Securing GenAI Systems: Cloud, SDLC & Regulatory Compliance

Explore foundations of genai security, focusing on data privacy and confidentiality, model integrity and robustness, and access control with rbac and mfa, plus a hands-on ci policy exercise.

Apply access control and authentication to generative AI systems, enforcing least privilege and role-based access, with multi-factor authentication and IAM tools from AWS and Google Cloud, monitor via audit logs.

Learn how cloud platforms secure generative AI with Google Safe and AWS generative AI Scoping Matrix, implement native security tools, and enforce identity, access control, monitoring, encryption, and key management.

Learn best practices for securing GenAI in cloud environments, focusing on data protection, encryption, key management, IAM, network isolation, monitoring, and governance across AWS, Google Cloud, and Azure.

Identify common pitfalls in GenAI security, including data leakage, misconfigurations, and insufficient access controls, then apply best practices like RBAC, encryption, and comprehensive monitoring to prevent breaches.

Explore common GenAI security pitfalls, from data exposure and prompt injections to API theft and deepfake disinformation, and learn multi-layer defenses including encryption, access controls, and threat modeling.

Integrate security into the GenAI development lifecycle with DevSecOps, secure training pipelines, model validation and testing for adversarial robustness and bias, and strong access control and compliance.

Assess and strengthen GenAI systems across the development lifecycle by applying adversarial testing, robustness evaluation, bias detection, explainability testing, boundary testing, model drift detection, security stress testing, and compliance.

Explore strategies to defend gen AI against evasion, poisoning, and extraction attacks, and implement real-time monitoring and anomaly detection. Develop incident response playbooks and a hands-on response plan.

Master centralized monitoring and logging across chatbots, APIs, and cloud infrastructure with real-time alerts, anomaly detection, and incident response playbooks for GenAI security.

Evaluate regulatory compliance and ethical considerations in generative AI systems, covering GDPR, CcpA, HIPAA, Coppa, and PCI DSS; implement auditing, transparency, and bias-free, fair outputs.

Balance security, performance, and usability in secure GenAI systems by using adaptive authentication, scalable cloud infrastructure, encryption, real-time monitoring, and an architecture template aligned with GDPR, CcpA, and HIPAA.

Secure generative AI systems through data protection, model integrity, API security, and compliance frameworks. Use monitoring, adversarial defenses, access controls, and a security plan to ensure ethical and compliant AI.