Course Format & Delivery Details Your Path to Mastery—Flexible, Risk-Free, and Backed by Global Credibility
Enrolment in Mastering AI-Driven Functional Safety Engineering for Future-Proof Leadership grants you immediate access to a meticulously engineered learning experience designed for professionals who demand clarity, control, and career acceleration. This is not a generic course—it’s a precision-crafted framework trusted by engineers, safety leaders, and technical managers across industries including automotive, aerospace, medical devices, industrial automation, and beyond. Self-Paced, On-Demand, and Built for Real Lives
This course is delivered entirely on-demand, with no fixed start dates, deadlines, or time commitments. You progress at your own pace, on your schedule. Most learners report completing the core curriculum in approximately 28–35 hours of focused study—and many apply foundational techniques to real-world projects within just the first 72 hours. - Self-paced: Start today. Study whenever it suits you—early mornings, late nights, or between global assignments.
- Immediate online access: Once your materials are ready, you’ll receive your access details with full instructions—no waiting, no delays.
- Lifetime access: Revisit content anytime for the rest of your career. Updates are automatically included at no extra cost, ensuring your knowledge remains future-proof as AI and safety standards evolve.
- 24/7 global access: Access your coursework from any country, any device, at any time.
- Mobile-friendly: Learn during commutes, client breaks, or travel—seamlessly across smartphones, tablets, and desktops.
Real Support from Practicing Functional Safety Leaders
You are not learning in isolation. Each module includes direct pathways to instructor-guided clarification. Our team of certified functional safety engineers—each with 15+ years of experience in ISO 26262, IEC 61508, and AI validation—provide authoritative guidance, answer technical questions, and offer implementation feedback. This isn’t automated chat support. This is expert-level mentorship tailored to your use case. Certificate of Completion: A Credential That Opens Doors
Upon successful completion, you’ll earn a Certificate of Completion issued by The Art of Service—a globally recognised provider of professional engineering education, with alumni in over 120 countries. This certificate is independently verifiable, digitally sharable, and highly respected by employers, auditors, and regulatory stakeholders. It signals not just completion, but mastery of AI-integrated safety assurance at an executive level. Transparent Pricing, Zero Hidden Fees
We believe in fairness. The price you see includes everything—no surprise charges, no renewal fees, no premium tiers. What you get: lifetime access, all updates, a globally respected certificate, and full support—all for a single, straightforward investment. Accepted Payment Methods
We accept all major payment options: Visa, Mastercard, and PayPal. Secure checkout ensures your information is protected with bank-grade encryption. 100% Risk-Free Guarantee: Satisfied or Refunded
We reverse the risk. If, within 30 days of receiving your course access, you find the material does not meet your expectations for professional value, depth, or applicability, simply request a full refund. No forms, no hoops, no questions asked. This is our promise to deliver not just content—but impact. What Happens After Enrollment?
Shortly after enrolling, you’ll receive a confirmation email. Once your course materials are prepared, you’ll receive a separate email with your secure login and access instructions. This ensures your learning environment is fully configured and ready for immediate progress tracking, gamified milestones, and structured advancement. “Will This Work for Me?” — The Objection We’ve Already Answered
Yes—especially if you’ve struggled with applying theoretical safety models to AI-driven systems, or felt unprepared to lead audits, justify safety cases, or design robust AI validation frameworks. This course was built for real-world complexity, not textbook simplicity. This works even if: - You’re new to AI integration in safety-critical systems—but need to sound like an expert tomorrow.
- You’re a seasoned safety engineer, but AI terminology, probabilistic reasoning, and dynamic hazard analysis feel outside your comfort zone.
- You’re leading a team and need to unify safety culture across software developers, data scientists, and compliance officers.
- You’ve failed a compliance review, or anticipate one—and need to close gaps fast.
- You’re transitioning into a leadership role and must demonstrate strategic safety vision, not just technical execution.
Role-Specific Examples That Demonstrate Immediate Applicability
For Functional Safety Engineers: Learn how to retrofit ISO 26262 workflows to include AI model validation, using traceable, auditable documentation templates included in the course. For AI/ML Engineers: Master how to build explainability, failure resilience, and safety-by-design into neural networks—so your models don’t get rejected at audit. For Engineering Managers: Deploy the Risk-Adjusted AI Integration Matrix to prioritise tasks, allocate resources, and demonstrate due diligence to executives and regulators. Trusted by Industry Leaders: Real Stories, Real Results
“I used the AI-HARA framework from Module 4 in my next audit at a Tier-1 automotive supplier. The auditor specifically noted the maturity of our safety argument. Two weeks later, I was promoted to Safety Team Lead.” — Marco T., Munich, Germany “As an AI researcher, I never thought I’d grasp functional safety. This course broke it down without dumbing it down. I now co-lead the safety task force at my medical robotics startup.” — Anika R., Toronto, Canada “The certification opened doors I didn’t expect. I included it in my promotion packet. My director said it was the most credible piece of evidence I presented.” — Samuel K., Singapore Your Confidence Is Our Priority
Every aspect of this course—from structure to support to certification—is engineered to reduce uncertainty, increase confidence, and maximise your return on investment. You’re not just learning. You’re preparing to lead with authority in the most complex, high-stakes engineering environment of our time.
Extensive & Detailed Course Curriculum
Module 1: Foundations of AI-Driven Functional Safety - Introduction to Functional Safety in the Age of Artificial Intelligence
- Core Principles of IEC 61508, ISO 26262, and ISO 13849
- Understanding Safety Integrity Levels (SIL, ASIL) in AI Contexts
- Defining Safety Goals for AI-Enabled Systems
- The Role of Machine Learning in Safety-Critical Decision Making
- Key Challenges: Non-Determinism, Black-Box Models, and Dynamic Environments
- Differentiating Between AI, ML, Deep Learning, and Neural Networks
- Fundamentals of Probabilistic Risk Assessment
- Interpreting Regulatory Expectations Across Jurisdictions
- Establishing a Safety Mindset in Cross-Functional Teams
- Historical Perspective: Learning from AI Safety Incidents
- The Importance of Safety Culture in Agile AI Development
- Building a Personal Learning Roadmap for Mastery
- Using the Functional Safety Maturity Self-Assessment Tool
- Overview of Course Resources and Templates
Module 2: Integrating AI into Safety Lifecycle Frameworks - Mapping Traditional V-Model to AI Development Processes
- Adapting ISO 26262 Parts 3–9 for AI Modules
- Integrating AI Activities into the Safety Lifecycle
- Defining AI-Specific Safety Requirements
- Determining AI Contribution to System Risk
- Classifying AI Functions by Safety Relevance
- Introducing the Concept of AI Safety Envelopes
- Identifying Boundaries Between AI and Non-AI Components
- Managing Interfaces in Hybrid Systems
- Establishing AI Training and Operational Domains
- Concept Phase Deliverables for AI-Integrated Projects
- Developing Safety Cases for AI Components
- Integrating AI into Hazard and Risk Assessment (HARA)
- Using the AI Hazard Categorization Matrix
- Defining AI Failure Modes and Fault Tolerance
Module 3: AI-Enhanced Hazard Analysis and Risk Assessment - Conducting AI-Specific HARA (AI-HARA)
- Identifying Novel AI-Induced Hazards
- Assessing Probabilistic Outcome Distributions from ML Models
- Quantifying Uncertainty in AI Predictions
- Using Monte Carlo Simulations for AI Risk Evaluation
- Incorporating Epistemic and Aleatoric Uncertainty into Risk Models
- Dynamic Scenario Modelling for Edge Cases
- Developing AI Safety Goals from HARA Outcomes
- Deriving Functional Safety Requirements for AI
- Linking AI Behaviour to ASIL Decomposition Rules
- Creating Traceability Matrices for AI Requirements
- Integrating Human-in-the-Loop Considerations
- Analysing Feedback Loops in Adaptive AI Systems
- Assessing Emergent Behaviour Risks
- Scenario-Based Risk Prioritisation Framework
Module 4: Designing AI Systems for Safety Assurance - Safety-by-Design Principles for AI Architectures
- Selecting Appropriate AI Models for Safety-Critical Roles
- Architectural Patterns: Modular, Layered, and Hybrid AI Systems
- Incorporating Redundancy and Diversity in AI Components
- Fail-Operational and Fail-Safe Strategies with AI
- Leveraging Ensemble Methods for Robustness
- Designing for Explainability Without Sacrificing Performance
- Implementing Uncertainty-Aware Decision Making
- Using Confidence Thresholds to Gate AI Outputs
- Designing Safe Fallback Mechanisms
- Integrating Model Monitoring and Drift Detection
- Architectural Safety Patterns for Deep Neural Networks
- Managing Model Updates and Versioning Safely
- Safe Integration of Third-Party AI Libraries
- Designing for Auditable AI Decision Trails
Module 5: AI Model Development with Functional Safety in Mind - Defining AI Training Objectives Aligned to Safety Goals
- Data Quality Requirements for Safety-Critical AI
- Data Provenance and Traceability in AI Training
- Curating Safety-Representative Datasets
- Handling Imbalanced and Rare-Event Data in Safety Contexts
- Annotating Data for Functional Safety Scenarios
- Validation Set Design for Edge Case Coverage
- Avoiding Data Leakage in Safety Validation
- Feature Engineering for Safety-Sensitive Performance
- Model Selection Based on Interpretability and Robustness
- Regularisation Techniques to Prevent Overfitting to Unsafe Patterns
- Training with Adversarial Examples for Resilience
- Incorporating Safety Constraints into Loss Functions
- Balancing Accuracy, Latency, and Safety in Deployment
- Documenting Model Development for Audit Readiness
Module 6: Verification and Validation of AI in Safety Systems - Developing AI Verification Plans Aligned to ISO Standards
- Test Strategy for Non-Deterministic AI Systems
- Defining Acceptance Criteria for AI Outputs
- Designing Test Cases for AI Edge Behaviours
- Using Synthetic Data Generation for Risk Coverage
- Simulation-Based Testing in Digital Twin Environments
- Grey-Box Testing of AI Components
- Formal Methods for AI Component Verification
- Symbolic Execution for Neural Networks
- Invariant-Based Validation of AI Behaviour
- Monitoring for Distributional Shift and Concept Drift
- Runtime Verification of AI Decision Boundaries
- Statistical Confidence in AI Performance Metrics
- Using Bootstrap Methods for Metric Uncertainty
- Documenting V&V Processes for Certification Bodies
Module 7: Achieving Explainability and Transparency in AI - Why Explainability Is a Functional Safety Requirement
- Global Regulatory Demands for AI Transparency
- Post-Hoc vs. Intrinsic Explainability Methods
- Using SHAP and LIME for Feature Attribution
- Visualising Attention Mechanisms in Deep Learning Models
- Generating Natural Language Justifications for AI Decisions
- Designing Human-Understandable Safety Explanations
- Creating Audit-Ready Explanation Reports
- Building Trust with Stakeholders Through Transparency
- Limitations of Current XAI Methods in Critical Systems
- Incorporating Explainability into Real-Time Monitoring
- Designing Interactive Explanation Interfaces
- Quantifying Explanation Fidelity and Reliability
- Using Counterfactual Explanations for Safety Debugging
- Regulatory Alignment of Explainability Documentation
Module 8: Managing AI Model Lifecycle and Updates - Safety Implications of Model Retraining and Updates
- Defining Model Revalidation Triggers
- Impact Analysis for AI Model Changes
- Version Control Strategies for AI Models
- Change Management Processes for AI Components
- Ensuring Backward Compatibility in AI Systems
- Automated Regression Testing for AI Updates
- Monitoring Model Performance in Production
- Handling Concept Drift with Automated Alerts
- Implementing Safe Rollback Mechanisms
- Documentation Requirements for Model Updates
- Establishing AI Model Deprecation Protocols
- Integrating AI Model Management into Configuration Control
- Creating AI Software Bills of Materials (SBOM)
- Security and Integrity Checks During AI Updates
Module 9: AI in Real-Time and Embedded Safety Systems - Timing Constraints for AI in Real-Time Control Loops
- Latency Budgeting for AI Inference Engines
- Resource Management: CPU, Memory, and Power in Embedded AI
- Optimising Neural Networks for Edge Deployment
- Quantisation and Pruning for Safety Without Performance Loss
- Using ONNX and Other Interchange Formats for Safety Targets
- Implementing AI on Safety-Certified Hardware
- Co-Designing AI Models with Real-Time Operating Systems
- Interrupt Handling and Priority Management for AI Tasks
- Ensuring Deterministic AI Behaviour Where Needed
- Measuring AI Inference Variability Under Load
- Designing for Graceful Degradation Under Stress
- Safe Integration with Legacy Safety Systems
- Thermal and Environmental Robustness in Embedded AI
- Power-Fail Safety Mechanisms for AI Modules
Module 10: AI Safety in Autonomous and Adaptive Systems - Challenges of Autonomy in Safety-Critical Domains
- Functional Safety for Reinforcement Learning Agents
- Handling Unseen Scenarios in Self-Learning Systems
- Safe Exploration Strategies in Adaptive AI
- Dynamic Replanning and Its Safety Implications
- Formal Specification of Safe Adaptive Behaviours
- Monitoring AI for Deviations from Safe Policies
- Using Digital Twins for Safe Adaptation Testing
- Human Override and Intervention Protocols
- Designing for Seamless Handover Between AI and Humans
- Safety Cases for Continuously Learning AI
- Addressing Moral and Ethical Dilemmas in AI Decision Logic
- Compliance with UN Regulation No. 157 and Equivalent Standards
- Verification of Autonomous Safety Assurance Systems
- Creating Digital Logbooks for Autonomous System History
Module 11: Cross-Domain Applications of AI-Driven Safety - AI in Automotive Functional Safety (ISO 26262)
- Medical AI and IEC 62304 Compliance
- Industrial Robotics and IEC 61508 Integration
- Aerospace and DO-178C for AI Components
- Railway Applications Under EN 50128 and EN 50657
- Oil & Gas: Managing AI in High-Consequence Environments
- AI for Predictive Maintenance with Safety Implications
- Smart Grids and AI-Enabled Grid Stability
- AI in Safety Instrumented Systems (SIS)
- Applying Functional Safety to AI-Controlled Drones
- Safety Assurance for AI in Prosthetics and Exoskeletons
- AI in Nuclear Plant Monitoring and Control
- AI for Collision Avoidance in Mixed Traffic Environments
- Functional Safety in AI-Powered Cybersecurity Systems
- Adapting Frameworks for Domain-Specific Regulators
Module 12: Organisational Leadership and Safety Governance - Establishing AI Functional Safety Governance Frameworks
- Roles and Responsibilities in AI Safety Teams
- Training Cross-Functional Engineers in AI Safety
- Developing Internal AI Safety Policies and Standards
- Creating a Safety-First AI Development Culture
- Managing Conflicts Between Innovation and Compliance
- Conducting Internal AI Safety Audits
- Reporting AI Safety Metrics to Executive Leadership
- Integrating AI Safety into Enterprise Risk Management
- Engaging with Regulators on AI Safety Proposals
- Managing Third-Party AI Vendors and Supply Chain Risks
- Intellectual Property and AI Safety Documentation
- Budgeting for AI Safety Assurance Activities
- Building a Centre of Excellence for AI Safety
- Succession Planning for AI Safety Expertise
Module 13: Advanced Topics in AI and System Safety Engineering - Formal Verification of Neural Networks Using SMT Solvers
- Reachability Analysis for Deep Learning Models
- Interval Bound Propagation for Safety Guarantees
- Using Abstract Interpretation in AI Safety
- Provable Robustness Against Adversarial Perturbations
- Conformance to IEEE P2851 Draft Standard
- Integrating Causal Reasoning into AI Safety Models
- Modelling System-of-Systems AI Interactions
- Dynamic Risk Assessment in Multi-Agent AI Environments
- Using Digital Twins for Whole-System Safety Simulation
- Resilient AI for Cyber-Physical Systems
- Safety Implications of Federated Learning Architectures
- Differential Privacy and Safety in AI Training
- Secure Aggregation in Distributed AI without Risk
- Future-Proofing AI Safety for Quantum-Enhanced Models
Module 14: Practical Application and Case Studies - Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
Module 1: Foundations of AI-Driven Functional Safety - Introduction to Functional Safety in the Age of Artificial Intelligence
- Core Principles of IEC 61508, ISO 26262, and ISO 13849
- Understanding Safety Integrity Levels (SIL, ASIL) in AI Contexts
- Defining Safety Goals for AI-Enabled Systems
- The Role of Machine Learning in Safety-Critical Decision Making
- Key Challenges: Non-Determinism, Black-Box Models, and Dynamic Environments
- Differentiating Between AI, ML, Deep Learning, and Neural Networks
- Fundamentals of Probabilistic Risk Assessment
- Interpreting Regulatory Expectations Across Jurisdictions
- Establishing a Safety Mindset in Cross-Functional Teams
- Historical Perspective: Learning from AI Safety Incidents
- The Importance of Safety Culture in Agile AI Development
- Building a Personal Learning Roadmap for Mastery
- Using the Functional Safety Maturity Self-Assessment Tool
- Overview of Course Resources and Templates
Module 2: Integrating AI into Safety Lifecycle Frameworks - Mapping Traditional V-Model to AI Development Processes
- Adapting ISO 26262 Parts 3–9 for AI Modules
- Integrating AI Activities into the Safety Lifecycle
- Defining AI-Specific Safety Requirements
- Determining AI Contribution to System Risk
- Classifying AI Functions by Safety Relevance
- Introducing the Concept of AI Safety Envelopes
- Identifying Boundaries Between AI and Non-AI Components
- Managing Interfaces in Hybrid Systems
- Establishing AI Training and Operational Domains
- Concept Phase Deliverables for AI-Integrated Projects
- Developing Safety Cases for AI Components
- Integrating AI into Hazard and Risk Assessment (HARA)
- Using the AI Hazard Categorization Matrix
- Defining AI Failure Modes and Fault Tolerance
Module 3: AI-Enhanced Hazard Analysis and Risk Assessment - Conducting AI-Specific HARA (AI-HARA)
- Identifying Novel AI-Induced Hazards
- Assessing Probabilistic Outcome Distributions from ML Models
- Quantifying Uncertainty in AI Predictions
- Using Monte Carlo Simulations for AI Risk Evaluation
- Incorporating Epistemic and Aleatoric Uncertainty into Risk Models
- Dynamic Scenario Modelling for Edge Cases
- Developing AI Safety Goals from HARA Outcomes
- Deriving Functional Safety Requirements for AI
- Linking AI Behaviour to ASIL Decomposition Rules
- Creating Traceability Matrices for AI Requirements
- Integrating Human-in-the-Loop Considerations
- Analysing Feedback Loops in Adaptive AI Systems
- Assessing Emergent Behaviour Risks
- Scenario-Based Risk Prioritisation Framework
Module 4: Designing AI Systems for Safety Assurance - Safety-by-Design Principles for AI Architectures
- Selecting Appropriate AI Models for Safety-Critical Roles
- Architectural Patterns: Modular, Layered, and Hybrid AI Systems
- Incorporating Redundancy and Diversity in AI Components
- Fail-Operational and Fail-Safe Strategies with AI
- Leveraging Ensemble Methods for Robustness
- Designing for Explainability Without Sacrificing Performance
- Implementing Uncertainty-Aware Decision Making
- Using Confidence Thresholds to Gate AI Outputs
- Designing Safe Fallback Mechanisms
- Integrating Model Monitoring and Drift Detection
- Architectural Safety Patterns for Deep Neural Networks
- Managing Model Updates and Versioning Safely
- Safe Integration of Third-Party AI Libraries
- Designing for Auditable AI Decision Trails
Module 5: AI Model Development with Functional Safety in Mind - Defining AI Training Objectives Aligned to Safety Goals
- Data Quality Requirements for Safety-Critical AI
- Data Provenance and Traceability in AI Training
- Curating Safety-Representative Datasets
- Handling Imbalanced and Rare-Event Data in Safety Contexts
- Annotating Data for Functional Safety Scenarios
- Validation Set Design for Edge Case Coverage
- Avoiding Data Leakage in Safety Validation
- Feature Engineering for Safety-Sensitive Performance
- Model Selection Based on Interpretability and Robustness
- Regularisation Techniques to Prevent Overfitting to Unsafe Patterns
- Training with Adversarial Examples for Resilience
- Incorporating Safety Constraints into Loss Functions
- Balancing Accuracy, Latency, and Safety in Deployment
- Documenting Model Development for Audit Readiness
Module 6: Verification and Validation of AI in Safety Systems - Developing AI Verification Plans Aligned to ISO Standards
- Test Strategy for Non-Deterministic AI Systems
- Defining Acceptance Criteria for AI Outputs
- Designing Test Cases for AI Edge Behaviours
- Using Synthetic Data Generation for Risk Coverage
- Simulation-Based Testing in Digital Twin Environments
- Grey-Box Testing of AI Components
- Formal Methods for AI Component Verification
- Symbolic Execution for Neural Networks
- Invariant-Based Validation of AI Behaviour
- Monitoring for Distributional Shift and Concept Drift
- Runtime Verification of AI Decision Boundaries
- Statistical Confidence in AI Performance Metrics
- Using Bootstrap Methods for Metric Uncertainty
- Documenting V&V Processes for Certification Bodies
Module 7: Achieving Explainability and Transparency in AI - Why Explainability Is a Functional Safety Requirement
- Global Regulatory Demands for AI Transparency
- Post-Hoc vs. Intrinsic Explainability Methods
- Using SHAP and LIME for Feature Attribution
- Visualising Attention Mechanisms in Deep Learning Models
- Generating Natural Language Justifications for AI Decisions
- Designing Human-Understandable Safety Explanations
- Creating Audit-Ready Explanation Reports
- Building Trust with Stakeholders Through Transparency
- Limitations of Current XAI Methods in Critical Systems
- Incorporating Explainability into Real-Time Monitoring
- Designing Interactive Explanation Interfaces
- Quantifying Explanation Fidelity and Reliability
- Using Counterfactual Explanations for Safety Debugging
- Regulatory Alignment of Explainability Documentation
Module 8: Managing AI Model Lifecycle and Updates - Safety Implications of Model Retraining and Updates
- Defining Model Revalidation Triggers
- Impact Analysis for AI Model Changes
- Version Control Strategies for AI Models
- Change Management Processes for AI Components
- Ensuring Backward Compatibility in AI Systems
- Automated Regression Testing for AI Updates
- Monitoring Model Performance in Production
- Handling Concept Drift with Automated Alerts
- Implementing Safe Rollback Mechanisms
- Documentation Requirements for Model Updates
- Establishing AI Model Deprecation Protocols
- Integrating AI Model Management into Configuration Control
- Creating AI Software Bills of Materials (SBOM)
- Security and Integrity Checks During AI Updates
Module 9: AI in Real-Time and Embedded Safety Systems - Timing Constraints for AI in Real-Time Control Loops
- Latency Budgeting for AI Inference Engines
- Resource Management: CPU, Memory, and Power in Embedded AI
- Optimising Neural Networks for Edge Deployment
- Quantisation and Pruning for Safety Without Performance Loss
- Using ONNX and Other Interchange Formats for Safety Targets
- Implementing AI on Safety-Certified Hardware
- Co-Designing AI Models with Real-Time Operating Systems
- Interrupt Handling and Priority Management for AI Tasks
- Ensuring Deterministic AI Behaviour Where Needed
- Measuring AI Inference Variability Under Load
- Designing for Graceful Degradation Under Stress
- Safe Integration with Legacy Safety Systems
- Thermal and Environmental Robustness in Embedded AI
- Power-Fail Safety Mechanisms for AI Modules
Module 10: AI Safety in Autonomous and Adaptive Systems - Challenges of Autonomy in Safety-Critical Domains
- Functional Safety for Reinforcement Learning Agents
- Handling Unseen Scenarios in Self-Learning Systems
- Safe Exploration Strategies in Adaptive AI
- Dynamic Replanning and Its Safety Implications
- Formal Specification of Safe Adaptive Behaviours
- Monitoring AI for Deviations from Safe Policies
- Using Digital Twins for Safe Adaptation Testing
- Human Override and Intervention Protocols
- Designing for Seamless Handover Between AI and Humans
- Safety Cases for Continuously Learning AI
- Addressing Moral and Ethical Dilemmas in AI Decision Logic
- Compliance with UN Regulation No. 157 and Equivalent Standards
- Verification of Autonomous Safety Assurance Systems
- Creating Digital Logbooks for Autonomous System History
Module 11: Cross-Domain Applications of AI-Driven Safety - AI in Automotive Functional Safety (ISO 26262)
- Medical AI and IEC 62304 Compliance
- Industrial Robotics and IEC 61508 Integration
- Aerospace and DO-178C for AI Components
- Railway Applications Under EN 50128 and EN 50657
- Oil & Gas: Managing AI in High-Consequence Environments
- AI for Predictive Maintenance with Safety Implications
- Smart Grids and AI-Enabled Grid Stability
- AI in Safety Instrumented Systems (SIS)
- Applying Functional Safety to AI-Controlled Drones
- Safety Assurance for AI in Prosthetics and Exoskeletons
- AI in Nuclear Plant Monitoring and Control
- AI for Collision Avoidance in Mixed Traffic Environments
- Functional Safety in AI-Powered Cybersecurity Systems
- Adapting Frameworks for Domain-Specific Regulators
Module 12: Organisational Leadership and Safety Governance - Establishing AI Functional Safety Governance Frameworks
- Roles and Responsibilities in AI Safety Teams
- Training Cross-Functional Engineers in AI Safety
- Developing Internal AI Safety Policies and Standards
- Creating a Safety-First AI Development Culture
- Managing Conflicts Between Innovation and Compliance
- Conducting Internal AI Safety Audits
- Reporting AI Safety Metrics to Executive Leadership
- Integrating AI Safety into Enterprise Risk Management
- Engaging with Regulators on AI Safety Proposals
- Managing Third-Party AI Vendors and Supply Chain Risks
- Intellectual Property and AI Safety Documentation
- Budgeting for AI Safety Assurance Activities
- Building a Centre of Excellence for AI Safety
- Succession Planning for AI Safety Expertise
Module 13: Advanced Topics in AI and System Safety Engineering - Formal Verification of Neural Networks Using SMT Solvers
- Reachability Analysis for Deep Learning Models
- Interval Bound Propagation for Safety Guarantees
- Using Abstract Interpretation in AI Safety
- Provable Robustness Against Adversarial Perturbations
- Conformance to IEEE P2851 Draft Standard
- Integrating Causal Reasoning into AI Safety Models
- Modelling System-of-Systems AI Interactions
- Dynamic Risk Assessment in Multi-Agent AI Environments
- Using Digital Twins for Whole-System Safety Simulation
- Resilient AI for Cyber-Physical Systems
- Safety Implications of Federated Learning Architectures
- Differential Privacy and Safety in AI Training
- Secure Aggregation in Distributed AI without Risk
- Future-Proofing AI Safety for Quantum-Enhanced Models
Module 14: Practical Application and Case Studies - Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
- Mapping Traditional V-Model to AI Development Processes
- Adapting ISO 26262 Parts 3–9 for AI Modules
- Integrating AI Activities into the Safety Lifecycle
- Defining AI-Specific Safety Requirements
- Determining AI Contribution to System Risk
- Classifying AI Functions by Safety Relevance
- Introducing the Concept of AI Safety Envelopes
- Identifying Boundaries Between AI and Non-AI Components
- Managing Interfaces in Hybrid Systems
- Establishing AI Training and Operational Domains
- Concept Phase Deliverables for AI-Integrated Projects
- Developing Safety Cases for AI Components
- Integrating AI into Hazard and Risk Assessment (HARA)
- Using the AI Hazard Categorization Matrix
- Defining AI Failure Modes and Fault Tolerance
Module 3: AI-Enhanced Hazard Analysis and Risk Assessment - Conducting AI-Specific HARA (AI-HARA)
- Identifying Novel AI-Induced Hazards
- Assessing Probabilistic Outcome Distributions from ML Models
- Quantifying Uncertainty in AI Predictions
- Using Monte Carlo Simulations for AI Risk Evaluation
- Incorporating Epistemic and Aleatoric Uncertainty into Risk Models
- Dynamic Scenario Modelling for Edge Cases
- Developing AI Safety Goals from HARA Outcomes
- Deriving Functional Safety Requirements for AI
- Linking AI Behaviour to ASIL Decomposition Rules
- Creating Traceability Matrices for AI Requirements
- Integrating Human-in-the-Loop Considerations
- Analysing Feedback Loops in Adaptive AI Systems
- Assessing Emergent Behaviour Risks
- Scenario-Based Risk Prioritisation Framework
Module 4: Designing AI Systems for Safety Assurance - Safety-by-Design Principles for AI Architectures
- Selecting Appropriate AI Models for Safety-Critical Roles
- Architectural Patterns: Modular, Layered, and Hybrid AI Systems
- Incorporating Redundancy and Diversity in AI Components
- Fail-Operational and Fail-Safe Strategies with AI
- Leveraging Ensemble Methods for Robustness
- Designing for Explainability Without Sacrificing Performance
- Implementing Uncertainty-Aware Decision Making
- Using Confidence Thresholds to Gate AI Outputs
- Designing Safe Fallback Mechanisms
- Integrating Model Monitoring and Drift Detection
- Architectural Safety Patterns for Deep Neural Networks
- Managing Model Updates and Versioning Safely
- Safe Integration of Third-Party AI Libraries
- Designing for Auditable AI Decision Trails
Module 5: AI Model Development with Functional Safety in Mind - Defining AI Training Objectives Aligned to Safety Goals
- Data Quality Requirements for Safety-Critical AI
- Data Provenance and Traceability in AI Training
- Curating Safety-Representative Datasets
- Handling Imbalanced and Rare-Event Data in Safety Contexts
- Annotating Data for Functional Safety Scenarios
- Validation Set Design for Edge Case Coverage
- Avoiding Data Leakage in Safety Validation
- Feature Engineering for Safety-Sensitive Performance
- Model Selection Based on Interpretability and Robustness
- Regularisation Techniques to Prevent Overfitting to Unsafe Patterns
- Training with Adversarial Examples for Resilience
- Incorporating Safety Constraints into Loss Functions
- Balancing Accuracy, Latency, and Safety in Deployment
- Documenting Model Development for Audit Readiness
Module 6: Verification and Validation of AI in Safety Systems - Developing AI Verification Plans Aligned to ISO Standards
- Test Strategy for Non-Deterministic AI Systems
- Defining Acceptance Criteria for AI Outputs
- Designing Test Cases for AI Edge Behaviours
- Using Synthetic Data Generation for Risk Coverage
- Simulation-Based Testing in Digital Twin Environments
- Grey-Box Testing of AI Components
- Formal Methods for AI Component Verification
- Symbolic Execution for Neural Networks
- Invariant-Based Validation of AI Behaviour
- Monitoring for Distributional Shift and Concept Drift
- Runtime Verification of AI Decision Boundaries
- Statistical Confidence in AI Performance Metrics
- Using Bootstrap Methods for Metric Uncertainty
- Documenting V&V Processes for Certification Bodies
Module 7: Achieving Explainability and Transparency in AI - Why Explainability Is a Functional Safety Requirement
- Global Regulatory Demands for AI Transparency
- Post-Hoc vs. Intrinsic Explainability Methods
- Using SHAP and LIME for Feature Attribution
- Visualising Attention Mechanisms in Deep Learning Models
- Generating Natural Language Justifications for AI Decisions
- Designing Human-Understandable Safety Explanations
- Creating Audit-Ready Explanation Reports
- Building Trust with Stakeholders Through Transparency
- Limitations of Current XAI Methods in Critical Systems
- Incorporating Explainability into Real-Time Monitoring
- Designing Interactive Explanation Interfaces
- Quantifying Explanation Fidelity and Reliability
- Using Counterfactual Explanations for Safety Debugging
- Regulatory Alignment of Explainability Documentation
Module 8: Managing AI Model Lifecycle and Updates - Safety Implications of Model Retraining and Updates
- Defining Model Revalidation Triggers
- Impact Analysis for AI Model Changes
- Version Control Strategies for AI Models
- Change Management Processes for AI Components
- Ensuring Backward Compatibility in AI Systems
- Automated Regression Testing for AI Updates
- Monitoring Model Performance in Production
- Handling Concept Drift with Automated Alerts
- Implementing Safe Rollback Mechanisms
- Documentation Requirements for Model Updates
- Establishing AI Model Deprecation Protocols
- Integrating AI Model Management into Configuration Control
- Creating AI Software Bills of Materials (SBOM)
- Security and Integrity Checks During AI Updates
Module 9: AI in Real-Time and Embedded Safety Systems - Timing Constraints for AI in Real-Time Control Loops
- Latency Budgeting for AI Inference Engines
- Resource Management: CPU, Memory, and Power in Embedded AI
- Optimising Neural Networks for Edge Deployment
- Quantisation and Pruning for Safety Without Performance Loss
- Using ONNX and Other Interchange Formats for Safety Targets
- Implementing AI on Safety-Certified Hardware
- Co-Designing AI Models with Real-Time Operating Systems
- Interrupt Handling and Priority Management for AI Tasks
- Ensuring Deterministic AI Behaviour Where Needed
- Measuring AI Inference Variability Under Load
- Designing for Graceful Degradation Under Stress
- Safe Integration with Legacy Safety Systems
- Thermal and Environmental Robustness in Embedded AI
- Power-Fail Safety Mechanisms for AI Modules
Module 10: AI Safety in Autonomous and Adaptive Systems - Challenges of Autonomy in Safety-Critical Domains
- Functional Safety for Reinforcement Learning Agents
- Handling Unseen Scenarios in Self-Learning Systems
- Safe Exploration Strategies in Adaptive AI
- Dynamic Replanning and Its Safety Implications
- Formal Specification of Safe Adaptive Behaviours
- Monitoring AI for Deviations from Safe Policies
- Using Digital Twins for Safe Adaptation Testing
- Human Override and Intervention Protocols
- Designing for Seamless Handover Between AI and Humans
- Safety Cases for Continuously Learning AI
- Addressing Moral and Ethical Dilemmas in AI Decision Logic
- Compliance with UN Regulation No. 157 and Equivalent Standards
- Verification of Autonomous Safety Assurance Systems
- Creating Digital Logbooks for Autonomous System History
Module 11: Cross-Domain Applications of AI-Driven Safety - AI in Automotive Functional Safety (ISO 26262)
- Medical AI and IEC 62304 Compliance
- Industrial Robotics and IEC 61508 Integration
- Aerospace and DO-178C for AI Components
- Railway Applications Under EN 50128 and EN 50657
- Oil & Gas: Managing AI in High-Consequence Environments
- AI for Predictive Maintenance with Safety Implications
- Smart Grids and AI-Enabled Grid Stability
- AI in Safety Instrumented Systems (SIS)
- Applying Functional Safety to AI-Controlled Drones
- Safety Assurance for AI in Prosthetics and Exoskeletons
- AI in Nuclear Plant Monitoring and Control
- AI for Collision Avoidance in Mixed Traffic Environments
- Functional Safety in AI-Powered Cybersecurity Systems
- Adapting Frameworks for Domain-Specific Regulators
Module 12: Organisational Leadership and Safety Governance - Establishing AI Functional Safety Governance Frameworks
- Roles and Responsibilities in AI Safety Teams
- Training Cross-Functional Engineers in AI Safety
- Developing Internal AI Safety Policies and Standards
- Creating a Safety-First AI Development Culture
- Managing Conflicts Between Innovation and Compliance
- Conducting Internal AI Safety Audits
- Reporting AI Safety Metrics to Executive Leadership
- Integrating AI Safety into Enterprise Risk Management
- Engaging with Regulators on AI Safety Proposals
- Managing Third-Party AI Vendors and Supply Chain Risks
- Intellectual Property and AI Safety Documentation
- Budgeting for AI Safety Assurance Activities
- Building a Centre of Excellence for AI Safety
- Succession Planning for AI Safety Expertise
Module 13: Advanced Topics in AI and System Safety Engineering - Formal Verification of Neural Networks Using SMT Solvers
- Reachability Analysis for Deep Learning Models
- Interval Bound Propagation for Safety Guarantees
- Using Abstract Interpretation in AI Safety
- Provable Robustness Against Adversarial Perturbations
- Conformance to IEEE P2851 Draft Standard
- Integrating Causal Reasoning into AI Safety Models
- Modelling System-of-Systems AI Interactions
- Dynamic Risk Assessment in Multi-Agent AI Environments
- Using Digital Twins for Whole-System Safety Simulation
- Resilient AI for Cyber-Physical Systems
- Safety Implications of Federated Learning Architectures
- Differential Privacy and Safety in AI Training
- Secure Aggregation in Distributed AI without Risk
- Future-Proofing AI Safety for Quantum-Enhanced Models
Module 14: Practical Application and Case Studies - Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
- Safety-by-Design Principles for AI Architectures
- Selecting Appropriate AI Models for Safety-Critical Roles
- Architectural Patterns: Modular, Layered, and Hybrid AI Systems
- Incorporating Redundancy and Diversity in AI Components
- Fail-Operational and Fail-Safe Strategies with AI
- Leveraging Ensemble Methods for Robustness
- Designing for Explainability Without Sacrificing Performance
- Implementing Uncertainty-Aware Decision Making
- Using Confidence Thresholds to Gate AI Outputs
- Designing Safe Fallback Mechanisms
- Integrating Model Monitoring and Drift Detection
- Architectural Safety Patterns for Deep Neural Networks
- Managing Model Updates and Versioning Safely
- Safe Integration of Third-Party AI Libraries
- Designing for Auditable AI Decision Trails
Module 5: AI Model Development with Functional Safety in Mind - Defining AI Training Objectives Aligned to Safety Goals
- Data Quality Requirements for Safety-Critical AI
- Data Provenance and Traceability in AI Training
- Curating Safety-Representative Datasets
- Handling Imbalanced and Rare-Event Data in Safety Contexts
- Annotating Data for Functional Safety Scenarios
- Validation Set Design for Edge Case Coverage
- Avoiding Data Leakage in Safety Validation
- Feature Engineering for Safety-Sensitive Performance
- Model Selection Based on Interpretability and Robustness
- Regularisation Techniques to Prevent Overfitting to Unsafe Patterns
- Training with Adversarial Examples for Resilience
- Incorporating Safety Constraints into Loss Functions
- Balancing Accuracy, Latency, and Safety in Deployment
- Documenting Model Development for Audit Readiness
Module 6: Verification and Validation of AI in Safety Systems - Developing AI Verification Plans Aligned to ISO Standards
- Test Strategy for Non-Deterministic AI Systems
- Defining Acceptance Criteria for AI Outputs
- Designing Test Cases for AI Edge Behaviours
- Using Synthetic Data Generation for Risk Coverage
- Simulation-Based Testing in Digital Twin Environments
- Grey-Box Testing of AI Components
- Formal Methods for AI Component Verification
- Symbolic Execution for Neural Networks
- Invariant-Based Validation of AI Behaviour
- Monitoring for Distributional Shift and Concept Drift
- Runtime Verification of AI Decision Boundaries
- Statistical Confidence in AI Performance Metrics
- Using Bootstrap Methods for Metric Uncertainty
- Documenting V&V Processes for Certification Bodies
Module 7: Achieving Explainability and Transparency in AI - Why Explainability Is a Functional Safety Requirement
- Global Regulatory Demands for AI Transparency
- Post-Hoc vs. Intrinsic Explainability Methods
- Using SHAP and LIME for Feature Attribution
- Visualising Attention Mechanisms in Deep Learning Models
- Generating Natural Language Justifications for AI Decisions
- Designing Human-Understandable Safety Explanations
- Creating Audit-Ready Explanation Reports
- Building Trust with Stakeholders Through Transparency
- Limitations of Current XAI Methods in Critical Systems
- Incorporating Explainability into Real-Time Monitoring
- Designing Interactive Explanation Interfaces
- Quantifying Explanation Fidelity and Reliability
- Using Counterfactual Explanations for Safety Debugging
- Regulatory Alignment of Explainability Documentation
Module 8: Managing AI Model Lifecycle and Updates - Safety Implications of Model Retraining and Updates
- Defining Model Revalidation Triggers
- Impact Analysis for AI Model Changes
- Version Control Strategies for AI Models
- Change Management Processes for AI Components
- Ensuring Backward Compatibility in AI Systems
- Automated Regression Testing for AI Updates
- Monitoring Model Performance in Production
- Handling Concept Drift with Automated Alerts
- Implementing Safe Rollback Mechanisms
- Documentation Requirements for Model Updates
- Establishing AI Model Deprecation Protocols
- Integrating AI Model Management into Configuration Control
- Creating AI Software Bills of Materials (SBOM)
- Security and Integrity Checks During AI Updates
Module 9: AI in Real-Time and Embedded Safety Systems - Timing Constraints for AI in Real-Time Control Loops
- Latency Budgeting for AI Inference Engines
- Resource Management: CPU, Memory, and Power in Embedded AI
- Optimising Neural Networks for Edge Deployment
- Quantisation and Pruning for Safety Without Performance Loss
- Using ONNX and Other Interchange Formats for Safety Targets
- Implementing AI on Safety-Certified Hardware
- Co-Designing AI Models with Real-Time Operating Systems
- Interrupt Handling and Priority Management for AI Tasks
- Ensuring Deterministic AI Behaviour Where Needed
- Measuring AI Inference Variability Under Load
- Designing for Graceful Degradation Under Stress
- Safe Integration with Legacy Safety Systems
- Thermal and Environmental Robustness in Embedded AI
- Power-Fail Safety Mechanisms for AI Modules
Module 10: AI Safety in Autonomous and Adaptive Systems - Challenges of Autonomy in Safety-Critical Domains
- Functional Safety for Reinforcement Learning Agents
- Handling Unseen Scenarios in Self-Learning Systems
- Safe Exploration Strategies in Adaptive AI
- Dynamic Replanning and Its Safety Implications
- Formal Specification of Safe Adaptive Behaviours
- Monitoring AI for Deviations from Safe Policies
- Using Digital Twins for Safe Adaptation Testing
- Human Override and Intervention Protocols
- Designing for Seamless Handover Between AI and Humans
- Safety Cases for Continuously Learning AI
- Addressing Moral and Ethical Dilemmas in AI Decision Logic
- Compliance with UN Regulation No. 157 and Equivalent Standards
- Verification of Autonomous Safety Assurance Systems
- Creating Digital Logbooks for Autonomous System History
Module 11: Cross-Domain Applications of AI-Driven Safety - AI in Automotive Functional Safety (ISO 26262)
- Medical AI and IEC 62304 Compliance
- Industrial Robotics and IEC 61508 Integration
- Aerospace and DO-178C for AI Components
- Railway Applications Under EN 50128 and EN 50657
- Oil & Gas: Managing AI in High-Consequence Environments
- AI for Predictive Maintenance with Safety Implications
- Smart Grids and AI-Enabled Grid Stability
- AI in Safety Instrumented Systems (SIS)
- Applying Functional Safety to AI-Controlled Drones
- Safety Assurance for AI in Prosthetics and Exoskeletons
- AI in Nuclear Plant Monitoring and Control
- AI for Collision Avoidance in Mixed Traffic Environments
- Functional Safety in AI-Powered Cybersecurity Systems
- Adapting Frameworks for Domain-Specific Regulators
Module 12: Organisational Leadership and Safety Governance - Establishing AI Functional Safety Governance Frameworks
- Roles and Responsibilities in AI Safety Teams
- Training Cross-Functional Engineers in AI Safety
- Developing Internal AI Safety Policies and Standards
- Creating a Safety-First AI Development Culture
- Managing Conflicts Between Innovation and Compliance
- Conducting Internal AI Safety Audits
- Reporting AI Safety Metrics to Executive Leadership
- Integrating AI Safety into Enterprise Risk Management
- Engaging with Regulators on AI Safety Proposals
- Managing Third-Party AI Vendors and Supply Chain Risks
- Intellectual Property and AI Safety Documentation
- Budgeting for AI Safety Assurance Activities
- Building a Centre of Excellence for AI Safety
- Succession Planning for AI Safety Expertise
Module 13: Advanced Topics in AI and System Safety Engineering - Formal Verification of Neural Networks Using SMT Solvers
- Reachability Analysis for Deep Learning Models
- Interval Bound Propagation for Safety Guarantees
- Using Abstract Interpretation in AI Safety
- Provable Robustness Against Adversarial Perturbations
- Conformance to IEEE P2851 Draft Standard
- Integrating Causal Reasoning into AI Safety Models
- Modelling System-of-Systems AI Interactions
- Dynamic Risk Assessment in Multi-Agent AI Environments
- Using Digital Twins for Whole-System Safety Simulation
- Resilient AI for Cyber-Physical Systems
- Safety Implications of Federated Learning Architectures
- Differential Privacy and Safety in AI Training
- Secure Aggregation in Distributed AI without Risk
- Future-Proofing AI Safety for Quantum-Enhanced Models
Module 14: Practical Application and Case Studies - Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
- Developing AI Verification Plans Aligned to ISO Standards
- Test Strategy for Non-Deterministic AI Systems
- Defining Acceptance Criteria for AI Outputs
- Designing Test Cases for AI Edge Behaviours
- Using Synthetic Data Generation for Risk Coverage
- Simulation-Based Testing in Digital Twin Environments
- Grey-Box Testing of AI Components
- Formal Methods for AI Component Verification
- Symbolic Execution for Neural Networks
- Invariant-Based Validation of AI Behaviour
- Monitoring for Distributional Shift and Concept Drift
- Runtime Verification of AI Decision Boundaries
- Statistical Confidence in AI Performance Metrics
- Using Bootstrap Methods for Metric Uncertainty
- Documenting V&V Processes for Certification Bodies
Module 7: Achieving Explainability and Transparency in AI - Why Explainability Is a Functional Safety Requirement
- Global Regulatory Demands for AI Transparency
- Post-Hoc vs. Intrinsic Explainability Methods
- Using SHAP and LIME for Feature Attribution
- Visualising Attention Mechanisms in Deep Learning Models
- Generating Natural Language Justifications for AI Decisions
- Designing Human-Understandable Safety Explanations
- Creating Audit-Ready Explanation Reports
- Building Trust with Stakeholders Through Transparency
- Limitations of Current XAI Methods in Critical Systems
- Incorporating Explainability into Real-Time Monitoring
- Designing Interactive Explanation Interfaces
- Quantifying Explanation Fidelity and Reliability
- Using Counterfactual Explanations for Safety Debugging
- Regulatory Alignment of Explainability Documentation
Module 8: Managing AI Model Lifecycle and Updates - Safety Implications of Model Retraining and Updates
- Defining Model Revalidation Triggers
- Impact Analysis for AI Model Changes
- Version Control Strategies for AI Models
- Change Management Processes for AI Components
- Ensuring Backward Compatibility in AI Systems
- Automated Regression Testing for AI Updates
- Monitoring Model Performance in Production
- Handling Concept Drift with Automated Alerts
- Implementing Safe Rollback Mechanisms
- Documentation Requirements for Model Updates
- Establishing AI Model Deprecation Protocols
- Integrating AI Model Management into Configuration Control
- Creating AI Software Bills of Materials (SBOM)
- Security and Integrity Checks During AI Updates
Module 9: AI in Real-Time and Embedded Safety Systems - Timing Constraints for AI in Real-Time Control Loops
- Latency Budgeting for AI Inference Engines
- Resource Management: CPU, Memory, and Power in Embedded AI
- Optimising Neural Networks for Edge Deployment
- Quantisation and Pruning for Safety Without Performance Loss
- Using ONNX and Other Interchange Formats for Safety Targets
- Implementing AI on Safety-Certified Hardware
- Co-Designing AI Models with Real-Time Operating Systems
- Interrupt Handling and Priority Management for AI Tasks
- Ensuring Deterministic AI Behaviour Where Needed
- Measuring AI Inference Variability Under Load
- Designing for Graceful Degradation Under Stress
- Safe Integration with Legacy Safety Systems
- Thermal and Environmental Robustness in Embedded AI
- Power-Fail Safety Mechanisms for AI Modules
Module 10: AI Safety in Autonomous and Adaptive Systems - Challenges of Autonomy in Safety-Critical Domains
- Functional Safety for Reinforcement Learning Agents
- Handling Unseen Scenarios in Self-Learning Systems
- Safe Exploration Strategies in Adaptive AI
- Dynamic Replanning and Its Safety Implications
- Formal Specification of Safe Adaptive Behaviours
- Monitoring AI for Deviations from Safe Policies
- Using Digital Twins for Safe Adaptation Testing
- Human Override and Intervention Protocols
- Designing for Seamless Handover Between AI and Humans
- Safety Cases for Continuously Learning AI
- Addressing Moral and Ethical Dilemmas in AI Decision Logic
- Compliance with UN Regulation No. 157 and Equivalent Standards
- Verification of Autonomous Safety Assurance Systems
- Creating Digital Logbooks for Autonomous System History
Module 11: Cross-Domain Applications of AI-Driven Safety - AI in Automotive Functional Safety (ISO 26262)
- Medical AI and IEC 62304 Compliance
- Industrial Robotics and IEC 61508 Integration
- Aerospace and DO-178C for AI Components
- Railway Applications Under EN 50128 and EN 50657
- Oil & Gas: Managing AI in High-Consequence Environments
- AI for Predictive Maintenance with Safety Implications
- Smart Grids and AI-Enabled Grid Stability
- AI in Safety Instrumented Systems (SIS)
- Applying Functional Safety to AI-Controlled Drones
- Safety Assurance for AI in Prosthetics and Exoskeletons
- AI in Nuclear Plant Monitoring and Control
- AI for Collision Avoidance in Mixed Traffic Environments
- Functional Safety in AI-Powered Cybersecurity Systems
- Adapting Frameworks for Domain-Specific Regulators
Module 12: Organisational Leadership and Safety Governance - Establishing AI Functional Safety Governance Frameworks
- Roles and Responsibilities in AI Safety Teams
- Training Cross-Functional Engineers in AI Safety
- Developing Internal AI Safety Policies and Standards
- Creating a Safety-First AI Development Culture
- Managing Conflicts Between Innovation and Compliance
- Conducting Internal AI Safety Audits
- Reporting AI Safety Metrics to Executive Leadership
- Integrating AI Safety into Enterprise Risk Management
- Engaging with Regulators on AI Safety Proposals
- Managing Third-Party AI Vendors and Supply Chain Risks
- Intellectual Property and AI Safety Documentation
- Budgeting for AI Safety Assurance Activities
- Building a Centre of Excellence for AI Safety
- Succession Planning for AI Safety Expertise
Module 13: Advanced Topics in AI and System Safety Engineering - Formal Verification of Neural Networks Using SMT Solvers
- Reachability Analysis for Deep Learning Models
- Interval Bound Propagation for Safety Guarantees
- Using Abstract Interpretation in AI Safety
- Provable Robustness Against Adversarial Perturbations
- Conformance to IEEE P2851 Draft Standard
- Integrating Causal Reasoning into AI Safety Models
- Modelling System-of-Systems AI Interactions
- Dynamic Risk Assessment in Multi-Agent AI Environments
- Using Digital Twins for Whole-System Safety Simulation
- Resilient AI for Cyber-Physical Systems
- Safety Implications of Federated Learning Architectures
- Differential Privacy and Safety in AI Training
- Secure Aggregation in Distributed AI without Risk
- Future-Proofing AI Safety for Quantum-Enhanced Models
Module 14: Practical Application and Case Studies - Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
- Safety Implications of Model Retraining and Updates
- Defining Model Revalidation Triggers
- Impact Analysis for AI Model Changes
- Version Control Strategies for AI Models
- Change Management Processes for AI Components
- Ensuring Backward Compatibility in AI Systems
- Automated Regression Testing for AI Updates
- Monitoring Model Performance in Production
- Handling Concept Drift with Automated Alerts
- Implementing Safe Rollback Mechanisms
- Documentation Requirements for Model Updates
- Establishing AI Model Deprecation Protocols
- Integrating AI Model Management into Configuration Control
- Creating AI Software Bills of Materials (SBOM)
- Security and Integrity Checks During AI Updates
Module 9: AI in Real-Time and Embedded Safety Systems - Timing Constraints for AI in Real-Time Control Loops
- Latency Budgeting for AI Inference Engines
- Resource Management: CPU, Memory, and Power in Embedded AI
- Optimising Neural Networks for Edge Deployment
- Quantisation and Pruning for Safety Without Performance Loss
- Using ONNX and Other Interchange Formats for Safety Targets
- Implementing AI on Safety-Certified Hardware
- Co-Designing AI Models with Real-Time Operating Systems
- Interrupt Handling and Priority Management for AI Tasks
- Ensuring Deterministic AI Behaviour Where Needed
- Measuring AI Inference Variability Under Load
- Designing for Graceful Degradation Under Stress
- Safe Integration with Legacy Safety Systems
- Thermal and Environmental Robustness in Embedded AI
- Power-Fail Safety Mechanisms for AI Modules
Module 10: AI Safety in Autonomous and Adaptive Systems - Challenges of Autonomy in Safety-Critical Domains
- Functional Safety for Reinforcement Learning Agents
- Handling Unseen Scenarios in Self-Learning Systems
- Safe Exploration Strategies in Adaptive AI
- Dynamic Replanning and Its Safety Implications
- Formal Specification of Safe Adaptive Behaviours
- Monitoring AI for Deviations from Safe Policies
- Using Digital Twins for Safe Adaptation Testing
- Human Override and Intervention Protocols
- Designing for Seamless Handover Between AI and Humans
- Safety Cases for Continuously Learning AI
- Addressing Moral and Ethical Dilemmas in AI Decision Logic
- Compliance with UN Regulation No. 157 and Equivalent Standards
- Verification of Autonomous Safety Assurance Systems
- Creating Digital Logbooks for Autonomous System History
Module 11: Cross-Domain Applications of AI-Driven Safety - AI in Automotive Functional Safety (ISO 26262)
- Medical AI and IEC 62304 Compliance
- Industrial Robotics and IEC 61508 Integration
- Aerospace and DO-178C for AI Components
- Railway Applications Under EN 50128 and EN 50657
- Oil & Gas: Managing AI in High-Consequence Environments
- AI for Predictive Maintenance with Safety Implications
- Smart Grids and AI-Enabled Grid Stability
- AI in Safety Instrumented Systems (SIS)
- Applying Functional Safety to AI-Controlled Drones
- Safety Assurance for AI in Prosthetics and Exoskeletons
- AI in Nuclear Plant Monitoring and Control
- AI for Collision Avoidance in Mixed Traffic Environments
- Functional Safety in AI-Powered Cybersecurity Systems
- Adapting Frameworks for Domain-Specific Regulators
Module 12: Organisational Leadership and Safety Governance - Establishing AI Functional Safety Governance Frameworks
- Roles and Responsibilities in AI Safety Teams
- Training Cross-Functional Engineers in AI Safety
- Developing Internal AI Safety Policies and Standards
- Creating a Safety-First AI Development Culture
- Managing Conflicts Between Innovation and Compliance
- Conducting Internal AI Safety Audits
- Reporting AI Safety Metrics to Executive Leadership
- Integrating AI Safety into Enterprise Risk Management
- Engaging with Regulators on AI Safety Proposals
- Managing Third-Party AI Vendors and Supply Chain Risks
- Intellectual Property and AI Safety Documentation
- Budgeting for AI Safety Assurance Activities
- Building a Centre of Excellence for AI Safety
- Succession Planning for AI Safety Expertise
Module 13: Advanced Topics in AI and System Safety Engineering - Formal Verification of Neural Networks Using SMT Solvers
- Reachability Analysis for Deep Learning Models
- Interval Bound Propagation for Safety Guarantees
- Using Abstract Interpretation in AI Safety
- Provable Robustness Against Adversarial Perturbations
- Conformance to IEEE P2851 Draft Standard
- Integrating Causal Reasoning into AI Safety Models
- Modelling System-of-Systems AI Interactions
- Dynamic Risk Assessment in Multi-Agent AI Environments
- Using Digital Twins for Whole-System Safety Simulation
- Resilient AI for Cyber-Physical Systems
- Safety Implications of Federated Learning Architectures
- Differential Privacy and Safety in AI Training
- Secure Aggregation in Distributed AI without Risk
- Future-Proofing AI Safety for Quantum-Enhanced Models
Module 14: Practical Application and Case Studies - Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
- Challenges of Autonomy in Safety-Critical Domains
- Functional Safety for Reinforcement Learning Agents
- Handling Unseen Scenarios in Self-Learning Systems
- Safe Exploration Strategies in Adaptive AI
- Dynamic Replanning and Its Safety Implications
- Formal Specification of Safe Adaptive Behaviours
- Monitoring AI for Deviations from Safe Policies
- Using Digital Twins for Safe Adaptation Testing
- Human Override and Intervention Protocols
- Designing for Seamless Handover Between AI and Humans
- Safety Cases for Continuously Learning AI
- Addressing Moral and Ethical Dilemmas in AI Decision Logic
- Compliance with UN Regulation No. 157 and Equivalent Standards
- Verification of Autonomous Safety Assurance Systems
- Creating Digital Logbooks for Autonomous System History
Module 11: Cross-Domain Applications of AI-Driven Safety - AI in Automotive Functional Safety (ISO 26262)
- Medical AI and IEC 62304 Compliance
- Industrial Robotics and IEC 61508 Integration
- Aerospace and DO-178C for AI Components
- Railway Applications Under EN 50128 and EN 50657
- Oil & Gas: Managing AI in High-Consequence Environments
- AI for Predictive Maintenance with Safety Implications
- Smart Grids and AI-Enabled Grid Stability
- AI in Safety Instrumented Systems (SIS)
- Applying Functional Safety to AI-Controlled Drones
- Safety Assurance for AI in Prosthetics and Exoskeletons
- AI in Nuclear Plant Monitoring and Control
- AI for Collision Avoidance in Mixed Traffic Environments
- Functional Safety in AI-Powered Cybersecurity Systems
- Adapting Frameworks for Domain-Specific Regulators
Module 12: Organisational Leadership and Safety Governance - Establishing AI Functional Safety Governance Frameworks
- Roles and Responsibilities in AI Safety Teams
- Training Cross-Functional Engineers in AI Safety
- Developing Internal AI Safety Policies and Standards
- Creating a Safety-First AI Development Culture
- Managing Conflicts Between Innovation and Compliance
- Conducting Internal AI Safety Audits
- Reporting AI Safety Metrics to Executive Leadership
- Integrating AI Safety into Enterprise Risk Management
- Engaging with Regulators on AI Safety Proposals
- Managing Third-Party AI Vendors and Supply Chain Risks
- Intellectual Property and AI Safety Documentation
- Budgeting for AI Safety Assurance Activities
- Building a Centre of Excellence for AI Safety
- Succession Planning for AI Safety Expertise
Module 13: Advanced Topics in AI and System Safety Engineering - Formal Verification of Neural Networks Using SMT Solvers
- Reachability Analysis for Deep Learning Models
- Interval Bound Propagation for Safety Guarantees
- Using Abstract Interpretation in AI Safety
- Provable Robustness Against Adversarial Perturbations
- Conformance to IEEE P2851 Draft Standard
- Integrating Causal Reasoning into AI Safety Models
- Modelling System-of-Systems AI Interactions
- Dynamic Risk Assessment in Multi-Agent AI Environments
- Using Digital Twins for Whole-System Safety Simulation
- Resilient AI for Cyber-Physical Systems
- Safety Implications of Federated Learning Architectures
- Differential Privacy and Safety in AI Training
- Secure Aggregation in Distributed AI without Risk
- Future-Proofing AI Safety for Quantum-Enhanced Models
Module 14: Practical Application and Case Studies - Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
- Establishing AI Functional Safety Governance Frameworks
- Roles and Responsibilities in AI Safety Teams
- Training Cross-Functional Engineers in AI Safety
- Developing Internal AI Safety Policies and Standards
- Creating a Safety-First AI Development Culture
- Managing Conflicts Between Innovation and Compliance
- Conducting Internal AI Safety Audits
- Reporting AI Safety Metrics to Executive Leadership
- Integrating AI Safety into Enterprise Risk Management
- Engaging with Regulators on AI Safety Proposals
- Managing Third-Party AI Vendors and Supply Chain Risks
- Intellectual Property and AI Safety Documentation
- Budgeting for AI Safety Assurance Activities
- Building a Centre of Excellence for AI Safety
- Succession Planning for AI Safety Expertise
Module 13: Advanced Topics in AI and System Safety Engineering - Formal Verification of Neural Networks Using SMT Solvers
- Reachability Analysis for Deep Learning Models
- Interval Bound Propagation for Safety Guarantees
- Using Abstract Interpretation in AI Safety
- Provable Robustness Against Adversarial Perturbations
- Conformance to IEEE P2851 Draft Standard
- Integrating Causal Reasoning into AI Safety Models
- Modelling System-of-Systems AI Interactions
- Dynamic Risk Assessment in Multi-Agent AI Environments
- Using Digital Twins for Whole-System Safety Simulation
- Resilient AI for Cyber-Physical Systems
- Safety Implications of Federated Learning Architectures
- Differential Privacy and Safety in AI Training
- Secure Aggregation in Distributed AI without Risk
- Future-Proofing AI Safety for Quantum-Enhanced Models
Module 14: Practical Application and Case Studies - Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
- Case Study: AI in Autonomous Emergency Braking (AEB)
- Case Study: AI for Anomaly Detection in Medical Imaging
- Case Study: Predictive Shutdown Systems in Industrial Plants
- Case Study: AI for Aircraft Taxi Assistance Systems
- Analysing the Tesla Autopilot Safety Reports
- Review of Waymo's Safety Concept Documentation
- Lessons from Boeing 737 MAX and AI Relevance
- How Toyota’s Guardian Approach Balances AI and Safety
- Validating AI in Surgical Robots: The da Vinci Example
- AI in Railway Signalling: The London Underground Case
- Safety Validation of AI in Drone Delivery Fleets
- Using AI for Dynamic Fire Evacuation Routing
- Assessing AI Safety in Financial Trading Algorithms
- Modelling Ethical Decisions in Autonomous Vehicles
- Workshop: Building a Complete AI Safety Package from Start to Finish
Module 15: Implementation, Integration, and Certification Readiness - Preparing for External Safety Audits and Certification
- Finalising Safety Case Documentation for AI Systems
- Compiling Evidence for AI Component Certification
- Engaging with Notified Bodies and Certification Agencies
- Responding to Auditors’ Questions About AI Behaviour
- Using Template Packs for ISO 26262 and IEC 61508 Compliance
- Integrating AI Safety Outputs into Full System Certification
- Demonstrating Due Diligence in AI Development
- Packaging AI Safety Arguments for Review Boards
- Creating Executable Safety Demonstrations
- Conducting Final Gap Analysis Before Submission
- Simulation-Based Certification Evidence Packages
- Leveraging Historical Field Data to Support AI Safety
- Preparing for Post-Certification Surveillance
- Continuous Improvement After Certification
Module 16: Certification, Career Advancement, and Next Steps - How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process
- How to Leverage Your Certificate of Completion
- Certification Guidelines from The Art of Service
- Verifying and Sharing Your Credential Online
- Updating Resumes and LinkedIn for Maximum Impact
- Using the Certificate in Promotion Discussions
- Networking with Other Certified Practitioners
- Accessing Alumni Resources and Updates
- Joining the AI Functional Safety Practitioners Network
- Continuing Education Pathways and Advanced Courses
- Contributing to Industry Standards Development
- Presenting Your Work at Engineering Conferences
- Mentoring Others in AI Safety Best Practices
- Leading Internal AI Safety Transformation Initiatives
- Developing Your Own AI Safety Framework
- Final Assessment and Certificate Award Process