Certified Zero Trust AI Architect (CZTAI)

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Certified Zero Trust AI Architect (CZTAI) Certification Program by Tonex

Certified Zero Trust AI Architect (CZTAI) Certification Program by Tonex

The Certified Zero Trust AI Architect (CZTAI) certification is designed for professionals seeking to lead the design, implementation, and governance of secure AI systems using Zero Trust principles. With the growing adoption of AI in mission-critical environments, ensuring that every AI component (model, API, data source, data pipeline) is authenticated, authorized, and verified continuously is essential.

This course teaches participants how to build secure, trustworthy AI systems with no implicit trust, applying modern security principles to ML pipelines, AI APIs, training datasets, and inference endpoints. Through real-world examples, architectural frameworks, and hands-on labs, learners will gain the expertise needed to become AI security leaders in Zero Trust environments.

Target Audience:

  • AI Security Architects
  • Cybersecurity Engineers
  • MLOps and DevSecOps Professionals
  • Cloud Security Architects
  • AI/ML Engineers deploying models in production
  • CISOs, CTOs, and Risk Managers involved in AI governance

Learning Objectives

Upon successful completion of the CZTAI™ program, participants will be able to:

  • Understand Zero Trust principles and how they apply to AI systems
  • Architect secure AI/ML pipelines across the data, model, and deployment lifecycle
  • Apply continuous authentication and policy enforcement for all AI assets
  • Implement secure model delivery and endpoint protection
  • Detect, respond to, and mitigate AI-specific security threats
  • Ensure compliance and accountability through explainability, auditing, and governance

Certification Exam Domains

Domain Weight

Zero Trust Foundations for AI Systems 15%

  • Core Zero Trust principles (NIST 800-207)
  • Mapping ZT concepts to AI components
  • Zero Trust maturity in AI organizations

Identity, Access, and Authentication for AI 20% 

  • IAM and service mesh in AI
  • SPIFFE/SPIRE, OPA, workload identity
  • Model, data, and API access policies

Zero Trust AI Architecture Design 20%

  • Secure design patterns for AI pipelines
  • Model verification, attestation, and runtime protection
  • Secure enclaves, encryption, and segmentation

Secure MLOps and AI DevSecOps Pipelines 15%

  • Hardening model training and deployment
  • CI/CD pipelines with trust policies
  • Secure ML tools: MLflow, Kubeflow, Seldon

AI API and Endpoint Security 10% 

  • Authentication and API gateway hardening
  • Inference-time verification and monitoring
  • API abuse and model extraction prevention

AI Threat Detection and Incident Response 10%

  • Drift detection, adversarial behavior
  • SOC integration and logging strategies
  • Red team vs blue team AI labs

Governance, Privacy, and Compliance in AI 10% 

  • AI audits, explainability (XAI)
  • Federated learning and privacy-preserving AI
  • Regulatory alignment (GDPR, ISO/IEC 23894)

Certification Exam Details

  • Format: 75 multiple-choice and scenario-based questions
  • Duration: 90 minutes
  • Passing Score: 70%
  • Delivery: Online (proctored) or in-person testing center
  • Prerequisite: Experience in AI/ML, cybersecurity, or cloud security recommended
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