Certified AI DevSecOps Engineer (CAIDSOE) Certification Program by Tonex
The Certified AI DevSecOps Engineer (CAIDSOE) Certification Program by Tonex is designed for professionals integrating security into AI development lifecycles. It equips AI/ML engineers and MLOps practitioners with the skills to secure model pipelines, manage secrets, and defend against supply chain threats. The course emphasizes secure coding, compliance automation, and containerized model protection using GitOps and Infrastructure-as-Code (IaC) principles. Participants will learn to verify model integrity, implement compliance-as-code, and handle security across the entire CI/CD flow tailored for AI. Ideal for those managing secure, scalable, and trusted AI systems in fast-paced cloud-native environments.
Audience:
- AI/ML Engineers
- MLOps Practitioners
- DevSecOps Professionals
- Cloud Security Engineers
- Site Reliability Engineers (SREs)
- Compliance Analysts
Learning Objectives:
- Understand AI-specific DevSecOps concepts and practices
- Secure model packaging, deployment, and versioning
- Detect and mitigate supply chain risks in AI pipelines
- Implement GitOps and manage secrets in AI workflows
- Apply compliance-as-code for secure AI deployments
Program Modules:
Module 1: Foundations of AI DevSecOps
- Introduction to DevSecOps for AI
- Role of MLOps and AI/ML pipelines
- Key security challenges in AI workflows
- Importance of secure development practices
- Overview of secure CI/CD for AI
- DevSecOps culture in AI engineering
Module 2: Secure Model Packaging and Deployment
- Model packaging standards and tools
- Secure containerization for AI models
- Version control and audit trails
- Protection of model binaries
- Threats during model release
- Ensuring reproducibility and traceability
Module 3: AI Supply Chain Security
- Understanding AI/ML supply chain risks
- Third-party model and dependency vetting
- Software Bill of Materials (SBOM) in AI
- Guarding against poisoned models
- Securing data and model provenance
- Tools for AI supply chain security
Module 4: Model Integrity and Verification
- Techniques for model signing and verification
- Tamper detection in AI pipelines
- Model checksum validation methods
- Verifying accuracy post-deployment
- Runtime model integrity enforcement
- Zero-trust principles for model handling
Module 5: GitOps for AI + Secrets Management
- GitOps workflows in ML pipelines
- Secure Git repositories and access control
- Managing secrets in AI deployment
- Integrating HashiCorp Vault/KMS tools
- Audit logging and change management
- Least privilege and role-based access
Module 6: Compliance-as-Code for AI Workflows
- Understanding compliance-as-code in AI
- Automating security controls in ML pipelines
- Integrating policy engines like OPA
- AI-related standards (e.g., NIST AI RMF)
- Reporting and governance automation
- Real-world compliance automation examples
Exam Domains:
- AI Security Governance and Risk Management
- CI/CD Security in AI/ML Pipelines
- Secure Model Lifecycle Management
- Supply Chain Threat Detection in AI
- Infrastructure and Secrets Security for AI
- Regulatory Compliance and Audit Automation
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 Certified AI DevSecOps Engineer. 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 Certified AI DevSecOps Engineer.
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 Certification Training exam, candidates must achieve a score of 70% or higher.
Advance your career by becoming a Certified AI DevSecOps Engineer. Enroll now and gain the expertise to secure AI systems from development to deployment.