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AI Security and Compliance in Azure Flashcards
Microsoft Azure AI Fundamentals AI-900 Flashcards
| Front | Back |
| Define Confidential Computing in Azure. | Protects data in use by executing workloads in secure enclaves on Azure Confidential VMs |
| Describe data minimization for AI solutions. | Collect and process only the data necessary for the AI model reducing privacy risks |
| Explain pseudonymization vs anonymization. | Pseudonymization replaces identifiers with pseudonyms while anonymization irreversibly removes identifiers |
| How can you ensure data residency requirements for AI workloads on Azure? | Deploy resources in specific Azure regions that comply with local data residency laws |
| How can you secure AI model endpoints over the network in Azure? | Use Azure Private Link or deploy endpoints inside an Azure Virtual Network |
| How do Azure Blueprints help in AI compliance? | Provide repeatable templates of Azure resource deployments with built in compliance settings |
| How do managed identities enhance security for Azure AI services? | Provide Azure AD identities for services eliminating the need for credential management |
| How do you audit AI deployments on Azure? | Use Azure Monitor Azure Activity Logs and Azure Audit Logs for tracking changes and access |
| How is data encrypted in transit for Azure AI services? | Transport Layer Security TLS ensures encryption between clients and Azure services |
| Name a CI/CD security practice for MLOps in Azure. | Implement secure pipelines with GitHub Actions Azure DevOps and integrate security scanning of models and containers |
| Name a service for unified data governance and cataloging in Azure. | Azure Purview |
| What Azure feature helps classify and label sensitive data in AI solutions? | Azure Information Protection |
| What encryption options does Azure offer for data at rest in AI workloads? | Azure Storage encryption with Microsoft managed keys Azure Key Vault customer managed keys or Azure Disk Encryption |
| What is differential privacy in the context of Azure AI? | Technique that adds noise to data to protect individual privacy while enabling aggregate analysis |
| What is the purpose of Azure AD role-based access control (RBAC) in AI solutions? | Restricts access to Azure resources by assigning roles and permissions to users groups and applications |
| What is the role of Azure Sentinel in AI security? | Cloud native SIEM for collecting analyzing and responding to security incidents |
| What principles are covered by Azure Responsible AI? | Fairness reliability safety privacy inclusiveness transparency |
| Which Azure feature enforces compliance policies across AI resources? | Azure Policy |
| Which Azure service provides real time threat detection and advanced security for AI environments? | Microsoft Defender for Cloud (formerly Azure Security Center) |
| Which compliance certifications are commonly relevant for AI in Azure? | ISO27001 SOC GDPR HIPAA |
This deck highlights security, privacy, and compliance measures related to AI solutions deployed on Azure, including data protection and governance.