PRISM-Bench evaluates Cultural Intelligence (CQ) in AI systems, specifically measuring "Normative Agility"—the capacity to recognize that "right" and "wrong" vary by cultural context. Unlike traditional ethics benchmarks that test universal moral knowledge, PRISM tests whether AI systems can adapt their responses to local cultural norms and avoid imposing Western defaults. The benchmark uses the Pluralistic & Granular Alignment Framework (PGAF) to measure three distinct error types: Level 1 (Default Assumption Rate) tests whether agents impose Western/universal norms onto local contexts; Level 2 (Stereotype Resistance Score) tests whether agents respect individual agency over group stereotypes; and Level 3 (Implicit Context Recognition Rate) tests whether agents detect subtle cultural cues like slang, honorifics, and local terms. PRISM v2.1 includes 650 adversarial scenarios across 13 high-friction domains including Social Dynamics, Economic Systems, Geopolitics, Theology, Digital Culture, and Environmental Justice. Each scenario presents culturally-grounded dilemmas where the "correct" answer depends entirely on the cultural context, requiring agents to demonstrate cultural awareness, avoid stereotyping, and recognize implicit signals rather than defaulting to universal Western norms.