Explore the Domains
Domain 1 (12%)
Domain 2 (23%)
Domain 3 (18%)
Domain 4 (25%)
Domain 5 (22%)
Optional Dropdown Label:
Domain 1: The Value of Information in the Age of AI
Weight: 12% of the exam
Topics
- Why information management matters
- Artificial intelligence
- AI governance and risk management
You should be able to:
- Tell records from non-records
- Connect information management to business outcomes
- Match AI use cases to information work
- Explain what large language models can and can't reliably do
- Compare types of AI solutions
- Describe intelligent information management
- Articulate the business value of AI
- Identify AI and analytics risks, such as poor training data, bias, black-box models and hallucination
- Recognize reputational risk
Domain 2: Capturing and Preparing Information for People and Machines
Weight: 23% of the exam
Topics
- Document capture and intelligent processing
- Preparing information for AI
- Organizing information: metadata and taxonomy
- Knowledge management, search and discovery
You should be able to:
- Apply records, document, content and data lifecycles
- Identify capture points and design capture processes
- Ensure completeness and accuracy at capture
- Plan digitization and long-term file formats
- Manage information from uncontrolled sources, such as personal devices and social platforms
- Extract information from structured applications and scanned images
- Attribute and label AI-generated content
- Protect authenticity, integrity and chain of custody
- Prepare content for AI, including ROT remediation
- Spot data exposure in prompts and outputs
- Support retrieval (RAG) and context management
- Develop a metadata strategy
- Use taxonomies, controlled vocabularies and ontologies
- Design information architecture
- Manage reference data and interoperability
- Set access rules by classification and sensitivity
- Choose classification, search and navigation approaches
Domain 3: Using and Sharing Information and AI Outputs
Weight: 18% of the exam
Topics
- Document and content management
- Collaboration and information sharing
- AI governance and risk management: AI outputs
- Managing prompts and improving AI practices
You should be able to:
- Apply check-in/check-out, version control and workflow
- Control changes to critical documents
- Govern sharing with partners and customers
- Manage email, chat and meeting content, including AI recaps, as records
- Keep humans in oversight of automated processes and AI agents
- Decide when AI output needs human review
- Build review and approval workflows for AI outputs
- Validate outputs and citations
- Maintain audit trails for AI decisions
- Manage prompt templates, prompt libraries and version control
- Use quality metrics to drive continuous improvement
Domain 4: Governing, Protecting and Retaining Information
Weight: 25% of the exam
Topics
- Information governance
- Privacy, security and compliance
- AI governance and risk management: ethics
- Records management, retention and eDiscovery
- Digital preservation and archiving
You should be able to:
- Define stewardship and governance roles
- Write governance policies and conduct information audits
- Inventory systems and repositories
- Protect personal and sensitive data
- Apply Privacy by Design and privacy assessments
- Handle consent and privacy impact assessments for AI data use
- Use redaction, encryption and rights management
- Address data sovereignty and breach response
- Manage information incidents
- Protect vital records and plan disaster recovery
- Address fairness, ethics and bias in AI data
- Build retention schedules and manage disposition
- Automate records management tasks
- Ensure secure disposition
- Manage legal holds and eDiscovery
- Decide whether AI prompts and outputs are records
- Plan a digital preservation strategy
- Govern use of archived content for AI training, including legal restrictions
Domain 5: Improving Information Management
Weight: 22% of the exam
Topics
- Information strategy and digital transformation
- Process and workflow automation
- Technology and vendor selection and management
- Change management and adoption
You should be able to:
- Set an information strategy with KPIs
- Run readiness assessments
- Diagnose root causes
- Build a program roadmap and business case
- Establish the system of record
- Govern continuous improvement
- Map processes and judge automation suitability
- Apply workflow, BPM, RPA and case management
- Design workflows with audit trails
- Select content management solutions
- Evaluate vendors, including where data is stored and processed
- Plan content migration
- Manage implementation costs and projects
- Create change plans and build AI literacy
- Support ongoing education
- Define implementation roles
- Measure success and run post-implementation reviews
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