Dynamic Data Masking is supposed to be simple: define what sensitive fields to hide, decide who sees what, and move on. At scale, it rarely works out that clean. As datasets grow and user groups multiply, the role structure inflates fast. Every new combination of permissions spawns another role. The system becomes fragile, slow to manage, and easy to break. This is the Large-Scale Role Explosion problem.
The core issue is not the masking mechanism itself but the static, role-based logic behind it. Traditional implementations tie masking rules to roles, which forces administrators to create new roles for slight variations in access. Over time, that pattern produces thousands of near-duplicate entries, bloats policy files, and makes audits painful. The more you rely on role granularity to meet edge cases, the faster the explosion happens.
The cost is high. Maintenance burns hours each week. Risk grows because it becomes harder to confirm who can see what. Migration across environments becomes risky because a single outdated role mapping can leak data or break features. When Dynamic Data Masking accidentally un-hides sensitive data or hides too much, teams scramble to fix policies they barely understand anymore.