CoreQ Academy4.1 Explain organizational governance structures that support AI.
AI governance answers who decides how AI is used, who builds it, who secures it and who checks it. Clear roles stop AI projects from bypassing security and legal review.
Organizational structures
- AI Center of Excellence
- A central team that sets standards, shares expertise, reviews AI projects and helps teams adopt AI safely.
- AI policies and procedures
- Written rules on acceptable AI use, approved tools, data handling, review and approval steps.
AI-related roles
- Data scientist
- Explores data and builds models to answer business questions.
- AI architect
- Designs how AI components fit into systems: models, data flows, integration and scale.
- Machine learning engineer
- Turns models into production-quality, maintainable software.
- Platform engineer
- Builds and runs the infrastructure (compute, GPUs, storage, networking) AI runs on.
- MLOps engineer
- Automates the model life cycle: training pipelines, versioning, deployment, monitoring and rollback.
- AI security architect
- Designs security controls for AI systems: threat models, guardrails, access, data protection.
- AI governance engineer
- Implements governance in tooling: model registries, policy checks, documentation and approval workflows.
- AI risk analyst
- Identifies, assesses and tracks AI risks and recommends treatments.
- AI auditor
- Independently checks that AI systems meet policy, legal and ethical requirements.
- Data engineer
- Builds pipelines that collect, clean, move and store data for AI.
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Original CoreQ Academy material, mapped to objective 4.1 of the publicly available CompTIA SecAI+ CY0-001 exam objectives (Exam objectives V1, document version 1.1). Not affiliated with or endorsed by CompTIA. Contains no exam questions.