Certified AI Security Architect (CAISA) Certification Course by Tonex
This certification is designed for professionals who specialize in securing AI systems from a wide range of cyber threats, including adversarial attacks, data poisoning, and model theft. The focus is on end-to-end AI system security, covering everything from development to deployment.
Learning objectives:
- Understanding AI System Architectures and Security Principles
- Identifying and Mitigating Adversarial AI Attacks
- Protecting AI Models from Data Poisoning and Model Theft
- Implementing Secure AI Model Development Lifecycles
- Ensuring Privacy and Confidentiality in AI Systems
- Securing AI Model Deployment and Operations
- Assessing AI System Vulnerabilities and Risk Management
- Designing Resilient AI Architectures Against Cyber Threats
- Complying with AI Security Standards and Regulations
- Managing AI System Security in Multi-Cloud and Hybrid Environments
Target Audience: Cybersecurity professionals, AI/ML engineers, IT security managers.
Program Modules:
Module 1: AI-specific Security Vulnerabilities and Attack Vectors
- Overview of AI system vulnerabilities
- Types of adversarial attacks (e.g., evasion, poisoning, extraction)
- Model inversion and membership inference attacks
- Security challenges in neural networks and deep learning
- Case studies of real-world AI security breaches
- Risk assessment methodologies for AI systems
Module 2: Adversarial Defense Strategies for AI Models
- Defensive distillation and robustness techniques
- Adversarial training methods for AI
- Use of differential privacy in AI models
- Model hardening techniques for secure deployment
- Techniques to detect and prevent adversarial inputs
- Evaluating the effectiveness of defense mechanisms
Module 3: Securing AI Data Pipelines and Model Training Environments
- Securing data integrity during the model training process
- Protecting against data poisoning in training datasets
- Best practices for handling sensitive data in AI
- Ensuring the security of distributed AI training (e.g., federated learning)
- Secure storage and transfer of AI models and data
- Role of cryptography in AI data pipeline protection
Module 4: AI Governance and Compliance in Security
- Regulatory frameworks for AI security and privacy
- Ethical implications of AI system security
- Implementing security governance for AI projects
- Ensuring compliance with GDPR, HIPAA, and other regulations
- Security documentation and audit trails for AI systems
- Managing third-party risks in AI development and deployment
Module 5: Real-time Monitoring and Incident Response
- Setting up AI system monitoring for anomaly detection
- Tools and techniques for real-time threat detection in AI systems
- Incident response planning for AI security breaches
- Role of AI in automating incident response
- Root cause analysis for AI security incidents
- Reporting and mitigating AI-related security vulnerabilities
Rationale: As AI becomes increasingly integrated into critical systems and infrastructure, the security of AI models and data pipelines is a growing concern. This certification will help meet the demand for skilled professionals who can protect AI systems from sophisticated cyber threats.
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, hands-on workshops, and project-based learning, facilitated by experts in the field of AI Security Architect. Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in AI Security Architect.
Exam domains:
- AI System Security Fundamentals – 15%
- Adversarial Threats and Attack Mitigation – 20%
- Secure AI Model Development and Deployment – 20%
- Data Protection and Privacy in AI Systems – 15%
- AI Governance, Risk, and Compliance – 10%
- Real-time Monitoring and Incident Response – 10%
- AI Security Standards and Regulations – 10%
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria:
To pass the Certified AI Security Architect (CAISA) Certification exam, candidates must achieve a score of 70% or higher.