An enterprise AI application may connect employees to documents, production data, and model infrastructure. Authorization determines who is permitted to access those resources. It belongs alongside authentication, encryption, and audits in the application’s security design—not as a feature added after deployment. section 10.4.1 section 11.4.8
Apply access controls at each relevant boundary:
- Documents: Azure OpenAI on your data supports role-based and document-level controls. Together with Azure AI Search security filters, these can limit access according to permitted groups and LDAP memberships. section 8.7
- Data pipelines and production systems: Specify access rights in data governance rules, and restrict production data and infrastructure to authorized personnel. Role-based access control and least privilege help reduce breach risk. section 10.4.1 section 11.4.8
- Models and artifacts: Protect model repositories as well as application data. Unauthorized repository access or an insider leak can expose model weights and other intellectual property. section 13.2.7
Searching enterprise documents
Suppose an AI assistant searches company documents for an employee. Document-level controls and Azure AI Search security filters can restrict the accessible documents using that employee’s permitted groups and LDAP memberships. The aim is to prevent the assistant’s data connection from making otherwise restricted information available. section 8.7
Authorization is one layer, not a complete security program. Pair it with trustworthy authentication, encryption at rest and in transit, monitoring for unauthorized activity, and regular compliance assessments. Revisit access rights as data sources and integrations change; a retrieval pipeline also needs testing and ongoing maintenance before and after deployment. section 10.4.1 section 11.4.8 section 8.4.1
