Privacy matters when a generative AI application uses corporate or personal data. In an enterprise RAG system, indexed documents may contain confidential information. Retrieving useful context is therefore not enough: the deployment must also address data protection obligations and the risk of inappropriate disclosure. section 7.6 section 13.0
Using proprietary data through RAG gives an enterprise more control over the information supplied to a model than relying on public or generalized data that a larger context window might access. That control can help with privacy compliance, but it does not remove the need to assess security, governance, and regulatory requirements. section 8.8 section 7.6
Choosing a service for confidential data
Suppose an enterprise plans to use confidential documents in an LLM application. Besides checking what enters its retrieval corpus, it should compare service capabilities. Azure OpenAI is described as offering data privacy alongside customer-managed keys, encryption at rest, private networking, and regional availability for enterprises with security or regulatory needs. These are capabilities to evaluate, not a substitute for a deployment-specific privacy review. section 7.6 section 2.7.2
Treat privacy as an ongoing deployment concern alongside security, fairness, and reliability. Decide what data the application needs, assess the available controls, and check applicable protection requirements before using confidential information. section 7.6 section 13.0
