As generative AI becomes rapidly embedded within enterprises, organizations have reached a critical tipping point. Teams are balancing their objective to support innovation and drive efficiency against the escalating risks of unsanctioned "Shadow AI" use and potential sensitive data exfiltration. While the benefits of AI automation and enhanced workflows are clear, the path to implementation can be complicated by concerns over sensitive data exposure and intellectual property loss. This session explores how organizations can move beyond restricting AI use to create a secure environment where AI adoption is both strategic and safe across the data ecosystem.

What We Will Cover:

• Understanding Emerging Threats: From prompt-injection attacks and unauthorized data exfiltration to model-training risks, we’ll examine potential AI data risks across the enterprise.
   
  
• Framework for Secure Adoption: A look at how to integrate AI into your environment without compromising your security posture or compliance standards through the pillars of policy, training, and technology
  
  
• Mitigating AI Data Risk: Understanding the mechanics of modern AI threats to sensitive data and strategies to address them in real time.

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