Use Cases

Why This Page Exists

AI strategy fails when governance is added too late. These use cases show where the platform creates immediate value while keeping implementation practical.

CEO: Scale AI Without Blind Risk

Use case: governed expansion from pilot to production

• Start with 2-5 agents in a pilot scope.
• Move to multi-team deployment with policy and workflow controls.
• Reach enterprise scale with shared governance standards.

Business outcome: growth in AI automation without unmanaged compliance or reputational exposure.

CTO: Standardize a Reliable AI Control Plane

Use case: agent onboarding with controlled permissions

Reusable flow from the platform APIs and workflow model:

1. Register each AI agent as a first-class identity.
2. Issue credentials with expiration and rotation controls.
3. Assign roles and scoped access permissions.
4. Approve dataset access before runtime use.

Technical outcome: repeatable onboarding with fewer production surprises and tighter blast-radius control.

Use case: policy change with approval gates

1. Create a policy and new version.
2. Submit activation request.
3. Require security and compliance approvals.
4. Activate only after required approvals are complete.

Technical outcome: no unreviewed policy drift in production.

Chief Data Officer: Protect Data Trust in AI

Use case: identity-aware RAG for data access governance

1. Agent submits a RAG query.
2. Platform checks identity, environment, and data permissions.
3. Retrieval is filtered to authorized datasets and chunks only.
4. Lineage links outputs back to exact source data.

Data outcome: consistent, entity-grounded answers with lower risk of cross-domain leakage.

Use case: entity resolution for canonical truth

• Resolve naming variants into canonical entities.
• Route uncertain merges into human review.
• Preserve rationale and confidence for every identity decision.

Data outcome: stronger MDM quality for AI reasoning and reporting.

Compliance Teams: Operationalize Control Evidence

Use case: immutable decision records and explainability

• Record decision inputs, outputs, model version, and confidence.
• Preserve traceability with narrative explanations.
• Link decisions to policy and lineage context.

Governance outcome: evidence is generated during execution, not reconstructed later.

Use case: workflow-driven change management

• Enforce approvals for policy, prompt, model, and dataset changes.
• Track approver rationale and timestamps.
• Maintain full state transition history.

Governance outcome: clear separation of duties and auditable change control.

Auditors: Faster, Defensible Investigations

Use case: incident response with kill switch and evidence chain

1. Detect high-risk anomaly.
2. Activate kill switch for affected agent.
3. Revoke credentials and contain activity.
4. Review related audit and lineage records.
5. Document remediation and closure.

Audit outcome: complete event reconstruction with verified chronology.

Use case: periodic evidence export

• Generate scoped audit export jobs for review periods.
• Track export completion and file integrity metadata.
• Produce certification artifacts for formal audit workflows.

Audit outcome: lower effort to prepare structured evidence packs.

Cross-Functional Use Cases You Can Start Now

1. Govern customer support agents that access policy documents.
2. Control AI assistants that query internal knowledge bases.
3. Enforce approval for model or prompt updates in production.
4. Detect abnormal access behavior in sensitive domains.
5. Build executive dashboard views for risk and governance KPIs.

Implementation Pattern

1. Pilot with one high-value use case and a small agent set.
2. Expand to additional teams with RBAC and anomaly tuning.
3. Standardize governance workflows across production AI programs.