AI Security and Enterprise Protection
Description
Comprehensive understanding of AI-specific security threats, enterprise protection strategies, and implementation of security frameworks for AI systems.
Learning Objectives
Learners will understand AI-specific security threats including adversarial attacks and model poisoning, implement Google's Secure AI Framework (SAIF), utilize security tools like Model Armor, and develop comprehensive security strategies for protecting AI systems in enterprise environments.
Topics (6)
Comprehensive overview of security threats unique to AI systems including adversarial examples, model extraction, membership inference attacks, and prompt injection vulnerabilities.
Understanding and implementation of Google's SAIF framework including its six core principles for securing AI systems from development to deployment.
Implementation of Google Cloud's security tools including Model Armor for prompt screening, Security Command Center integration, and automated threat detection for AI workloads.
Comprehensive security architecture design including IAM controls, VPC Service Controls, encryption strategies, and network security for enterprise AI deployments.
Understanding regulatory compliance requirements, risk assessment methodologies, and governance frameworks for maintaining security compliance in AI systems.
Development of monitoring frameworks, anomaly detection systems, and incident response procedures tailored to AI-specific security events and vulnerabilities.