. Few have decided how it runs in production — who governs it, where it's isolated, and how it's audited. The Governed AI Operating Model turns “we're trying AI” into a controlled, repeatable system. Drsti Studio is its reference implementation.
Four pillars · one reference architecture · an operating loop you can adopt
The trust gap
Shadow AI is what happens without a model
Your teams are already shipping AI agents into Slack. Sales is running AI on customer data. Finance deployed a bot nobody approved. Without an operating model, every team invents its own — and you have no visibility, no controls, and no answer when a regulator asks who owns it.
47
rogue API calls
Average uncontrolled external calls per AI agent.
$500K
shadow spend
8 AI tools billing in parallel. Nobody budgeted for this.
0
audit trail entries
AI made a $50K decision. No record of why.
3x
compliance risk
Regulators ask: "Who controls this AI?" You don't have an answer.
The model
Four pillars of governed AI
The Governed AI Operating Model rests on four controls that every production AI system should be able to prove. Adopt them as principles — or run them as software in Drsti Studio.
Govern every action
Nothing executes by default. Whitelist exactly which APIs and tools an agent can call, gate high-stakes actions behind human approval, and cap spend per workflow.
- Tool & API allow-lists
- Human approval gates
- Spend & budget controls
Isolate every tenant
Each team or customer gets a dedicated subdomain with its own data, keys, and runtime — encrypted at rest and in transit, with role-based access. No shared state, no cross-tenant leaks.
- Dedicated runtime per tenant
- Encrypted data & keys
- Role-based access control
Audit every decision
Every action an agent takes is logged immutably — what it did, which policy allowed it, and who approved it. Export the full trail for regulators without a fire drill.
- Immutable decision logs
- Approver & policy on record
- Regulator-ready export
Attribute every output
No shadow routing. Models are named and versioned, Assists are immutable and rollback-able, and every result traces back to the exact logic and inputs that produced it.
- Named LLM providers
- Immutable Assist versioning
- Explainable, traceable outcomes
Reference architecture
The model, expressed as architecture
The four pillars map onto five layers. Every request flows top to bottom — governed, isolated, and audited at each boundary before it ever reaches your data.
Channel Layer
Slack, Teams, Web, API
Slack, Teams, Web, API
Control Plane
Auth, Policy, Routing
Auth, Policy, Routing
Runtime
Assist Execution Engine
Assist Execution Engine
Tool Perimeter
MCP-gated tool access
MCP-gated tool access
Data Plane
Encrypted, isolated storage
Encrypted, isolated storage
The operating loop
How you run the model
Establish boundaries
Finance team gets finance.studio.drsti.ai
Stand up an isolated workspace with dedicated data, credentials, and runtime. The tenant boundary is the unit of trust — everything else builds on it.
Set policy gates
"Approver required for >$10K transactions"
Whitelist which tools each Assist can reach, define approval gates and spend caps, and scope team access. Policy is declared, versioned, and changeable in seconds.
Deploy & audit
Launch "Expense Assist" to #finance, fully logged
Ship the Assist to Slack, Teams, or your app. Every action it takes is governed at runtime and written to an immutable trail you can review or export.
Reference implementation
The model, implemented: Drsti Studio
You don't have to build the platform to run the model. Drsti Studio ships the four pillars as software — every API call approved, every decision audited, every tenant isolated.
Multi-Tenant Isolation
Each tenant gets a dedicated subdomain with fully isolated data, keys, and runtime.
Policy-Gated Tool Execution
Define which tools each Assist can access. MCP perimeters enforce boundaries at runtime.
Immutable Assist Versioning
Every Assist version is tracked and immutable. Roll back or audit any change with confidence.
Human Approval Gates
Insert human checkpoints into any workflow. No action proceeds without explicit approval.
Cross-Agent Profile with Consent
Agents share context only with explicit user consent. Full control over what data flows where.
Slack / Teams Ready
Deploy Assists directly into Slack or Microsoft Teams. Meet your team where they already work.
Run the model on Drsti Studio
Your workspace lives at yourcompany.studio.drsti.ai
See it in action
From Trust to Execution to Scale
Drsti for Business
See how Drsti helps businesses build trusted AI that delivers real results.
Execution Grade AI
Multi-step actions, not chatbot responses.
Beyond the Chatbot
Turn your expertise into an AI Assist.
Compliance mapping
The controls auditors ask for
The model isn't a slide — it's the controls. Each pillar maps to evidence a security or compliance team can actually verify.