AI security and data residency

Security and data residency in the AI era

Before adopting generative AI, security and legal leaders ask the right questions: where data is processed, who has access, whether information trains external models and how it aligns with regulations. These questions are not obstacles; they are legitimate requirements. The good news is Microsoft's AI solutions were designed with clear answers to them.

The questions that matter

• Where is my data processed and stored?
• Does my data train third-party models?
• Who, inside and outside the company, can access the content?
• How does this meet applicable data protection laws?
• How do I audit usage and respond to incidents?

Copilot for Microsoft 365 and data protection

Copilot operates within your Microsoft 365 tenant's compliance boundary. Key points:

• Copilot prompts and responses are not used to train the base models.
• Processing happens within the service, respecting user permissions.
• Purview sensitivity labels and DLP policies are honored.
• Activities can be audited for compliance.

This means corporate content stays under the same security and privacy controls that already protect Microsoft 365.

Azure OpenAI and data residency

For custom solutions, Azure OpenAI offers even more granular controls:

Region choice is decisive for companies with residency demands: you define where data is processed, helping meet local requirements.

Privacy and data protection

In Brazil, the LGPD requires a legal basis, minimization and security when processing personal data. In international contexts, equivalent requirements apply. Recommended practices when adopting AI:

• Minimization: give the AI only the data it needs.
• Legal basis and transparency: document processing and inform data subjects when applicable.
• Labels and DLP: prevent sensitive personal data from entering improper flows.
• Proper retention: define how long prompts and outputs are kept.

Security layers for AI

1. Identity: MFA and conditional access in Entra ID.
2. Data: classification, labels and DLP in Purview.
3. Network: private endpoints for Azure solutions.
4. Content: safety filters and human review.
5. Monitoring: auditing and anomaly detection.

Common mistakes to avoid

• Pasting corporate data into uncontrolled public tools (shadow AI).
• Ignoring region choice when there is a residency requirement.
• Not configuring auditing before going to production.
• Assuming AI respects permissions without having fixed oversharing.

Security and residency checklist

• Region defined per residency requirement
• Confirmation that data does not train base models
• Identity with MFA and conditional access
• Labels and DLP applied to sensitive data
• Private networking for Azure OpenAI solutions
• Auditing and retention configured

How RHC helps

As a Microsoft Solutions Partner and CSP, RHC designs secure AI architectures, with region choice suited to data residency, network isolation, Entra ID and Purview integration and auditing and retention policies. We help legal and security answer the critical questions with confidence, so AI can advance without compromising compliance.

Key takeaways

• Copilot and Azure OpenAI do not use your data to train base models.
• Region choice in Azure meets residency requirements.
• AI security is multi-layered: identity, data, network, content and monitoring.
• Fixing oversharing and configuring auditing are indispensable steps.