Certified Trustworthy AI Developer (CTAI-D) Certification Program by Tonex
The Certified Trustworthy AI Developer (CTAI-D) program trains professionals to design, develop, and deploy secure, reliable, and transparent AI systems. It equips developers with practical skills to embed trust and accountability into AI workflows from the start. Participants learn to implement explainability techniques, enforce OWASP LLM controls, and build robust RAG architectures.
The program also covers trust injection in agentic workflows, safe evaluation practices, and auditable logging for regulatory compliance. This certification emphasizes the cybersecurity implications of AI, preparing learners to mitigate risks like adversarial attacks, data leakage, and malicious prompt injections while enhancing resilience of AI-enabled systems.
Learning Objectives:
- Understand principles of secure AI software development lifecycle
- Implement OWASP controls in LLM-based applications
- Apply explainability techniques (e.g., SHAP, LIME) effectively
- Design trustworthy RAG pipelines with reliable retrieval mechanisms
- Inject trust features into agentic workflows using popular frameworks
- Establish auditable logging and safe evaluation procedures
Target Audience:
- Cybersecurity professionals
- Machine learning engineers
- Software engineers
- Prompt engineers
- Generative AI developers
Program Modules:
Module 1: Secure AI Software Development Life Cycle (AI-SDLC)
- Threat modeling in AI systems
- Secure data handling practices
- Privacy-by-design in AI pipelines
- Secure deployment of AI models
- Monitoring AI security post-deployment
- Governance and compliance considerations
Module 2: OWASP LLM Controls Implementation
- Input validation techniques for LLMs
- Output sanitization and guardrails
- Managing prompt injection threats
- Implementing rate limiting and authentication
- Red teaming LLM applications
- Securing APIs and endpoints
Module 3: Implementing AI Explainability
- Introduction to explainability in AI
- Using SHAP for feature importance
- Applying LIME for local explanations
- Crafting counterfactual examples
- Explaining outputs to non-technical stakeholders
- Balancing transparency and security
Module 4: RAG Architectures and Trustworthy Retrieval
- Basics of Retrieval-Augmented Generation
- Designing reliable retrieval pipelines
- Indexing and document store security
- Filtering and validation in RAG workflows
- Monitoring retrieval accuracy
- Reducing hallucinations through context integrity
Module 5: Trust Injection Techniques for Agentic Workflows
- Overview of agentic AI systems
- Building guardrails with Rebuff
- Leveraging LangChain for control flows
- Integrating policy engines
- Aligning agent behavior with goals
- Handling multi-agent trust scenarios
Module 6: Safety-Centric Evaluation and Auditing
- Defining evaluation metrics for trust and safety
- Testing against adversarial inputs
- Simulating edge-case scenarios
- Logging and tracing model behaviors
- Creating audit-ready prompt records
- Regulatory reporting best practices
Exam Domains:
- Principles of Trustworthy AI Design
- AI System Threats and Mitigation Strategies
- Privacy, Security, and Compliance in AI
- Human-AI Interaction and Accountability
- Monitoring and Auditing AI Behaviors
- Future Trends and Ethical Considerations in AI
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in trustworthy AI development. 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 Trustworthy AI Developer (CTAI-D).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified Trustworthy AI Developer (CTAI-D) Certification Training exam, candidates must achieve a score of 70% or higher.
Become a trusted expert in designing secure and transparent AI systems. Enroll today and strengthen your skills to build AI that people and organizations can trust.