What it means
AI shared-responsibility mapping assigns control duties across model providers, cloud platforms, application teams, data owners, users, and oversight functions.
Why it matters
AI services combine multiple layers, and unassigned controls tend to become gaps between contracts, architecture, and operations.
Practical focus
- Define the system boundary, accountable owner, approved purpose, material risks, and decision authority for AI Shared-Responsibility Mapping
- Map each control objective to the party that implements, configures, monitors, supplies evidence, responds to failure, and approves exceptions.
- Collect and protect this evidence: responsibility matrices, architecture diagrams, service terms, control inheritance decisions, operational contacts, and review history.
- Exercise this failure scenario as a planned test: both the customer and provider assume the other party monitors prompt abuse or protects stored embeddings.
Common mistakes
- Using the label AI Shared-Responsibility Mapping without defining the exact systems, data, people, decisions, and time period in scope
- Treating a policy statement or tool demonstration as proof that AI Shared-Responsibility Mapping operates consistently in production
- Failing to retain responsibility matrices, architecture diagrams, service terms, control inheritance decisions, operational contacts, and review history in a form that lets an independent reviewer reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when both the customer and provider assume the other party monitors prompt abuse or protects stored embeddings.
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: