3-Day Instructor-Led Training
Official Courseware created by CSA and Northeastern University
After-Course Instructor-Coaching Included
Exam Voucher
Trusted AI Safety Expert (TAISE) Certification
Course 1204
- Duration: 3 days
- Exam Voucher: Yes
- Language: English
- Level: Intermediate
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 Certification Delivery Methods
In-Person
Online
Upskill your whole team by bringing Private Team Training to your facility.
Trusted AI Safety Expert Certification Course Information
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
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.
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Trusted AI Safety Expert Certification FAQs
- AI practitioners and developers
- Security and risk professionals
- Compliance, governance, and policy professionals
- Technical leaders and architects
- Auditors and consultants
- Cross-functional roles (e.g., product managers, data governance, executives)
- Environment: Online, open-book, unproctored
- Questions: 60 multiple-choice questions (6 per module)
- Time Limit: 120 minutes
- Passing Score: 80%
- Attempts: 2 included with the training + exam bundle (valid for 2 years)
The results are immediate. You will know your score as soon as you finish the exam.
The TAISE exam is open book, online, and not proctored. It is made up of 60 multiple-choice questions that have a single correct answer. These questions are selected randomly from a question pool. The minimum passing score is 80%.
The topics covered on the exam include:
- AI fundamentals
- GenAI architecture and design
- AI use cases
- Ethics, transparency, and explainability in AI
- AI model lifecycle and threat taxonomy
- AI governance, risk management, and compliance
- AI safety and security
- Cloud and AI security
- Data security and privacy in AI systems
- Continuous learning and adaptation with AI