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⇱ Responsible AI with AWS Security and Governance | Coursera

Responsible AI with AWS Security and Governance

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Responsible AI with AWS Security and Governance

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Intermediate level

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6 hours to complete
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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand key principles of Responsible AI and ethical risk management

  • Use AWS tools to build secure, explainable, and bias-aware AI models

  • Apply governance, compliance, and security best practices in AI development

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Assessments

9 assignments

Taught in English

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This course is part of the AWS AI Practitioner Certification Prep Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 2 modules in this course

Master Responsible AI with AWS: A Security and Governance Learning Experience.

In today's AI-driven world, creating intelligent systems is not enough; they must also be secure, ethical, and accountable. This course teaches you how to master responsible AI techniques with AWS technologies geared for governance, fairness, and compliance. Through real-life situations and hands-on activities, you'll learn about the fundamental concepts of responsible AI, the legal risks associated with generative AI, and how to apply model guardrails to ensure explainability and fairness. But this course doesn't just teach responsible AI in theory—it shows you how to secure it in practice. You'll work with key AWS services like Amazon Guardrails, Macie, and A2I, and implement IAM policies, data encryption, and compliance frameworks to protect your AI models at every stage. Designed for learners preparing for the AWS Certified AI Practitioner exam, this course is ideal for those with prior knowledge of AI/ML and AWS Cloud Fundamentals. By the end, you'll be equipped to design AI solutions that are not only smart but also safe, ethical, and trusted.

This module covers the principles and governance of Responsible AI, highlighting best practices and real-world implementation using AWS tools to ensure fairness, transparency, and accountability in AI systems.

What's included

16 videos4 readings4 assignments

16 videos•Total 61 minutes
  • Course Introduction•4 minutes
  • Learning Objectives•1 minute
  • Introduction to Responsible AI•5 minutes
  • Principles of Responsible AI•6 minutes
  • Guardrails for Responsible AI•6 minutes
  • Responsible Practices in Model Selection•5 minutes
  • Demo- Amazon Guardrail Walkthrough•10 minutes
  • Legal Risks of Generative AI•5 minutes
  • Summary•2 minutes
  • Learning Objectives•1 minute
  • Interpretability and Explainability for Responsible AI•3 minutes
  • AWS Tools for Interpretability and Explainability•3 minutes
  • Role of MLOps in Responsible AI•2 minutes
  • Responsible AI with Amazon A2I•2 minutes
  • Demo-Getting Started with Augmented AI•5 minutes
  • Summary•2 minutes
4 readings•Total 40 minutes
  • Generative AI Capabilities and Challenges•10 minutes
  • Generative AI Security Scoping Matrix•10 minutes
  • Ensuring Transparency in AI Models•10 minutes
  • Monitoring and Auditing AI Decisions•10 minutes
4 assignments•Total 60 minutes
  • Foundations of Responsible AI and Governance•15 minutes
  • Responsible AI in Action with AWS•15 minutes
  • Foundations of Responsible AI•15 minutes
  • Interpretability and Explainability for Responsible AI•15 minutes

This module focuses on securing AI systems through identity management, access control, and data protection. It also explores governance, compliance, and security best practices using AWS services.

What's included

24 videos4 readings5 assignments

24 videos•Total 81 minutes
  • Learning Objective•1 minute
  • AWS IAM•6 minutes
  • Working of IAM•3 minutes
  • IAM Identities•6 minutes
  • IAM Policies•3 minutes
  • IAM Policy Types & Access Analyzer•5 minutes
  • IAM Benefits•2 minutes
  • IAM Usage in AI Model•4 minutes
  • Understanding IAM Policies and Permissions for AI Model•5 minutes
  • Demo - Creating IAM Users•5 minutes
  • Demo - Enabling MFA and Creating Access Keys•5 minutes
  • Demo - Creating Policies with IAM•4 minutes
  • Demo - Create and Assign Roles with IAM•5 minutes
  • Data Encryption•2 minutes
  • Summary•2 minutes
  • Learning Objectives•1 minute
  • AWS PrivateLink•2 minutes
  • Amazon Macie•2 minutes
  • AWS Shared Responsibility Model•3 minutes
  • Data Lineage and Data Cataloging•4 minutes
  • Data Governance Strategies in AI Systems on AWS•3 minutes
  • Key Compliance Standards•5 minutes
  • Summary•2 minutes
  • Course Completion•2 minutes
4 readings•Total 40 minutes
  • IAM Best Practices in AI Projects•10 minutes
  • IAM Integration with AI/ML Pipelines•10 minutes
  • Amazon Inspector•10 minutes
  • Scenerios for Security Best Practices•10 minutes
5 assignments•Total 75 minutes
  • Identity, Access, and Data Protection in AI•15 minutes
  • Ensuring AI Governance, Compliance, and Data Security with AWS•15 minutes
  • AWS IAM, Identities and Policies•15 minutes
  • IAM Benefits, Understanding IAM Policies and Permissions for AI Model•15 minutes
  • Ensuring AI Governance, Compliance, and Data Security with AWS•15 minutes

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Frequently asked questions

The main goals of responsible AI are security, accountability, transparency, and justice. AWS facilitates this through Audit Manager for compliance, SageMaker Clarify for bias detection and many other tools.

Yes, a course completion certificate is allocated upon completing all graded assignments and quizzes present in the Responsible AI with AWS Security and Governance.

This AI Practitioner course is intended for professionals interested in ethical AI, such as project managers, cloud architects, data governance officers, and those involved in delivering or managing AI solutions on AWS.

Yes, this course will provide you with skills in ethical AI, governance, and AWS security, all of which are highly valued in professions such as AI practitioner, ML engineer, cloud security specialist, and compliance officer. Furthermore, it's targeted for people studying for the AWS Certified AI Practitioner exam, which can significantly increase their professional credentials.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,