Oracle Unveils 'Deep Trust' AI Architecture to Embed Security and Business Rules in Database Layer
Oracle announced the 'Deep Trust' AI Architecture at AI World, aiming to reduce risks associated with AI agents by embedding trust mechanisms into four key layers of the enterprise technology stack: software platform, data, data access, and application development.
US tech giant Oracle officially announced its 'Deep Trust' AI Architecture on October 26, 2026, during its Oracle AI World conference. This blueprint aims to make trust an inherent property of the enterprise technology stack, rather than leaving it as a responsibility for each individual AI agent or application to implement.
As organizations increasingly deploy AI agents and AI-generated applications onto existing architectures, they face growing challenges in protecting sensitive information, enforcing business rules, and ensuring accurate answers. Oracle states that the Deep Trust AI Architecture addresses these issues by embedding trust where it cannot be bypassed or compromised by AI models, deep within four critical layers: software platform, data, data access, and application development.
The architecture integrates existing technologies such as Oracle AI Database, Oracle Autonomous AI Database, and Oracle APEX AI Application Generator. Specifically, Oracle Deep Data Security allows organizations to define end-user access rules directly in the database and enforce them on every SQL request. Additionally, Oracle SQL Assertions enable the enforcement of business rules (such as spending limits) at the database level, regardless of which application, user, or agent submits the request.
In terms of application development, Oracle APEX AI Application Generator enables AI coding agents to translate natural-language descriptions into APEXlang declarative definitions, which are then executed by Oracle APEX's centralized runtime engine. This means AI generates only the application definition, not the running code, thereby reducing the security risks associated with maintaining large volumes of AI-generated execution code.