Regulatory Compliance in Data Use | Certified AI Security Professional

Federated & Distributed Learning Security | Certified AI Security Professional

Privacy Engineering & Differential Privacy | Certified AI Security Professional

Secure Data Lifecycle | Encryption, Access, and Retention in AI Systems

Data Classification for AI Pipelines

AI Risk Ownership & Accountability | Defining Roles & Escalations

Integrating AI Risk into Enterprise GRC | ISO 31000 & COSO Alignment

AI Risk Registers & Control Catalogs | Mapping Threats to Frameworks

Building an AI Security Program | Governance, Policy & Metrics

The AI Risk Landscape | Understanding Data, Model & Ethical Risks

Module 4 – AI Risk & Security

Module 4 Closing – Securing the Future of AI

Monitoring for Security Incidents (CAIF 4.7)

Operational Security Controls (CAIF 4.6)

Secrets, Keys & Credential Hygiene (CAIF 4.5)

Model Privacy Risks (CAIF 4.4)

Data Poisoning & Integrity Attacks (CAIF 4.3)

Adversarial Examples (CAIF 4.2)

AI Threats Overview (CAIF 4.1)

Introduction to Module 3: AI Lifecycle & Platforms

Module 3 Closing & Key Takeaways

Documentation: Model Cards & Factsheets (CAIF 3.7)

Monitoring & Observability (CAIF 3.6)

Cloud AI Platforms (CAIF 3.5)

APIs & Inference Gateways (CAIF 3.4)

Deployment Patterns (CAIF 3.3)

Model Registries & Artifact Management (CAIF 3.2)

AI Lifecycle Overview (CAIF 3.1)

Module 2 Summary & Key Takeaways

Data Governance and Lineage (CAIF 2.7)

Introduction to Differential Privacy and Synthetic Data (CAIF 2.6)

Privacy Basics: PII, Anonymization, and DPIAs (CAIF 2.5)

Detecting and Mitigating Bias Risk (CAIF 2.4)

Sources of Bias and Fairness in AI (CAIF 2.3)

Data Labeling and Quality (CAIF 2.2)

The Data Lifecycle: From Collection to Deletion (CAIF 2.1)

Welcome To Module 2 — Introduction to Data, Ethics, and Stewardship

Welcome to Module 1: AI Concepts & Capabilities | Certified AI Fundamentals (CAIF)

Training vs Inference Explained Simply (CAIF 1.2)

Real-World AI Applications & Limitations (CAIF 1.7)

AI Model Evaluation Metrics Explained (CAIF 1.6)

Module 1 Recap: Key Takeaways from AI Fundamentals (CAIF 1.8)

Supervised, Unsupervised & Reinforcement Learning Explained (CAIF 1.3)

How Large Language Models & Embeddings Work (CAIF 1.5)

Neural Networks Made Simple (CAIF 1.4)

What’s the Difference Between AI, Machine Learning & Generative AI? (CAIF 1.1)

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