AI agents need governance in the data layer
as enterprises give ai agents more autonomy, governance must shift to the data layer to prevent unauthorized actions and ensure accountability.
as ai agents gain more autonomy, enterprises face challenges in ensuring they act within authorized boundaries. traditional guardrails, such as instructions and monitoring, have structural limits. governance must become executable and enforced at the data layer, where agents interact with data. this involves implementing controls like role- and attribute-based access, row- and column-level security, and policy as code. by treating agents as principals with their own identity and purpose, enterprises can ensure accountability and prevent unauthorized actions.