Security, Compliance and Governance for AI Solutions

Strengthen AI solutions with responsible governance

AI systems need more than strong technical performance.

They also need clear controls around security, privacy, fairness, transparency, and accountability. On this course, you’ll explore the principles and AWS practices that help organisations use artificial intelligence responsibly and with greater confidence.

You’ll examine how security, compliance, and governance fit across the AI lifecycle, from the data used to train models through to deployment and ongoing monitoring.

Protect data and AI systems

Explore the security considerations involved in AI and machine learning solutions, including data privacy, access controls, and the protection of sensitive information.

You’ll consider how AWS tools and security practices can help reduce risk across AI workloads.

Navigate responsible and explainable AI

Examine fairness, bias, transparency, explainability, and human-centred design. You’ll consider why these principles matter when AI influences decisions and how responsible AI practices can support more trustworthy outcomes.

Apply compliance and governance principles

Explore how governance frameworks, policies, monitoring, and oversight help organisations manage AI responsibly.

You’ll consider how compliance requirements can influence the way models and AI-powered services are designed, deployed, and maintained.

By the end, you’ll have a stronger understanding of the controls and practices that support secure, compliant, and responsible AI solutions on AWS, preparing you to consolidate your learning through AWS AI Practitioner exam preparation.

This course is for learners with foundational AI and AWS knowledge who want to understand security, privacy, responsible AI, compliance, and governance. It is suitable for technical, data, risk, and business professionals working with AI solutions.

This course is for learners with foundational AI and AWS knowledge who want to understand security, privacy, responsible AI, compliance, and governance. It is suitable for technical, data, risk, and business professionals working with AI solutions.

• Explain core responsible AI concepts, including fairness, bias, transparency, and explainability.
• Identify key security considerations, data privacy risks, and access controls within AI workloads.
• Describe AWS tools and security practices used to protect sensitive data and AI systems.
• Summarise governance frameworks, policies, and compliance requirements across the AI lifecycle.
• Discuss the role of human-centered design, oversight, and continuous monitoring in maintaining trustworthy AI.