As generative AI tools become mainstream in enterprise environments, organizations face new security challenges around data privacy, prompt injection attacks, and model vulnerabilities. This session examines emerging security risks in deploying AI systems, from sensitive data leakage through model responses to supply chain concerns with third-party AI services. We'll explore practical strategies for secure AI integration, including proper access controls, prompt engineering best practices, and monitoring mechanisms for AI interactions. Using recent incidents as case studies, we'll discuss how to develop AI security policies that balance innovation with risk management. Whether you're currently using AI tools or planning to adopt them, you'll learn actionable steps to protect your organization's data and systems in this rapidly evolving landscape.

The core of our discussion focuses on practical defense strategies across three key areas: data protection, access management, and operational security. We'll explore essential topics including:

1. Developing robust AI usage policies that protect intellectual property
2. Implementing proper authentication and monitoring for AI system access
3. Detecting and preventing prompt injection attacks
4. Managing sensitive data exposure risks in AI interactions
5. Evaluating third-party AI service providers for security compliance
6. Training employees on secure AI usage practices

This session is designed for security professionals, technology leaders, and decision-makers who need to understand and address AI security risks while enabling their organizations to benefit from these transformative tools. No deep technical knowledge is required, though familiarity with basic security concepts and enterprise AI use cases will be helpful.