Your AI database agent should not remember tenant filters
Mads Hansen proposes a secure architecture for AI database agents, emphasizing that models should not directly interact with raw database tables or concatenate SQL queries. Instead, agents should leverage approved views that encapsulate business logic, security policies, and data redaction rules. This approach ensures that sensitive information is masked, tenant boundaries are enforced, and queries are executed safely through a parameterized system rather than direct string concatenation, thereby mitigating risks of data leakage and incorrect query execution. AI
IMPACT Proposes a secure architecture for AI database agents, enhancing data safety and reliability in production environments.