Certified AI Functional Hazard Analysis Engineer (CAFHA) Certification Program by Tonex
The Certified AI Functional Hazard Analysis Engineer (CAFHA) Certification Program by Tonex prepares professionals to identify, analyze, document, and control functional hazards associated with artificial intelligence-enabled systems. The program examines AI safety principles, hazardous system behavior, autonomous decision risks, human-autonomy coordination, failure propagation, explainability limitations, dataset weaknesses, model drift, and operational uncertainty. Participants learn how to structure an AI Functional Hazard Analysis, establish hazard classifications, evaluate failure effects, assign risk levels, and develop defensible safety requirements.
The program follows a practical training approach that includes exercises, real-world case studies, and examples of processes and documentation used in AI safety and functional hazard analysis projects. It connects engineering analysis with governance, assurance, verification, and operational decision-making.
Cybersecurity has a direct impact on AI functional safety because compromised data, altered models, unauthorized commands, and adversarial inputs can create hazardous system behavior. Participants examine how cybersecurity weaknesses can influence safety controls, operational reliability, and risk acceptance decisions across the AI system lifecycle.
Learning Objectives
Upon successful completion of this program, participants will be able to:
- Explain the principles of AI functional safety and hazard-based engineering analysis.
- Identify hazardous AI behaviors across design, deployment, and operational environments.
- Develop structured AI hazard logs with traceable causes, effects, controls, and ownership.
- Conduct an AI Functional Hazard Analysis using severity and likelihood classifications.
- Evaluate dataset hazards, model drift, explainability limitations, and decision uncertainty.
- Integrate cybersecurity considerations into AI hazard identification and risk-control activities.
- Construct risk matrices and communicate safety findings to technical and governance stakeholders.
Audience
This certification program is designed for:
- AI Safety Engineers
- Functional Safety Engineers
- Systems Engineers
- Reliability and Assurance Professionals
- Autonomous Systems Engineers
- Risk Assessment Specialists
- Cybersecurity Professionals
- AI Governance and Compliance Professionals
- Defense and Aerospace Engineers
- Technical Program Managers
- Verification and Validation Professionals
- Safety Review Board Members
Program Modules
Module 1: Foundations of AI Functional Safety
- Principles of functional safety for AI-enabled systems
- Differences between conventional and AI-related hazards
- Safety responsibilities across the AI lifecycle
- Intended functions and reasonably foreseeable misuse
- Safety boundaries, assumptions, and operating constraints
- Relationship between system safety and AI assurance
- Roles of engineering, governance, and operational stakeholders
Module 2: Systematic Hazard Discovery and Classification
- Hazard identification methods for AI-enabled functions
- Sources of hazards within complex system architectures
- Preliminary hazard analysis and functional decomposition
- Unsafe control actions and hazardous decision outputs
- Severity, likelihood, exposure, and controllability factors
- Hazard categorization and prioritization techniques
- Development and maintenance of AI hazard logs
Module 3: Autonomous Decision Risk and Control
- Hazards arising from autonomous decision authority
- Context awareness and operational boundary limitations
- Unintended actions caused by conflicting objectives
- Escalation paths for uncertain AI decisions
- Human oversight and intervention requirements
- Fail-safe, degraded, and restricted operating states
- Safety controls for autonomous defense applications
Module 4: AI Failure Behavior and Explainability
- AI failure modes and their operational consequences
- False positives, false negatives, and confidence errors
- Unpredictable behavior under unfamiliar conditions
- Explainability limitations in safety-critical decisions
- Root-cause challenges within complex AI models
- Failure propagation across connected system functions
- Evidence requirements for safety-related AI explanations
Module 5: Data Integrity and Performance Degradation
- Dataset quality hazards and representation weaknesses
- Labeling errors, imbalance, and hidden correlations
- Data lineage and provenance for safety assurance
- Distribution shifts and changing operational conditions
- Model drift detection and performance interpretation
- Hazardous outcomes caused by corrupted information
- Data-related controls supporting dependable AI behavior
Module 6: Functional Risk Evaluation and Documentation
- Structure and purpose of an AI FHA
- Definition of functions, failures, and operational effects
- Risk matrix design and classification criteria
- Assignment of safety objectives and control requirements
- Traceability between hazards, controls, and evidence
- Residual risk evaluation and acceptance decisions
- Preparation of reviewable AI safety documentation
Exam Domains
- AI Safety Governance and Assurance Principles
- Hazard Recognition and Causal Analysis
- Autonomous Decision Authority and Oversight
- Intelligent System Failure Characterization
- Data Reliability and Behavioral Degradation
- Functional Risk Evaluation and Safety Evidence
Course Delivery
The course is delivered through a combination of expert-led lectures, interactive discussions, hands-on workshops, structured exercises, and project-based learning facilitated by specialists in AI safety, functional safety, and risk engineering. Participants receive access to supporting readings, real-world case studies, risk-analysis resources, hazard documentation examples, and practical tools for conducting AI Functional Hazard Analysis activities.
Assessment and Certification
Participants are assessed through quizzes, written assignments, structured hazard-analysis exercises, risk evaluation activities, and a capstone project. The assessments measure the participant’s ability to identify AI-related hazards, evaluate failure effects, classify functional risks, recommend appropriate controls, and produce traceable safety documentation.
Upon successful completion of the course requirements and certification examination, participants will receive the Certified AI Functional Hazard Analysis Engineer (CAFHA) certification from Tonex.
Question Types
- Multiple Choice Questions (MCQs)
- Scenario-Based Questions
- Hazard Classification Questions
- Risk Matrix Interpretation Questions
- Functional Failure Analysis Questions
- Documentation and Traceability Questions
Passing Criteria
To pass the Certified AI Functional Hazard Analysis Engineer (CAFHA) Certification Program exam, candidates must achieve a score of 70% or higher.
Strengthen your ability to evaluate hazardous AI behavior, document functional risks, and support dependable AI operations. Enroll in the Certified AI Functional Hazard Analysis Engineer (CAFHA) Certification Program by Tonex and develop the engineering judgment required to address safety, cybersecurity, autonomy, and operational assurance challenges.