Use cases describe the operational contexts in which AIOBES is applied. They define the environments where behavioural evaluation is required to ensure safe, stable and compliant AI behaviour (The operational settings where AI behaviour has direct consequences, affects decisions or workflows, and carries regulatory or safety implications).
AIOBES applies to any operational context where AI systems perform tasks, support workflows or interact with people. Its scope is not limited to specific industries or sectors (Any environment where AI outputs influence actions, obligations or outcomes).
AIOBES recognises several operational context categories that describe the environments in which behavioural evaluation is required:
(Structural categories covering environments where rules, dependencies, scale or public interaction amplify the consequences of AI behaviour)
Behavioural instability, drift, contamination and misalignment create operational, regulatory and legal exposure across all operational contexts. AIOBES provides a structured way to detect and document these behaviours (Contexts where incorrect or unstable AI behaviour propagates into decisions, compliance obligations or workflow outcomes).
Use case types illustrate how behavioural evaluation applies across operational contexts without relying on fictional workflows or hypothetical scenarios:
(Real operational situations where AI behaviour interacts with rules, dependencies, safety boundaries or cross-system effects)
AIOBES provides behavioural evaluation methodologies, evidence requirements and operational concepts that support safe and compliant AI deployment across all operational contexts (A structured way for organisations to demonstrate that AI behaviour remains stable, aligned and non-contaminating during real operational use).
AIOBES will expand its use case guidance into a dedicated annex as operational requirements evolve (Additional context categories and examples will be added as the operational section develops).