What it means
AI risk scenario analysis describes a plausible chain from threat, vulnerability, error, misuse, or change through affected assets, stakeholders, and consequences.
Why it matters
Specific scenarios support better control selection than generic labels such as model risk or AI failure.
Practical focus
- Define the system boundary, accountable owner, approved purpose, material risks, and decision authority for AI Risk Scenario Analysis
- Document initiating conditions, actors, system boundaries, dependencies, failure path, affected decisions, impact dimensions, likelihood assumptions, and existing controls.
- Collect and protect this evidence: scenario workshops, threat models, assumptions, supporting data, scoring, control mappings, and updates after incidents or major changes.
- Exercise this failure scenario as a planned test: a risk assessment lists prompt injection without showing which inputs, tools, privileges, data, or business actions create material loss.
Common mistakes
- Using the label AI Risk Scenario Analysis without defining the exact systems, data, people, decisions, and time period in scope
- Treating a policy statement or tool demonstration as proof that AI Risk Scenario Analysis operates consistently in production
- Failing to retain scenario workshops, threat models, assumptions, supporting data, scoring, control mappings, and updates after incidents or major changes in a form that lets an independent reviewer reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when a risk assessment lists prompt injection without showing which inputs, tools, privileges, data, or business actions create material loss.
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: