Trusted AI Safety Expert (TAISE) Certification

The Trusted AI Safety Expert (TAISE) certificate, created by the Cloud Security Alliance (CSA) and Northeastern University, is a rigorous, research-backed program for professionals who build, manage, or audit intelligent systems. Through 10 comprehensive modules and a final certificate exam, learners gain practical skills to evaluate and mitigate real-world AI risks, apply AI safety and security controls, navigate compliance frameworks, and lead responsible AI adoption across industries. TAISE is more than a certificate—it’s a commitment to advancing safe, secure, and responsible AI.

Trusted AI Safety Expert (TAISE) Certification Benefits

• Important Course Information:

  • Empowers you to lead and drive organizational change
  • Provides a clear roadmap for building and managing an AI governance program
  • Provides a comprehensive framework for managing AI-specific risks and threats throughout the entire AI model lifecycle

  Prerequisites:

  No prerequisites are required before you take the TAISE training and exam. A basic familiarity with AI, cloud, and cybersecurity is recommended. For foundational knowledge that will aid in your understanding of the TAISE material, consider exploring CSA’s Certificate of Cloud Security Knowledge (CCSK) first.

Trusted AI Safety Expert Certification Training Outline

Learning Objectives

Module 1: Introduction to AI

Provides foundational knowledge of AI concepts, modalities, and their historical evolution.

Module 2: Generative AI Architecture & Design

Explores the technical components, training methods, and deployment considerations of generative AI.

Module 3: AI Use Cases: GenAI, Multimodal & AI Agents

Examines real-world applications of generative AI across industries while addressing ethical implications such as deepfakes, misinformation, and bias.

Module 4: Ethics, Transparency, & Explainability in AI

Introduces key ethical principles and practical explainability methods to promote fairness, accountability, and transparency in AI systems.

Module 6: Governance, Risk Management, & Compliance

Focuses on governance structures, regulatory frameworks, and compliance models for managing AI systems responsibly at the organizational level.

Module 7: Introduction to AI Safety & Security

Differentiates between AI safety and AI security, highlighting the unique challenges of securing GenAI.

Module 8: Cloud & AI Security

Details cloud security fundamentals for AI, including deployment strategies, monitoring, Zero Trust, and incident response planning

Module 9: Data Security & Privacy in AI Systems

Explains techniques for ensuring data quality, privacy, and governance in AI systems, with a focus on authenticity, minimization, and secure handling.

Module 10: Continuous Learning & Adaptation

Emphasizes ongoing monitoring, feedback loops, and MLSecOps practices to keep AI systems accurate, resilient, and safe over time.