The text discusses the challenges posed by inference and model inversion attacks on AI systems, which exploit the model's functionality rather than traditional system vulnerabilities to extract confidential information. It emphasizes the inadequacy of conventional security measures like firewalls and data loss prevention tools in detecting these sophisticated attacks, as they focus on transport-layer anomalies rather than model-specific threats. The text outlines strategies for defending against such attacks, including implementing OWASP LLM Top 10 recommendations, using differential privacy during model training, and enhancing monitoring with semantic analysis and anomaly detection. Architectural safeguards such as model partitioning and federated learning are recommended to isolate sensitive data. The document also highlights the importance of compliance with regulations such as GDPR and HIPAA and stresses the need for AI-specific risk management frameworks to bridge the gap between technical safeguards and boardroom accountability. Finally, it introduces Galileo as a solution for real-time defense against these AI-specific security threats, offering customizable defenses and seamless integration with monitoring tools to enhance compliance and governance without compromising performance.