AI & Machine Learning Security
Master AI & Machine Learning Security with comprehensive hands-on practice across 9 modules.
What you'll master in Level 5
Master AI & Machine Learning Security with comprehensive hands-on practice across 9 modules.
Training Objectives & Core Deliverables
Understand the unique attack surface and threat vectors of AI, Machine Learning, and Deep Learning models.
Dissect Generative AI architectures: GANs, VAEs, Transformer models (GPT, BERT), and their security risks.
Distinguish traditional software security vulnerabilities from AI-specific data and algorithmic threats.
Execute adversarial evasion attacks: Zeroth Order Optimization (ZOO), Decision-based Boundary attacks, and GeoDA.
Simulate model poisoning attacks: SVM poisoning, Backdoors, Clean-Label, Adversarial Embeddings, and Bullseye Polytope.
Day-by-Day Training Schedule & Live Labs
name
Practical live lab drills for name
AI, ML and DL
Practical live lab drills for AI, ML and DL
ML Algorithms:
Practical live lab drills for ML Algorithms:
Generative AI
Practical live lab drills for Generative AI
Introduction to AI and ML Security
Practical live lab drills for Introduction to AI and ML Security
Threat Landscape
Practical live lab drills for Threat Landscape
Day 4
Practical live lab drills for Day 4
Poison Attacks
Practical live lab drills for Poison Attacks
Data Security in AI and ML
Practical live lab drills for Data Security in AI and ML
Detailed Module-wise Curriculum
Overview of AI: Key concepts, history, and modern applications
Machine Learning (ML) Basics
Supervised
Unsupervised
Reinforcement Learning
Regression
Classification
Clustering
Neural Networks
Deep Learning
What is Generative AI?:
Overview of generative models and their capabilities (e.g., GANs, VAEs, Transformers)
Types of Generative Models:
Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)
Transformer-based Models (e.g., GPT, BERT)
Applications of Generative AI: Text, image, video, and audio generation, content creation, and data augmentation
Overview of AI and ML technologies
Importance of security in AI and ML systems
Distinction between traditional software security and AI/ML security
Common security threats and attacks targeting AI and ML systems
Adversarial attacks: poisoning, evasion, data integrity attacks
Privacy risks: data leakage, membership inference attacks
Zeroth Order Optimisation (ZOO)
Decision-based/Boundary Attack
Geometric Decision-based Attack (GeoDA)
Poisoning Attack on Support Vector Machines (SVM)
Backdoor Attack
Clean-Label Backdoor Attack
Adversarial Embedding Backdoor Attack
Hidden Trigger Backdoor Attack
Bullseye Polytope
Backdoor Attack on Deep Generative Models (DGM)
Overview of data security challenges
Importance of data protection for maintaining confidentiality, integrity and availability
Data Privacy Regulations and Compliance
Data Classification and Sensitivity
· Data Collection and Acquisition
· Data Storage and Encryption
· Data Transmission and Network Security
· Data Masking and Anonymization
· Data Governance and Compliance Monitoring
Revision and doucumentation
Certificate of Technical Competency — AI & Machine Learning Security
Complete all live hands-on labs to earn this verifiable competency badge. This credential unlocks progression to SQL & Oracle Database Training.
Enroll in AI & Machine Learning Security
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