Certified AI DevSecOps Engineer (CAIDSE) Certification Course by Tonex
This certification bridges the gap between AI, DevOps, and security, focusing on embedding security practices within the AI development and deployment pipeline. It ensures that AI models are not only efficiently deployed but also secure throughout the lifecycle, from development to continuous monitoring.
Target Audience: DevOps engineers, security professionals, AI/ML engineers, IT managers.
Learning Objectives:
- Understand the fundamentals of AI, DevOps, and security integration.
- Implement secure coding practices for AI model development.
- Apply AI governance and compliance standards.
- Develop a secure AI deployment pipeline using DevSecOps principles.
- Assess and mitigate security vulnerabilities in AI systems.
- Implement continuous security monitoring and incident response for AI models.
- Secure data handling and privacy within AI workflows.
- Integrate security tools and practices into CI/CD for AI.
- Perform risk management and threat modeling in AI development.
- Ensure compliance with legal and regulatory requirements for AI systems.
Program Modules:
Module 1: Integrating Security Practices into the AI/ML Lifecycle
- Identifying security vulnerabilities in the AI/ML development process.
- Implementing secure coding techniques for AI models.
- Protecting AI/ML data pipelines from tampering and misuse.
- Ensuring secure collaboration between data scientists and DevSecOps teams.
- Embedding security validation in AI/ML model training.
- Secure management of AI model repositories and version control.
Module 2: Secure CI/CD Pipelines for AI Systems
- Designing a CI/CD pipeline with integrated security checks for AI deployments.
- Using containerization and orchestration tools securely for AI environments.
- Automating security scans during model builds and deployments.
- Managing secrets and credentials in AI deployment pipelines.
- Incorporating encryption and secure transmission methods in CI/CD for AI.
- Implementing role-based access control for CI/CD pipeline security.
Module 3: Automating Security Tests for AI Models and Datasets
- Automating vulnerability assessments on AI datasets and models.
- Validating data integrity and identifying data poisoning threats.
- Testing AI models against adversarial attacks using automated tools.
- Automating model robustness and bias testing.
- Security testing for model output predictions and decision-making processes.
- Integration of AI-specific security testing frameworks into DevSecOps tools.
Module 4: Continuous Monitoring for AI Security Risks
- Implementing AI model monitoring for adversarial attack detection.
- Real-time monitoring of data flow for signs of data poisoning.
- Setting up anomaly detection for AI model behavior and performance.
- Integrating AI model monitoring tools with SIEM systems.
- Incident response planning for AI-specific threats.
- Regular security audits and updates for AI/ML infrastructure.
Module 5: Compliance with Security and Privacy Regulations During AI Deployment
- Understanding key security and privacy regulations for AI (GDPR, HIPAA, etc.).
- Ensuring data privacy compliance for AI models processing sensitive data.
- Documenting AI model governance and compliance practices.
- Building auditable security measures into AI/ML pipelines.
- Managing regulatory reporting requirements for AI model deployments.
- Aligning AI system deployments with global data privacy standards.
Module 6: Risk Management and Threat Modeling in AI Development
- Identifying potential security threats unique to AI systems.
- Developing risk management strategies tailored to AI/ML workflows.
- Implementing threat modeling tools to identify vulnerabilities in AI systems.
- Evaluating risk in AI model decision-making and automation.
- Securing data sources against tampering and privacy violations.
- Conducting regular threat assessments for AI infrastructure.
Rationale: With the rising integration of DevOps practices in AI systems, ensuring that security is embedded at every stage of AI development is critical. This certification will help organizations deploy secure AI models while maintaining operational efficiency.
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 DevSecOps Engineering. 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 DevSecOps Engineering.
Exam domains:
- AI/ML Lifecycle Security Integration – 20%
- Secure CI/CD Pipelines for AI – 15%
- Automating Security Testing for AI – 15%
- Continuous AI Security Monitoring – 20%
- Compliance and Privacy in AI Deployments – 10%
- Risk Management and Threat Modeling for AI – 20%
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 DevSecOps Engineer (CAIDSE) Certification exam, candidates must achieve a score of 70% or higher.