Secure and Govern Azure AI Services
AI and the need to secure organizational data Data overexposure Data used by AI could be sensitive, including PII and PHI and could violate patient's privacy rights Loss of customer trust Data loss Data produced by AI may be vulnerable to unauthorized access or disclosure by malicious actors Diminished competitive edge Data compliance Data used and produced by AI may not comply with relevant regulations and AI ethics principles Heavy penalties Fortify data security with an integrated approach Discover and auto-classify data and prevent it from unauthorized use across apps, services, and devices Understand the user intent and context around sensitive data to identify the most critical risks Enable Adaptive Protection to assign appropriate DLP policies to high-risk users Enable Adaptive Protection with Microsoft Purview Optimize data protection automatically Context-aware detection Identify the most critical risks with ML-driven analysis of both content and user activities Dynamic controls Enforce effective controls on high-risk users while others maintain productivity Automated mitigation Minimize the impact of potential data security incidents and reduce admin overhead Sensitivity labels span your entire data estate They are a representation of your information taxonomy They describe the priority assigned to your categories of sensitive information.
