How We Govern AI
Practical safety for professional-services firms. Not compliance theatre, not a 50-page policy document. Governance built into every workflow.
Human oversight at every step
Every AI workflow includes human review. AI drafts, suggests, and prepares. Your team reviews and approves before anything goes out. No autonomous actions on client data without explicit sign-off.
One workflow at a time
Every deployment follows the same process: discovery, design, testing, sign-off, and monitored go-live. No broad rollouts. No big-bang deployments. One workflow, proved and governed, before moving to the next.
Scoped permissions and data access
AI only accesses what it needs. Microsoft 365 permissions are scoped deliberately for each workflow. No broad data access, no accidental exposure of sensitive documents. Every data source is explicitly approved by your team.
Approval gates for critical actions
When AI takes an action that affects clients, communications, or sensitive data, it pauses and waits for human approval. Your team decides what requires sign-off. Clear escalation paths for edge cases.
Governance built in, not bolted on
Governance is not a separate document you file and forget. It is built into the workflow itself: scoped permissions, logged actions, approval gates, and regular reviews. Practical and operational, not bureaucratic.
Governance by deployment model
Microsoft-native
Data sits
Within your Microsoft 365 tenant
Permissions
Microsoft 365 RBAC and SharePoint permissions
Logs
Microsoft 365 audit logs
Human review
Approvals via Teams, Outlook, or Power Automate
Hybrid (external AI connectors)
Data sits
In your tenant, with governed API connections to Claude or other models
Permissions
Scoped API access, no broad data sharing
Logs
Combined Microsoft 365 and connector audit trails
Human review
Same approval gates, regardless of which model generated the output
Private deployment
Data sits
On infrastructure you control (on-premises or private cloud)
Permissions
Your own access controls and network policies
Logs
Stored within your environment
Human review
Same governance framework, different infrastructure layer
Common questions
Yes. With governed workflows, human oversight, and scoped permissions, AI handles repetitive work while your team stays in control of every decision that matters.
All AI access is scoped to specific data sources you approve. Permissions are reviewed before deployment. Audit logs track every action the AI takes.
Not necessarily. Copilot is one option. Claude, Power Automate, and hybrid approaches may fit better depending on your workflows. The audit determines the right tools.
Yes. Claude can be connected to your Microsoft 365 environment for tasks like knowledge retrieval, document analysis, and draft preparation. Permissions and access controls apply.
Your team uses the workflow as normal. When AI prepares a draft, suggestion, or action, it appears for review. Approved items proceed. Everything is logged. Monthly reviews keep the system current.
Yes. Private deployment options run AI models on infrastructure you control. For on-premises deployments, no data leaves your network. For private Azure, data stays within a dedicated tenant you control, separate from shared cloud services. Both are available for firms with strict data sovereignty or regulatory requirements.
The workflow audit assesses your data sensitivity, regulatory obligations, and infrastructure. Each workflow gets the deployment model that fits its risk profile. You do not need to choose one model for everything.
Want to see how governance works for your firm?
Book a workflow review to discuss your specific requirements.