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AI Security Lifecycle
This course provides an overview of every stage of the AI Security Lifecycle, from planning and data preparation to deployment, monitoring, and governance—equipping them with the frameworks, tools, and best practices necessary to safeguard AI models and data in enterprise environments.
Course Content
Building and Managing Secure, Compliant, and Ethical AI Systems
This course provides a comprehensive, end-to-end understanding of how to design, develop, deploy, and govern secure and trustworthy AI systems. It guides learners through every stage of the AI Security Lifecycle—from planning and data preparation to deployment, monitoring, and governance—equipping them with the frameworks, tools, and best practices necessary to safeguard AI models and data in enterprise environments.
Through real-world case studies, learners will explore threat modeling, adversarial testing, compliance frameworks (such as GDPR, HIPAA, NIST AI RMF, and ISO 42001), and governance principles that promote responsible and explainable AI.
Who is this course for
This course is designed for AI practitioners, data scientists, ML engineers, cybersecurity professionals, and compliance officers seeking to integrate security and ethical governance into the AI development process. It is also valuable for technical managers and policymakers who oversee enterprise AI initiatives and need to ensure regulatory and organizational compliance.
Learning Objectives
Upon completing this course, learners should be able to:
- Describe the end-to-end AI Security Lifecycle and its role in ensuring trustworthy and compliant AI operations.
- Identify key security threats and vulnerabilities unique to AI systems, such as data poisoning, model inversion, and prompt injection.
- Explain how to design secure architectures, implement access controls, and embed compliance requirements during AI planning and deployment.
- Apply threat modeling, adversarial testing, and bias assessment techniques to evaluate AI system resilience.
- Implement best practices for data integrity, encryption, and secure model management across cloud and edge environments.
- Analyze monitoring and observability data to detect anomalies, compliance drift, and adversarial behavior in production AI systems.
- Evaluate and document governance structures aligned with frameworks such as NIST AI RMF, ISO 42001, and the EU AI Act.
- Demonstrate the ability to integrate ethical oversight, policy enforcement, and continuous improvement into AI operations.
