AI is transforming the way organizations operate, innovate, and compete. It's also introducing new security risks faster than many businesses can keep up with.
In this episode, we break down why AI security cannot be treated as a standalone initiative. Effective AI risk management starts with strong security fundamentals, including identity management, endpoint protection, network segmentation, visibility, and governance.
You'll learn:
• Why AI security is built on existing cybersecurity foundations
• The growing risks of Shadow AI and unsanctioned tool usage
• How prompt injection, model poisoning, excessive permissions, and data leakage create real business exposure
• Why visibility is the first step to managing AI risk
• Practical controls including AI firewalls, DLP protections, browser extensions, and role-based training
• How organizations can balance innovation with security without slowing the business down
Whether you're evaluating AI adoption, building governance programs, or trying to understand where your organization is most exposed, this discussion provides a practical framework for approaching AI security in the real world.
AI security is not a destination. It is an ongoing process of visibility, control, education, and continuous improvement.
