As GenAI adoption expands across organizations, controlling who can request or receive specific information from AI systems has become a critical requirement. For example, salary information may be appropriate for payroll personnel, but exposing it to other employees would create security, privacy, and compliance risks. When sensitive information or documents are directly ingested into an LLM, they can be unintentionally reused, inferred, or externally exposed. Training unstructured data without access control policies significantly amplifies these risks.
Effective AI security is not simply about blocking access. It requires ensuring that AI systems learn from and respond with only the information that a user is authorized to access. To achieve this, every data must maintain an embedded ACL (Access Control List), and AI systems must evaluate these permissions in real time for both input and output. When ACLs are preserved at the data level, organizations can enable AI DLP (Data Loss Prevention) and DHC (Data Hygiene Control) consistently and reliably.
Through this approach, organizations can:
To build a secure agent-based AI environment, organizations must define what information the AI is allowed to learn and whom it is allowed to respond to, guided by ACL-based controls. FDR, Wrapsody, and FED together provide the essential data-level ACL foundation for responsible, secure GenAI usage.
