Solving Compliance and Security Challenges As enterprises rapidly adopt AI to boost productivity and gain competitive advantage, they face a critical challenge: sensitive data now proliferates across AI infrastructure—from public GenAI tools to private models and SaaS copilots—creating new security and compliance risks. With 43% of enterprises experiencing sensitive data loss through GenAI applications in the past year and 60% lacking visibility into half their data estate, organizations need a holistic approach that secures data across the entire AI lifecycle without inhibiting innovation or creating gaps between point solutions. Key Points: AI fundamentally changes data exposure dynamics Microsoft Copilot presents unique oversharing challenges Securing AI requires a multifaceted approach across public and private AI Once sensitive data trains an LLM, it cannot be fully removed The AI compliance landscape is rapidly evolving and fragmented Zscaler provides comprehensive AI data security
