Trusted AI Safety Expert (TAISE) Certificate

Learning Objectives (LOs):
● Define key terms and foundational concepts in generative AI.
● Describe the various generative AI models (LLMs, GANs, Diffusion Models, VAEs).
● Trace the historical evolution and milestones in Generative AI.
● Differentiate between cloud service models (SaaS, PaaS, and IaaS) and their implications for generative AI
deployment.
Skills:
● Generative AI Fundamentals
● AI Evolution & Trends

• Overview of Artificial Intelligence
• Overview of Generative AI
• Definitions and Concepts of Generative AI
• Overview of Generative AI Modalities: LLMs, GANs, Diffusion Models, and VAEs
• History and Evolution of Generative AI

● Understand the fundamental architecture of Transformers and Attention Mechanisms.
● Understand the fundamental architecture of Probabilistic Denoising Diffusion Models.
● Analyze how Generative AI models learn through training data and fine-tuning.
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● Understand how RAG supplements LLMs with Information Retrieval systems.
● Explore data ingestion (where data comes from) and data storing.
● Conceptualize Knowledge Augmented Generation (KAG).

Skills:
● Deep Learning & AI Models
● AI Modeling & Training

Learning Objectives (LOs):
● Explore real-world applications of generative AI in domains such as healthcare, finance, and content creation.
● Define specialized applications including AI agents, chatbots, and multimodal systems.
● Reflect on the ethical implications related to deepfakes, misinformation, and content authenticity.
● Recognize the role of safety and security in generative AI applications.
● Identify and categorize the different types of bias seen in generative AI.
Skills:
● Conversational AI
● Generative AI Applications
● AI Ethics & Content Authenticity

Learning Objectives (LOs):
● Explore the foundational ethical principles in AI development, including fairness, accountability, and
non-maleficence.
● Utilize explainability techniques (e.g., SHAP, LIME, Integrated Gradients) to interpret model behavior.
● Identify and mitigate bias in AI models.
● Develop transparency measures through comprehensive model and data documentation.
● Communicate AI risks, limitations, and ethical considerations effectively to stakeholders
Skills:
● AI Ethics & Responsible AI Development
● Explainability
● Bias Mitigation
● Stakeholder Communication

Learning Objectives (LOs):
● Outline the phases of the AI model lifecycle–from preparation to monitoring.
● Classify potential threats to AI models (e.g., data poisoning, model manipulation, sensitive data disclosure).
● Implement risk assessment strategies and scenario planning using frameworks like NIST AI RMF, ISO Standards,
and MITRE ATLAS.
● Conduct periodic risk reviews to ensure ongoing safety management.
Skills:
● AI Lifecycle Management
● AI Security & Threat Analysis
● AI Risk Assessment

Learning Objectives (LOs):
● Describe the principles of AI and data governance in the context of AI safety and security.
● Interpret regulatory frameworks such as GDPR, CCPA, the EU AI Act, and USAISI.
● Design and implement RACI models for AI systems within an organization.
● Define the role and responsibilities of the AI Safety Officer.
● Apply risk management frameworks (e.g., NIST AI RMF, ISO 23894, CSA AICM, STAR) to ensure compliance.
● Develop comprehensive model documentation practices including Model Cards, Data Sheets, and Risk Cards.
Skills:
● AI Governance & Compliance
● AI Risk Management
● AI Documentation & Transparency

Learning Objectives (LOs):
● Define the distinctions between AI safety and AI security.
● Explore the role of AI in enhancing cybersecurity and overall risk management.
● Identify the common challenges in securing generative AI systems.
Skills:
● AI Safety & Security
● AI for Cybersecurity
● Risk Management

Learning Objectives (LOs):
● Explain the fundamentals of cloud security as it applies to AI systems.
● Implement secure deployment and management strategies on cloud platforms (SaaS, PaaS, IaaS).
● Establish continuous monitoring practices and secure MLOps pipelines.
● Develop incident response and disaster recovery plans for AI in cloud environments.
● Apply Zero Trust Architecture principles to protect AI workloads.
Skills:
● Cloud Security & AI
● MLOps Deployment

Learning Objectives (LOs):
● Implement data authenticity, anonymization, and minimization techniques.
● Evaluate data quality management and preprocessing strategies to ensure safe AI model performance.
● Manage data access and secure transmission protocols in AI systems.
● Establish effective data governance practices, including data lineage and metadata management.
● Leverage synthetic data generation techniques to support privacy and security in AI.
Skills:
● Data Privacy
● Data Security
● Data Quality Management

Learning Objectives (LOs):
● Design feedback loops and monitoring processes to detect data drift.
● Manage online learning and model updates to ensure sustained accuracy and reliability.
● Integrate continuous improvement practices into AI operations (MLSecOps).
● Incorporate user feedback and incident reports to refine AI models.
● Educate internal stakeholders on ongoing AI risks and safety practices.
Skills:
● AI Risk Management
● ML Monitoring & Observability