Getting Started with Clarity AI

Overview 

Clarity AI enables organizations to implement structured AI governance across the full lifecycle of AI systems - from defining policies and managing risks to supporting approvals and maintaining audit readiness. 

This guide introduces the core modules within Clarity AI and provides a recommended sequence for setting up governance processes. 

It is intended for teams establishing AI oversight frameworks as well as organizations formalizing compliance and risk management practices. 

1. Establish governance foundations 

Before registering AI systems or datasets, configure the governance structure and ensure organizational readiness. 

Key areas to review:

• Using the Clarity AI Dashboard 
• Using the AI Maturity Assessment 
• Settings Dashboard 
• Settings – Management Tab 
• Employee Management Dashboard 
• Team Management Dashboard 

These components help define governance roles, responsibilities, and visibility across the organization. 

2. Define policies and training structure 

Establish internal guidelines for responsible AI usage and ensure employees understand governance expectations. 

Relevant modules: 

• Policy Management Dashboard 
• Create New Policy 
• Policy Details View
• Policy Details – Approvals Tab
• Policy Details – Acknowledgements Tab 

Note: select the appropriate compliance framework within the Compliance section. 

Policies and training help ensure consistent and accountable AI adoption across teams. 

3. Register and manage AI systems 

Once policies and training are in place, begin registering AI systems and tracking governance information. 

Relevant modules: 

At this stage, AI systems typically complete risk assessments and attach supporting documentation before approval processes begin. 

Datasets may also be linked to AI systems during this phase. 

4. Manage datasets and data governance 

Track datasets used for training, testing, and operating AI systems. 

Relevant modules: 

Dataset governance supports transparency, compliance readiness, and traceability of data sources used in AI systems. 

5. Manage risk across the lifecycle 

Track risks identified through assessments, system evaluations, or manual entry. 

Relevant modules: 

Risk management workflows support structured evaluation and mitigation of AI-related risks. 

6. Track and respond to incidents 

Record and manage operational events that may affect AI system reliability, compliance, or performance. 

Relevant modules: 

Incident tracking supports accountability and continuous improvement. 

7. Monitor vulnerabilities 

Track vulnerabilities associated with AI systems and supporting components. 

Relevant modules: 

Vulnerability tracking supports risk reduction and ongoing governance oversight. 

8. Support audit readiness 

Maintain documentation and evidence required for internal reviews and regulatory obligations. 

Relevant modules: 

Audit readiness ensures governance activities remain transparent and verifiable.