Mastering AI-Driven Safety Engineering for Autonomous Systems
You're under pressure. Deadlines are tightening, safety gaps are widening, and stakeholders are demanding proof that your autonomous systems won't fail under real-world conditions. You know AI is the future, but integrating it safely? That’s where most teams stall, misjudge risks, or launch with blind spots that could cost millions-or lives. The field is moving fast. Regulators are catching up. Investors are demanding safety-first frameworks. If you’re not leading the charge on AI-driven safety, you’re falling behind. But right now, you might feel stuck between outdated methodologies and the hype of unproven tools that promise safety without delivering traceable, auditable engineering rigor. Mastering AI-Driven Safety Engineering for Autonomous Systems is your roadmap from uncertainty to authority. It equips you with a battle-tested, systematised approach to designing, validating, and certifying AI-based safety controllers in autonomous vehicles, drones, industrial robots, and smart infrastructure-so you can deliver systems that are not just intelligent, but provably safe. This course enables you to go from concept to a fully documented, board-ready safety case in under 30 days-complete with hazard taxonomies, failure mode mitigation strategies, and assurance arguments that satisfy both technical and regulatory scrutiny. Just ask Maria Tran, Lead Safety Engineer at a Tier 1 autonomous mobility startup: “After applying the framework from this course, we passed our ISO 21448 and SOTIF audit on the first submission. Our CTO said it was the most coherent safety architecture we’ve ever produced. We’re now using it as the template across all new projects.” No more guesswork. No more last-minute scrambles. This is the structured methodology elite engineering teams use to secure funding, pass audits, and deploy faster-because they prove safety, not assume it. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-paced. Immediate access. Fully on-demand. Begin the moment you're ready-no fixed start dates, no scheduling conflicts. This course is designed for high-performing engineers and technical leads who need flexibility without compromise. Designed for Real-World Integration
Most professionals complete the core curriculum in 4 to 6 weeks by investing 6 to 8 hours per week. However, many report applying key frameworks to active projects within the first 72 hours-generating immediate ROI through redesigned hazard analyses, improved safety arguments, and faster regulatory alignment. - Lifetime access to all materials, including future updates-at no additional cost
- Access from any device, anytime, anywhere-fully mobile-friendly and optimised for on-the-go learning
- Available 24/7 across all global time zones
Clarity, Support, and Continuous Guidance
Even in a self-paced format, you’re never alone. You receive direct, thoughtful feedback from certified safety engineering practitioners during key project milestones. Instructor support is embedded into the assignment review process, ensuring you develop work that meets industry-grade standards-not just theoretical ideals. You’ll also gain access to a curated network of peer reviewers and domain specialists through exclusive community channels, enabling cross-industry knowledge exchange and peer validation of your safety architectures. Trust, Credibility, and Career Recognition
Upon successful completion, you’ll earn a Certificate of Completion issued by The Art of Service-a globally recognised credential trusted by engineering teams in aerospace, automotive, robotics, and industrial automation sectors. This is not a participation badge. Your certificate confirms mastery of AI-driven hazard analysis, functional safety integration, and assurance case development-validated through hands-on projects that mirror real industry deliverables. - No hidden fees. No subscription traps. One simple payment covers everything.
- Secure checkout accepts Visa, Mastercard, and PayPal.
- Backed by a 30-day 100% money-back guarantee: if the course doesn’t meet your expectations, you’ll be refunded-no questions asked.
After enrollment, you’ll receive a confirmation email. Once your course materials are prepared, your access details will be sent separately, ensuring a smooth onboarding experience. “Will This Work For Me?” - Objection Handled
Yes-even if you're new to AI integration in safety-critical systems. Even if your current tools lack traceability. Even if you’ve been burned by “smart” solutions that failed real-world validation. This system works even if: - You’re not a machine learning expert-but need to ensure ML components behave safely
- You work in aerospace, automotive, medical robotics, or smart infrastructure and face strict regulatory scrutiny
- Your team lacks a unified safety language between AI developers and functional safety engineers
- You’ve tried ISO 26262 or IEC 61508 approaches but found them insufficient for AI uncertainty
Graduates include autonomous vehicle safety architects, firmware engineers transitioning into safety roles, and systems engineers in defence contractors who’ve used this methodology to de-risk AI adoption at scale. This is risk-reversed learning. You invest in proven methodology, not hype. You get clarity, not confusion. And you finish with assets you can immediately use to strengthen proposals, pass audits, and lead high-stakes projects with confidence.
Module 1: Foundations of AI-Driven Safety in Autonomous Systems - Defining Safety in the Context of AI and Autonomy
- Key Differences Between Traditional and AI-Driven Safety Engineering
- Understanding Emergent Behaviours in Autonomous Systems
- The Role of Uncertainty, Non-Determinism, and Stochasticity
- High-Level Architecture Patterns for Safety-Critical AI
- Mapping AI Capabilities to Safety Functions
- Introduction to Safety Assurance Cases and Their Structure
- Core Principles of Functional Safety Applied to AI
- Regulatory Drivers Across Automotive, Aerospace, and Industrial Sectors
- Integrating Safety Culture into AI Development Teams
Module 2: Hazard Identification and Risk Assessment Frameworks - AI-Specific Hazard Taxonomy Development
- Adapting HAZOP and FMEA for AI Systems
- Threat Modelling for Data-Driven Components
- System-Theoretic Process Analysis (STPA) for AI-Controlled Systems
- Defining Unsafe Control Actions Involving AI
- Incorporating Edge Cases and Long-Tail Scenarios
- Scenario-Based Risk Assessment for Operational Design Domains
- Data-Driven Hazard Discovery Using Anomaly Detection
- Safety Requirements Generation from Identified Hazards
- Establishing Safety Goals and Performance Targets
Module 3: AI Safety Requirements Engineering - Translating Hazards into Safety Requirements
- Handling Non-Functional Safety Requirements in AI Systems
- Specifying Accuracy, Robustness, and Confidence Thresholds
- Defining Fail-Safe and Graceful Degradation Behaviors
- Incorporating Temporal Constraints on AI Outputs
- Interface-Level Safety Constraints Between AI and Non-AI Components
- Latency, Drift, and Concept Shift as Safety Parameters
- Traceability Between Safety Goals and Requirements
- Tool-Assisted Requirements Management
- Versioning and Change Control for Evolving Safety Requirements
Module 4: Safety-Centric AI Development Lifecycle - Integrating Safety into the Machine Learning Development Pipeline
- Safe Data Curation and Labelling Practices
- Data Bias Detection and Mitigation Strategies
- Ensuring Data Representativeness for Operational Domains
- Training Stability and Convergence Monitoring
- Model Interpretability Techniques for Safety Justification
- Using Explainable AI (XAI) to Support Safety Arguments
- Latent Space Monitoring for Anomalous Learning
- Safety Gates at Each Stage of Model Development
- Handover Protocols from AI Teams to Safety Engineers
Module 5: AI Model Validation and Verification - Designing Test Suites for Probabilistic Outputs
- Formal Methods for Bounding AI Behavior
- Conformity Testing Against Known Input-Output Pairs
- Adversarial Testing and Robustness Evaluation
- Monte Carlo Simulation for Coverage Analysis
- Statistical Guarantees on Model Performance
- Measuring Calibration and Confidence Reliability
- Cross-Validation in Safety-Critical Contexts
- Validation of Ensemble Models and Voting Mechanisms
- Quantifying Uncertainty in Deep Neural Network Predictions
Module 6: Runtime Safety Mechanisms and Monitoring - Designing Redundant and Diverse AI Subsystems
- Runtime Health Monitoring of AI Components
- Input Validation and Sanitisation at Inference Time
- Out-of-Distribution Detection Algorithms
- Confidence Thresholding and Rejection Strategies
- Safety Supervisors and Watchdog Architectures
- Fail-Operational vs Fail-Safe Design Patterns
- Dynamic Reconfiguration Based on Safety State
- Real-Time Monitoring of Model Drift and Degradation
- Automated Logging and Diagnostics for Incident Investigation
Module 7: Safety Assurance and Certification Strategies - Building Articulated Safety Cases for AI Components
- Goal Structuring Notation (GSN) for AI Assurance
- Evidence Collection: From Testing to Formal Proofs
- Handling Partial or Probabilistic Evidence in Arguments
- Leveraging Tool Qualification to Strengthen Claims
- Integrating Safety Cases with ISO 26262 and ISO 21448 (SOTIF)
- Compliance Mapping for DO-178C, IEC 61508, and Other Standards
- Preparing for Regulatory Audits and Third-Party Certification
- Documentation Standards for AI Safety Artefacts
- Managing Safety Case Updates Across Model Versions
Module 8: Data-Centric Safety Engineering - Data Provenance and Chain of Custody
- Label Quality Assurance and Audit Trails
- Synthetic Data Generation with Safety Integrity
- Scenario Mining from Real-World Operational Data
- Data Versioning and Reproducibility
- Monitoring Data Pipeline Integrity
- Defining Data Expiry and Refresh Policies
- Geofencing Data Usage by Operational Domain
- Legal and Ethical Compliance in Data Handling
- Audit-Ready Data Governance Frameworks
Module 9: Model Monitoring and Continuous Safety Assurance - Designing Model Monitoring Dashboards
- Key Safety Metrics for Production Models
- Alerting on Safety Violations and Threshold Breaches
- Root Cause Analysis for AI Failures
- Incident Response Playbooks for AI Safety Events
- Feedback Loops from Field Data to Retraining
- Change Impact Analysis Before Model Updates
- Rollback and Version Recovery Procedures
- Safety Sign-Off Requirements for Deployments
- Periodic Safety Reassessment Cycles
Module 10: AI Safety in System Integration and Architecture - Co-Designing AI and Safety-Critical Subsystems
- Interface Safety Contracts for AI Components
- Timing and Synchronisation Constraints
- Memory and Compute Isolation Strategies
- Hardware-Accelerated Safety Monitoring
- Fault Tolerance in Distributed AI Systems
- Secure Boot and Trusted Execution Environments
- Network-Level Safety for Connected Autonomous Systems
- Integration with Functional Safety Managers (e.g. ASIL-D)
- Verifying End-to-End Safety Chains
Module 11: Advanced Topics in Autonomous System Safety - Safety of Reinforcement Learning Agents
- Ensuring Safe Exploration in Online Learning
- Safety in Multi-Agent Autonomous Systems
- Coordination Safety in Swarm Robotics
- Human-Machine Interaction Safety Protocols
- Safety Implications of Transfer Learning
- Zero-Shot and Few-Shot Learning Safety Risks
- Neural Architecture Search with Safety Constraints
- Physics-Informed Neural Networks for Safety-Critical Control
- Hybrid Symbolic-Neural Approaches for Verifiable AI
Module 12: Safety Case Development Project - Selecting a Real-World Autonomous System for Analysis
- Defining Operational Design Domain (ODD)
- Developing System Boundary and Context Diagram
- Conducting Full Hazard Analysis Using STPA and FMEA
- Generating Safety Requirements with Full Traceability
- Designing AI Model with Embedded Safety Controls
- Creating Validation Plan with Coverage Metrics
- Developing Runtime Monitoring Strategy
- Building Complete GSN-Based Safety Case
- Final Review and Peer Validation of Safety Architecture
Module 13: Industry Applications and Cross-Domain Adaptation - Autonomous Vehicles: Integration with ADAS and SAE Levels
- Unmanned Aerial Vehicles (UAVs) and Beyond Visual Line of Sight (BVLOS)
- Autonomous Industrial Machinery and Robotics
- Safety in Medical AI and Surgical Robots
- Rail and Public Transportation Automation
- Smart City Infrastructure and Traffic Management
- Maritime Autonomous Surface Ships (MASS)
- Agricultural and Mining Automation Systems
- Safety Engineering for Humanoid and Collaborative Robots
- Tailoring Frameworks to Domain-Specific Regulations
Module 14: Tools, Templates, and Accelerators - Industry-Standard Tools for Safety Case Modelling
- Open Source Frameworks for AI Safety Testing
- Template Libraries for Hazard Registers
- GSN Diagram Templates for Common AI Use Cases
- Checklists for Regulatory Compliance Readiness
- Model Documentation Templates (AI Factsheets)
- Issue Tracking Integration for Safety Defects
- Automated Traceability Tools
- Dashboard Templates for Production Monitoring
- Project Kickoff and Review Meeting Agendas
Module 15: Certification, Career Advancement, and Next Steps - Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service
- Defining Safety in the Context of AI and Autonomy
- Key Differences Between Traditional and AI-Driven Safety Engineering
- Understanding Emergent Behaviours in Autonomous Systems
- The Role of Uncertainty, Non-Determinism, and Stochasticity
- High-Level Architecture Patterns for Safety-Critical AI
- Mapping AI Capabilities to Safety Functions
- Introduction to Safety Assurance Cases and Their Structure
- Core Principles of Functional Safety Applied to AI
- Regulatory Drivers Across Automotive, Aerospace, and Industrial Sectors
- Integrating Safety Culture into AI Development Teams
Module 2: Hazard Identification and Risk Assessment Frameworks - AI-Specific Hazard Taxonomy Development
- Adapting HAZOP and FMEA for AI Systems
- Threat Modelling for Data-Driven Components
- System-Theoretic Process Analysis (STPA) for AI-Controlled Systems
- Defining Unsafe Control Actions Involving AI
- Incorporating Edge Cases and Long-Tail Scenarios
- Scenario-Based Risk Assessment for Operational Design Domains
- Data-Driven Hazard Discovery Using Anomaly Detection
- Safety Requirements Generation from Identified Hazards
- Establishing Safety Goals and Performance Targets
Module 3: AI Safety Requirements Engineering - Translating Hazards into Safety Requirements
- Handling Non-Functional Safety Requirements in AI Systems
- Specifying Accuracy, Robustness, and Confidence Thresholds
- Defining Fail-Safe and Graceful Degradation Behaviors
- Incorporating Temporal Constraints on AI Outputs
- Interface-Level Safety Constraints Between AI and Non-AI Components
- Latency, Drift, and Concept Shift as Safety Parameters
- Traceability Between Safety Goals and Requirements
- Tool-Assisted Requirements Management
- Versioning and Change Control for Evolving Safety Requirements
Module 4: Safety-Centric AI Development Lifecycle - Integrating Safety into the Machine Learning Development Pipeline
- Safe Data Curation and Labelling Practices
- Data Bias Detection and Mitigation Strategies
- Ensuring Data Representativeness for Operational Domains
- Training Stability and Convergence Monitoring
- Model Interpretability Techniques for Safety Justification
- Using Explainable AI (XAI) to Support Safety Arguments
- Latent Space Monitoring for Anomalous Learning
- Safety Gates at Each Stage of Model Development
- Handover Protocols from AI Teams to Safety Engineers
Module 5: AI Model Validation and Verification - Designing Test Suites for Probabilistic Outputs
- Formal Methods for Bounding AI Behavior
- Conformity Testing Against Known Input-Output Pairs
- Adversarial Testing and Robustness Evaluation
- Monte Carlo Simulation for Coverage Analysis
- Statistical Guarantees on Model Performance
- Measuring Calibration and Confidence Reliability
- Cross-Validation in Safety-Critical Contexts
- Validation of Ensemble Models and Voting Mechanisms
- Quantifying Uncertainty in Deep Neural Network Predictions
Module 6: Runtime Safety Mechanisms and Monitoring - Designing Redundant and Diverse AI Subsystems
- Runtime Health Monitoring of AI Components
- Input Validation and Sanitisation at Inference Time
- Out-of-Distribution Detection Algorithms
- Confidence Thresholding and Rejection Strategies
- Safety Supervisors and Watchdog Architectures
- Fail-Operational vs Fail-Safe Design Patterns
- Dynamic Reconfiguration Based on Safety State
- Real-Time Monitoring of Model Drift and Degradation
- Automated Logging and Diagnostics for Incident Investigation
Module 7: Safety Assurance and Certification Strategies - Building Articulated Safety Cases for AI Components
- Goal Structuring Notation (GSN) for AI Assurance
- Evidence Collection: From Testing to Formal Proofs
- Handling Partial or Probabilistic Evidence in Arguments
- Leveraging Tool Qualification to Strengthen Claims
- Integrating Safety Cases with ISO 26262 and ISO 21448 (SOTIF)
- Compliance Mapping for DO-178C, IEC 61508, and Other Standards
- Preparing for Regulatory Audits and Third-Party Certification
- Documentation Standards for AI Safety Artefacts
- Managing Safety Case Updates Across Model Versions
Module 8: Data-Centric Safety Engineering - Data Provenance and Chain of Custody
- Label Quality Assurance and Audit Trails
- Synthetic Data Generation with Safety Integrity
- Scenario Mining from Real-World Operational Data
- Data Versioning and Reproducibility
- Monitoring Data Pipeline Integrity
- Defining Data Expiry and Refresh Policies
- Geofencing Data Usage by Operational Domain
- Legal and Ethical Compliance in Data Handling
- Audit-Ready Data Governance Frameworks
Module 9: Model Monitoring and Continuous Safety Assurance - Designing Model Monitoring Dashboards
- Key Safety Metrics for Production Models
- Alerting on Safety Violations and Threshold Breaches
- Root Cause Analysis for AI Failures
- Incident Response Playbooks for AI Safety Events
- Feedback Loops from Field Data to Retraining
- Change Impact Analysis Before Model Updates
- Rollback and Version Recovery Procedures
- Safety Sign-Off Requirements for Deployments
- Periodic Safety Reassessment Cycles
Module 10: AI Safety in System Integration and Architecture - Co-Designing AI and Safety-Critical Subsystems
- Interface Safety Contracts for AI Components
- Timing and Synchronisation Constraints
- Memory and Compute Isolation Strategies
- Hardware-Accelerated Safety Monitoring
- Fault Tolerance in Distributed AI Systems
- Secure Boot and Trusted Execution Environments
- Network-Level Safety for Connected Autonomous Systems
- Integration with Functional Safety Managers (e.g. ASIL-D)
- Verifying End-to-End Safety Chains
Module 11: Advanced Topics in Autonomous System Safety - Safety of Reinforcement Learning Agents
- Ensuring Safe Exploration in Online Learning
- Safety in Multi-Agent Autonomous Systems
- Coordination Safety in Swarm Robotics
- Human-Machine Interaction Safety Protocols
- Safety Implications of Transfer Learning
- Zero-Shot and Few-Shot Learning Safety Risks
- Neural Architecture Search with Safety Constraints
- Physics-Informed Neural Networks for Safety-Critical Control
- Hybrid Symbolic-Neural Approaches for Verifiable AI
Module 12: Safety Case Development Project - Selecting a Real-World Autonomous System for Analysis
- Defining Operational Design Domain (ODD)
- Developing System Boundary and Context Diagram
- Conducting Full Hazard Analysis Using STPA and FMEA
- Generating Safety Requirements with Full Traceability
- Designing AI Model with Embedded Safety Controls
- Creating Validation Plan with Coverage Metrics
- Developing Runtime Monitoring Strategy
- Building Complete GSN-Based Safety Case
- Final Review and Peer Validation of Safety Architecture
Module 13: Industry Applications and Cross-Domain Adaptation - Autonomous Vehicles: Integration with ADAS and SAE Levels
- Unmanned Aerial Vehicles (UAVs) and Beyond Visual Line of Sight (BVLOS)
- Autonomous Industrial Machinery and Robotics
- Safety in Medical AI and Surgical Robots
- Rail and Public Transportation Automation
- Smart City Infrastructure and Traffic Management
- Maritime Autonomous Surface Ships (MASS)
- Agricultural and Mining Automation Systems
- Safety Engineering for Humanoid and Collaborative Robots
- Tailoring Frameworks to Domain-Specific Regulations
Module 14: Tools, Templates, and Accelerators - Industry-Standard Tools for Safety Case Modelling
- Open Source Frameworks for AI Safety Testing
- Template Libraries for Hazard Registers
- GSN Diagram Templates for Common AI Use Cases
- Checklists for Regulatory Compliance Readiness
- Model Documentation Templates (AI Factsheets)
- Issue Tracking Integration for Safety Defects
- Automated Traceability Tools
- Dashboard Templates for Production Monitoring
- Project Kickoff and Review Meeting Agendas
Module 15: Certification, Career Advancement, and Next Steps - Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service
- Translating Hazards into Safety Requirements
- Handling Non-Functional Safety Requirements in AI Systems
- Specifying Accuracy, Robustness, and Confidence Thresholds
- Defining Fail-Safe and Graceful Degradation Behaviors
- Incorporating Temporal Constraints on AI Outputs
- Interface-Level Safety Constraints Between AI and Non-AI Components
- Latency, Drift, and Concept Shift as Safety Parameters
- Traceability Between Safety Goals and Requirements
- Tool-Assisted Requirements Management
- Versioning and Change Control for Evolving Safety Requirements
Module 4: Safety-Centric AI Development Lifecycle - Integrating Safety into the Machine Learning Development Pipeline
- Safe Data Curation and Labelling Practices
- Data Bias Detection and Mitigation Strategies
- Ensuring Data Representativeness for Operational Domains
- Training Stability and Convergence Monitoring
- Model Interpretability Techniques for Safety Justification
- Using Explainable AI (XAI) to Support Safety Arguments
- Latent Space Monitoring for Anomalous Learning
- Safety Gates at Each Stage of Model Development
- Handover Protocols from AI Teams to Safety Engineers
Module 5: AI Model Validation and Verification - Designing Test Suites for Probabilistic Outputs
- Formal Methods for Bounding AI Behavior
- Conformity Testing Against Known Input-Output Pairs
- Adversarial Testing and Robustness Evaluation
- Monte Carlo Simulation for Coverage Analysis
- Statistical Guarantees on Model Performance
- Measuring Calibration and Confidence Reliability
- Cross-Validation in Safety-Critical Contexts
- Validation of Ensemble Models and Voting Mechanisms
- Quantifying Uncertainty in Deep Neural Network Predictions
Module 6: Runtime Safety Mechanisms and Monitoring - Designing Redundant and Diverse AI Subsystems
- Runtime Health Monitoring of AI Components
- Input Validation and Sanitisation at Inference Time
- Out-of-Distribution Detection Algorithms
- Confidence Thresholding and Rejection Strategies
- Safety Supervisors and Watchdog Architectures
- Fail-Operational vs Fail-Safe Design Patterns
- Dynamic Reconfiguration Based on Safety State
- Real-Time Monitoring of Model Drift and Degradation
- Automated Logging and Diagnostics for Incident Investigation
Module 7: Safety Assurance and Certification Strategies - Building Articulated Safety Cases for AI Components
- Goal Structuring Notation (GSN) for AI Assurance
- Evidence Collection: From Testing to Formal Proofs
- Handling Partial or Probabilistic Evidence in Arguments
- Leveraging Tool Qualification to Strengthen Claims
- Integrating Safety Cases with ISO 26262 and ISO 21448 (SOTIF)
- Compliance Mapping for DO-178C, IEC 61508, and Other Standards
- Preparing for Regulatory Audits and Third-Party Certification
- Documentation Standards for AI Safety Artefacts
- Managing Safety Case Updates Across Model Versions
Module 8: Data-Centric Safety Engineering - Data Provenance and Chain of Custody
- Label Quality Assurance and Audit Trails
- Synthetic Data Generation with Safety Integrity
- Scenario Mining from Real-World Operational Data
- Data Versioning and Reproducibility
- Monitoring Data Pipeline Integrity
- Defining Data Expiry and Refresh Policies
- Geofencing Data Usage by Operational Domain
- Legal and Ethical Compliance in Data Handling
- Audit-Ready Data Governance Frameworks
Module 9: Model Monitoring and Continuous Safety Assurance - Designing Model Monitoring Dashboards
- Key Safety Metrics for Production Models
- Alerting on Safety Violations and Threshold Breaches
- Root Cause Analysis for AI Failures
- Incident Response Playbooks for AI Safety Events
- Feedback Loops from Field Data to Retraining
- Change Impact Analysis Before Model Updates
- Rollback and Version Recovery Procedures
- Safety Sign-Off Requirements for Deployments
- Periodic Safety Reassessment Cycles
Module 10: AI Safety in System Integration and Architecture - Co-Designing AI and Safety-Critical Subsystems
- Interface Safety Contracts for AI Components
- Timing and Synchronisation Constraints
- Memory and Compute Isolation Strategies
- Hardware-Accelerated Safety Monitoring
- Fault Tolerance in Distributed AI Systems
- Secure Boot and Trusted Execution Environments
- Network-Level Safety for Connected Autonomous Systems
- Integration with Functional Safety Managers (e.g. ASIL-D)
- Verifying End-to-End Safety Chains
Module 11: Advanced Topics in Autonomous System Safety - Safety of Reinforcement Learning Agents
- Ensuring Safe Exploration in Online Learning
- Safety in Multi-Agent Autonomous Systems
- Coordination Safety in Swarm Robotics
- Human-Machine Interaction Safety Protocols
- Safety Implications of Transfer Learning
- Zero-Shot and Few-Shot Learning Safety Risks
- Neural Architecture Search with Safety Constraints
- Physics-Informed Neural Networks for Safety-Critical Control
- Hybrid Symbolic-Neural Approaches for Verifiable AI
Module 12: Safety Case Development Project - Selecting a Real-World Autonomous System for Analysis
- Defining Operational Design Domain (ODD)
- Developing System Boundary and Context Diagram
- Conducting Full Hazard Analysis Using STPA and FMEA
- Generating Safety Requirements with Full Traceability
- Designing AI Model with Embedded Safety Controls
- Creating Validation Plan with Coverage Metrics
- Developing Runtime Monitoring Strategy
- Building Complete GSN-Based Safety Case
- Final Review and Peer Validation of Safety Architecture
Module 13: Industry Applications and Cross-Domain Adaptation - Autonomous Vehicles: Integration with ADAS and SAE Levels
- Unmanned Aerial Vehicles (UAVs) and Beyond Visual Line of Sight (BVLOS)
- Autonomous Industrial Machinery and Robotics
- Safety in Medical AI and Surgical Robots
- Rail and Public Transportation Automation
- Smart City Infrastructure and Traffic Management
- Maritime Autonomous Surface Ships (MASS)
- Agricultural and Mining Automation Systems
- Safety Engineering for Humanoid and Collaborative Robots
- Tailoring Frameworks to Domain-Specific Regulations
Module 14: Tools, Templates, and Accelerators - Industry-Standard Tools for Safety Case Modelling
- Open Source Frameworks for AI Safety Testing
- Template Libraries for Hazard Registers
- GSN Diagram Templates for Common AI Use Cases
- Checklists for Regulatory Compliance Readiness
- Model Documentation Templates (AI Factsheets)
- Issue Tracking Integration for Safety Defects
- Automated Traceability Tools
- Dashboard Templates for Production Monitoring
- Project Kickoff and Review Meeting Agendas
Module 15: Certification, Career Advancement, and Next Steps - Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service
- Designing Test Suites for Probabilistic Outputs
- Formal Methods for Bounding AI Behavior
- Conformity Testing Against Known Input-Output Pairs
- Adversarial Testing and Robustness Evaluation
- Monte Carlo Simulation for Coverage Analysis
- Statistical Guarantees on Model Performance
- Measuring Calibration and Confidence Reliability
- Cross-Validation in Safety-Critical Contexts
- Validation of Ensemble Models and Voting Mechanisms
- Quantifying Uncertainty in Deep Neural Network Predictions
Module 6: Runtime Safety Mechanisms and Monitoring - Designing Redundant and Diverse AI Subsystems
- Runtime Health Monitoring of AI Components
- Input Validation and Sanitisation at Inference Time
- Out-of-Distribution Detection Algorithms
- Confidence Thresholding and Rejection Strategies
- Safety Supervisors and Watchdog Architectures
- Fail-Operational vs Fail-Safe Design Patterns
- Dynamic Reconfiguration Based on Safety State
- Real-Time Monitoring of Model Drift and Degradation
- Automated Logging and Diagnostics for Incident Investigation
Module 7: Safety Assurance and Certification Strategies - Building Articulated Safety Cases for AI Components
- Goal Structuring Notation (GSN) for AI Assurance
- Evidence Collection: From Testing to Formal Proofs
- Handling Partial or Probabilistic Evidence in Arguments
- Leveraging Tool Qualification to Strengthen Claims
- Integrating Safety Cases with ISO 26262 and ISO 21448 (SOTIF)
- Compliance Mapping for DO-178C, IEC 61508, and Other Standards
- Preparing for Regulatory Audits and Third-Party Certification
- Documentation Standards for AI Safety Artefacts
- Managing Safety Case Updates Across Model Versions
Module 8: Data-Centric Safety Engineering - Data Provenance and Chain of Custody
- Label Quality Assurance and Audit Trails
- Synthetic Data Generation with Safety Integrity
- Scenario Mining from Real-World Operational Data
- Data Versioning and Reproducibility
- Monitoring Data Pipeline Integrity
- Defining Data Expiry and Refresh Policies
- Geofencing Data Usage by Operational Domain
- Legal and Ethical Compliance in Data Handling
- Audit-Ready Data Governance Frameworks
Module 9: Model Monitoring and Continuous Safety Assurance - Designing Model Monitoring Dashboards
- Key Safety Metrics for Production Models
- Alerting on Safety Violations and Threshold Breaches
- Root Cause Analysis for AI Failures
- Incident Response Playbooks for AI Safety Events
- Feedback Loops from Field Data to Retraining
- Change Impact Analysis Before Model Updates
- Rollback and Version Recovery Procedures
- Safety Sign-Off Requirements for Deployments
- Periodic Safety Reassessment Cycles
Module 10: AI Safety in System Integration and Architecture - Co-Designing AI and Safety-Critical Subsystems
- Interface Safety Contracts for AI Components
- Timing and Synchronisation Constraints
- Memory and Compute Isolation Strategies
- Hardware-Accelerated Safety Monitoring
- Fault Tolerance in Distributed AI Systems
- Secure Boot and Trusted Execution Environments
- Network-Level Safety for Connected Autonomous Systems
- Integration with Functional Safety Managers (e.g. ASIL-D)
- Verifying End-to-End Safety Chains
Module 11: Advanced Topics in Autonomous System Safety - Safety of Reinforcement Learning Agents
- Ensuring Safe Exploration in Online Learning
- Safety in Multi-Agent Autonomous Systems
- Coordination Safety in Swarm Robotics
- Human-Machine Interaction Safety Protocols
- Safety Implications of Transfer Learning
- Zero-Shot and Few-Shot Learning Safety Risks
- Neural Architecture Search with Safety Constraints
- Physics-Informed Neural Networks for Safety-Critical Control
- Hybrid Symbolic-Neural Approaches for Verifiable AI
Module 12: Safety Case Development Project - Selecting a Real-World Autonomous System for Analysis
- Defining Operational Design Domain (ODD)
- Developing System Boundary and Context Diagram
- Conducting Full Hazard Analysis Using STPA and FMEA
- Generating Safety Requirements with Full Traceability
- Designing AI Model with Embedded Safety Controls
- Creating Validation Plan with Coverage Metrics
- Developing Runtime Monitoring Strategy
- Building Complete GSN-Based Safety Case
- Final Review and Peer Validation of Safety Architecture
Module 13: Industry Applications and Cross-Domain Adaptation - Autonomous Vehicles: Integration with ADAS and SAE Levels
- Unmanned Aerial Vehicles (UAVs) and Beyond Visual Line of Sight (BVLOS)
- Autonomous Industrial Machinery and Robotics
- Safety in Medical AI and Surgical Robots
- Rail and Public Transportation Automation
- Smart City Infrastructure and Traffic Management
- Maritime Autonomous Surface Ships (MASS)
- Agricultural and Mining Automation Systems
- Safety Engineering for Humanoid and Collaborative Robots
- Tailoring Frameworks to Domain-Specific Regulations
Module 14: Tools, Templates, and Accelerators - Industry-Standard Tools for Safety Case Modelling
- Open Source Frameworks for AI Safety Testing
- Template Libraries for Hazard Registers
- GSN Diagram Templates for Common AI Use Cases
- Checklists for Regulatory Compliance Readiness
- Model Documentation Templates (AI Factsheets)
- Issue Tracking Integration for Safety Defects
- Automated Traceability Tools
- Dashboard Templates for Production Monitoring
- Project Kickoff and Review Meeting Agendas
Module 15: Certification, Career Advancement, and Next Steps - Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service
- Building Articulated Safety Cases for AI Components
- Goal Structuring Notation (GSN) for AI Assurance
- Evidence Collection: From Testing to Formal Proofs
- Handling Partial or Probabilistic Evidence in Arguments
- Leveraging Tool Qualification to Strengthen Claims
- Integrating Safety Cases with ISO 26262 and ISO 21448 (SOTIF)
- Compliance Mapping for DO-178C, IEC 61508, and Other Standards
- Preparing for Regulatory Audits and Third-Party Certification
- Documentation Standards for AI Safety Artefacts
- Managing Safety Case Updates Across Model Versions
Module 8: Data-Centric Safety Engineering - Data Provenance and Chain of Custody
- Label Quality Assurance and Audit Trails
- Synthetic Data Generation with Safety Integrity
- Scenario Mining from Real-World Operational Data
- Data Versioning and Reproducibility
- Monitoring Data Pipeline Integrity
- Defining Data Expiry and Refresh Policies
- Geofencing Data Usage by Operational Domain
- Legal and Ethical Compliance in Data Handling
- Audit-Ready Data Governance Frameworks
Module 9: Model Monitoring and Continuous Safety Assurance - Designing Model Monitoring Dashboards
- Key Safety Metrics for Production Models
- Alerting on Safety Violations and Threshold Breaches
- Root Cause Analysis for AI Failures
- Incident Response Playbooks for AI Safety Events
- Feedback Loops from Field Data to Retraining
- Change Impact Analysis Before Model Updates
- Rollback and Version Recovery Procedures
- Safety Sign-Off Requirements for Deployments
- Periodic Safety Reassessment Cycles
Module 10: AI Safety in System Integration and Architecture - Co-Designing AI and Safety-Critical Subsystems
- Interface Safety Contracts for AI Components
- Timing and Synchronisation Constraints
- Memory and Compute Isolation Strategies
- Hardware-Accelerated Safety Monitoring
- Fault Tolerance in Distributed AI Systems
- Secure Boot and Trusted Execution Environments
- Network-Level Safety for Connected Autonomous Systems
- Integration with Functional Safety Managers (e.g. ASIL-D)
- Verifying End-to-End Safety Chains
Module 11: Advanced Topics in Autonomous System Safety - Safety of Reinforcement Learning Agents
- Ensuring Safe Exploration in Online Learning
- Safety in Multi-Agent Autonomous Systems
- Coordination Safety in Swarm Robotics
- Human-Machine Interaction Safety Protocols
- Safety Implications of Transfer Learning
- Zero-Shot and Few-Shot Learning Safety Risks
- Neural Architecture Search with Safety Constraints
- Physics-Informed Neural Networks for Safety-Critical Control
- Hybrid Symbolic-Neural Approaches for Verifiable AI
Module 12: Safety Case Development Project - Selecting a Real-World Autonomous System for Analysis
- Defining Operational Design Domain (ODD)
- Developing System Boundary and Context Diagram
- Conducting Full Hazard Analysis Using STPA and FMEA
- Generating Safety Requirements with Full Traceability
- Designing AI Model with Embedded Safety Controls
- Creating Validation Plan with Coverage Metrics
- Developing Runtime Monitoring Strategy
- Building Complete GSN-Based Safety Case
- Final Review and Peer Validation of Safety Architecture
Module 13: Industry Applications and Cross-Domain Adaptation - Autonomous Vehicles: Integration with ADAS and SAE Levels
- Unmanned Aerial Vehicles (UAVs) and Beyond Visual Line of Sight (BVLOS)
- Autonomous Industrial Machinery and Robotics
- Safety in Medical AI and Surgical Robots
- Rail and Public Transportation Automation
- Smart City Infrastructure and Traffic Management
- Maritime Autonomous Surface Ships (MASS)
- Agricultural and Mining Automation Systems
- Safety Engineering for Humanoid and Collaborative Robots
- Tailoring Frameworks to Domain-Specific Regulations
Module 14: Tools, Templates, and Accelerators - Industry-Standard Tools for Safety Case Modelling
- Open Source Frameworks for AI Safety Testing
- Template Libraries for Hazard Registers
- GSN Diagram Templates for Common AI Use Cases
- Checklists for Regulatory Compliance Readiness
- Model Documentation Templates (AI Factsheets)
- Issue Tracking Integration for Safety Defects
- Automated Traceability Tools
- Dashboard Templates for Production Monitoring
- Project Kickoff and Review Meeting Agendas
Module 15: Certification, Career Advancement, and Next Steps - Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service
- Designing Model Monitoring Dashboards
- Key Safety Metrics for Production Models
- Alerting on Safety Violations and Threshold Breaches
- Root Cause Analysis for AI Failures
- Incident Response Playbooks for AI Safety Events
- Feedback Loops from Field Data to Retraining
- Change Impact Analysis Before Model Updates
- Rollback and Version Recovery Procedures
- Safety Sign-Off Requirements for Deployments
- Periodic Safety Reassessment Cycles
Module 10: AI Safety in System Integration and Architecture - Co-Designing AI and Safety-Critical Subsystems
- Interface Safety Contracts for AI Components
- Timing and Synchronisation Constraints
- Memory and Compute Isolation Strategies
- Hardware-Accelerated Safety Monitoring
- Fault Tolerance in Distributed AI Systems
- Secure Boot and Trusted Execution Environments
- Network-Level Safety for Connected Autonomous Systems
- Integration with Functional Safety Managers (e.g. ASIL-D)
- Verifying End-to-End Safety Chains
Module 11: Advanced Topics in Autonomous System Safety - Safety of Reinforcement Learning Agents
- Ensuring Safe Exploration in Online Learning
- Safety in Multi-Agent Autonomous Systems
- Coordination Safety in Swarm Robotics
- Human-Machine Interaction Safety Protocols
- Safety Implications of Transfer Learning
- Zero-Shot and Few-Shot Learning Safety Risks
- Neural Architecture Search with Safety Constraints
- Physics-Informed Neural Networks for Safety-Critical Control
- Hybrid Symbolic-Neural Approaches for Verifiable AI
Module 12: Safety Case Development Project - Selecting a Real-World Autonomous System for Analysis
- Defining Operational Design Domain (ODD)
- Developing System Boundary and Context Diagram
- Conducting Full Hazard Analysis Using STPA and FMEA
- Generating Safety Requirements with Full Traceability
- Designing AI Model with Embedded Safety Controls
- Creating Validation Plan with Coverage Metrics
- Developing Runtime Monitoring Strategy
- Building Complete GSN-Based Safety Case
- Final Review and Peer Validation of Safety Architecture
Module 13: Industry Applications and Cross-Domain Adaptation - Autonomous Vehicles: Integration with ADAS and SAE Levels
- Unmanned Aerial Vehicles (UAVs) and Beyond Visual Line of Sight (BVLOS)
- Autonomous Industrial Machinery and Robotics
- Safety in Medical AI and Surgical Robots
- Rail and Public Transportation Automation
- Smart City Infrastructure and Traffic Management
- Maritime Autonomous Surface Ships (MASS)
- Agricultural and Mining Automation Systems
- Safety Engineering for Humanoid and Collaborative Robots
- Tailoring Frameworks to Domain-Specific Regulations
Module 14: Tools, Templates, and Accelerators - Industry-Standard Tools for Safety Case Modelling
- Open Source Frameworks for AI Safety Testing
- Template Libraries for Hazard Registers
- GSN Diagram Templates for Common AI Use Cases
- Checklists for Regulatory Compliance Readiness
- Model Documentation Templates (AI Factsheets)
- Issue Tracking Integration for Safety Defects
- Automated Traceability Tools
- Dashboard Templates for Production Monitoring
- Project Kickoff and Review Meeting Agendas
Module 15: Certification, Career Advancement, and Next Steps - Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service
- Safety of Reinforcement Learning Agents
- Ensuring Safe Exploration in Online Learning
- Safety in Multi-Agent Autonomous Systems
- Coordination Safety in Swarm Robotics
- Human-Machine Interaction Safety Protocols
- Safety Implications of Transfer Learning
- Zero-Shot and Few-Shot Learning Safety Risks
- Neural Architecture Search with Safety Constraints
- Physics-Informed Neural Networks for Safety-Critical Control
- Hybrid Symbolic-Neural Approaches for Verifiable AI
Module 12: Safety Case Development Project - Selecting a Real-World Autonomous System for Analysis
- Defining Operational Design Domain (ODD)
- Developing System Boundary and Context Diagram
- Conducting Full Hazard Analysis Using STPA and FMEA
- Generating Safety Requirements with Full Traceability
- Designing AI Model with Embedded Safety Controls
- Creating Validation Plan with Coverage Metrics
- Developing Runtime Monitoring Strategy
- Building Complete GSN-Based Safety Case
- Final Review and Peer Validation of Safety Architecture
Module 13: Industry Applications and Cross-Domain Adaptation - Autonomous Vehicles: Integration with ADAS and SAE Levels
- Unmanned Aerial Vehicles (UAVs) and Beyond Visual Line of Sight (BVLOS)
- Autonomous Industrial Machinery and Robotics
- Safety in Medical AI and Surgical Robots
- Rail and Public Transportation Automation
- Smart City Infrastructure and Traffic Management
- Maritime Autonomous Surface Ships (MASS)
- Agricultural and Mining Automation Systems
- Safety Engineering for Humanoid and Collaborative Robots
- Tailoring Frameworks to Domain-Specific Regulations
Module 14: Tools, Templates, and Accelerators - Industry-Standard Tools for Safety Case Modelling
- Open Source Frameworks for AI Safety Testing
- Template Libraries for Hazard Registers
- GSN Diagram Templates for Common AI Use Cases
- Checklists for Regulatory Compliance Readiness
- Model Documentation Templates (AI Factsheets)
- Issue Tracking Integration for Safety Defects
- Automated Traceability Tools
- Dashboard Templates for Production Monitoring
- Project Kickoff and Review Meeting Agendas
Module 15: Certification, Career Advancement, and Next Steps - Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service
- Autonomous Vehicles: Integration with ADAS and SAE Levels
- Unmanned Aerial Vehicles (UAVs) and Beyond Visual Line of Sight (BVLOS)
- Autonomous Industrial Machinery and Robotics
- Safety in Medical AI and Surgical Robots
- Rail and Public Transportation Automation
- Smart City Infrastructure and Traffic Management
- Maritime Autonomous Surface Ships (MASS)
- Agricultural and Mining Automation Systems
- Safety Engineering for Humanoid and Collaborative Robots
- Tailoring Frameworks to Domain-Specific Regulations
Module 14: Tools, Templates, and Accelerators - Industry-Standard Tools for Safety Case Modelling
- Open Source Frameworks for AI Safety Testing
- Template Libraries for Hazard Registers
- GSN Diagram Templates for Common AI Use Cases
- Checklists for Regulatory Compliance Readiness
- Model Documentation Templates (AI Factsheets)
- Issue Tracking Integration for Safety Defects
- Automated Traceability Tools
- Dashboard Templates for Production Monitoring
- Project Kickoff and Review Meeting Agendas
Module 15: Certification, Career Advancement, and Next Steps - Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service
- Preparing for Third-Party Certification Bodies
- Navigating Audits with Confidence and Clarity
- Presenting Safety Cases to Executive and Board-Level Stakeholders
- Building a Portfolio of Safety Engineering Work
- Advancing Your Career in Safety-Critical AI
- Transitioning into Roles like AI Safety Architect or Chief Safety Officer
- Contributing to Standards Development and Best Practices
- Networking with Global Safety Engineering Communities
- Staying Ahead of Evolving AI Regulations
- Lifetime Access to Course Updates and the Certificate of Completion issued by The Art of Service