AI Governance
Policy & controls
Prompt-and-pray isn’t anAI strategy.
Get reliable quality every time—consistent outputs across people and teams, with lower compliance and reputational risk.
Governance gaps show up when AI adoption accelerates:
Sound familiar?
AI adoption ahead of policy
“Legal wants an approved-use-case list we don’t have.”
Role-based scenarios need owners, data boundaries, and success metrics—not a blanket “employees may use Copilot” memo.
“Sensitive data is indexed before labels exist.”
Classification and DLP must be applied before Copilot and agents reach libraries leadership would not want in prompts.
“We can’t prove who used what model when.”
Audit trails for integrations, agents, and high-risk workflows are required for regulated buyers and internal risk committees.
“Automated outputs act without review.”
Human-in-the-loop rules define where AI suggestions become business action—contracts, client comms, and financial decisions.
Policy and control areas
What we implement
Approved use cases
Role-based scenarios with owners, data boundaries, prohibited uses, and success metrics leadership can review quarterly.
Data classification
Sensitivity labels, DLP, and retention applied before Copilot indexing and agent connectors go live.
Logging and review
Audit trails for Copilot, Azure OpenAI, custom agents, and integration accounts—exportable for compliance.
Human-in-the-loop
Workflow rules where automated outputs require review before client-facing or financial action.
Copilot & Purview controls
Tenant settings, enterprise search boundaries, and Purview policies aligned to your risk appetite.
Agent & API governance
Registration, secrets management, and data scopes for Copilot Studio, Bedrock, and custom runtimes.
Proof point
Case study: Governance prerequisites in a Copilot sprint
During a 90-day Copilot activation for a regulated mid-market client, OWCER completed sensitivity labels on priority libraries, DLP for external sharing, and an approved-use-case register—alongside adoption metrics leadership could discuss.
“OWCER mapped our governance prerequisites alongside activation—so we could scale Copilot without waiting for the next audit finding.”
Copilot adoption case study · AI governance checklist (blog)
How we engage
AI governance in four steps
Free resource
AI governance before you scale
Our blog post covers data boundaries, approved use cases, logging, and human-in-the-loop rules—the same domains we implement in client engagements.
Don’t scale blind
Policy that enables adoption.
Start with an AI Activation Assessment that includes governance and identity gaps—or talk to us about a standalone AI policy engagement.
