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AI Governance for Safety-Critical Systems

$199.00
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A tailored course, built for your situation

AI Governance for Safety-Critical Systems

A self-paced, implementation-ready course for engineering leaders embedding AI into high-assurance domains

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
You can't afford a governance gap when AI drives real-world outcomes.

The situation this course is for

Even with strong functional safety foundations, integrating AI introduces new risks: opaque decision logic, inconsistent validation paths, and evolving regulatory scrutiny. Traditional safety frameworks weren't built for adaptive models. Without a tailored governance layer, teams face rework, audit failures, or worse, safety incidents that erode trust.

Who this is for

Engineering or technical leadership roles in organizations deploying AI within safety-critical systems, automotive, industrial automation, medical devices, aerospace, where ISO 26262, IEC 61508, or similar standards apply.

Who this is not for

This is not for data scientists building proof-of-concept models, or executives seeking high-level AI strategy. It’s not for teams operating outside regulated environments.

What you walk away with

  • Establish a governance framework aligned with functional safety and AI assurance principles
  • Implement model traceability from requirements to deployment
  • Integrate AI validation into existing safety workflows
  • Prepare for audits with structured documentation and evidence packs
  • Reduce rework by catching compliance gaps early in the development cycle

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI in Safety-Critical Contexts
Understand the unique risks AI introduces in high-assurance systems. Explore real cases where governance gaps led to delays or safety concerns. Define the scope of AI governance beyond traditional functional safety.
12 chapters in this module
  1. AI vs traditional software risks
  2. Safety-critical domains using AI
  3. Regulatory expectations ahead
  4. Functional safety overlap
  5. Governance maturity model
  6. Stakeholder alignment map
  7. Risk classification framework
  8. Assurance boundary definition
  9. Lifecycle integration points
  10. Compliance evidence types
  11. Audit readiness checklist
  12. Case study: autonomous braking
Module 2. Model Risk Classification
Classify AI components by safety impact using a structured, repeatable method. Learn how to map model behavior to hazard levels and allocate assurance effort accordingly.
12 chapters in this module
  1. Hazard identification process
  2. Severity vs controllability
  3. Model autonomy levels
  4. Failure mode mapping
  5. Risk matrix customization
  6. AI-specific hazard types
  7. Classification decision tree
  8. Cross-functional review steps
  9. Documentation standards
  10. Reclassification triggers
  11. Toolchain integration
  12. Case study: perception stack
Module 3. Traceability from Requirements to Deployment
Build end-to-end traceability for AI components, linking safety goals to model behavior. Implement lightweight, maintainable tracking that survives agile sprints.
12 chapters in this module
  1. Safety goal decomposition
  2. Model input specification
  3. Output behavior bounds
  4. Data lineage tracking
  5. Version control strategy
  6. Change impact analysis
  7. Automated trace checks
  8. Tool interoperability tips
  9. Audit trail structure
  10. Human-in-the-loop points
  11. Failure response mapping
  12. Case study: OTA update
Module 4. Validation of Adaptive Systems
Validate models that evolve post-deployment. Design test strategies that cover both static performance and adaptive behavior under edge conditions.
12 chapters in this module
  1. Static vs dynamic validation
  2. Test data diversity metrics
  3. Corner case generation
  4. Simulation fidelity levels
  5. Real-world monitoring design
  6. Drift detection thresholds
  7. Retraining triggers
  8. Fallback logic validation
  9. Scenario stress testing
  10. Performance degradation signs
  11. Validation report structure
  12. Case study: weather adaptation
Module 5. Assurance Case Construction
Build compelling, evidence-based arguments that your AI system is acceptably safe. Structure claims, evidence, and arguments to satisfy auditors and stakeholders.
12 chapters in this module
  1. Assurance case anatomy
  2. Claim decomposition
  3. Evidence sufficiency rules
  4. Argument strength indicators
  5. Gaps identification method
  6. Tool-supported authoring
  7. Peer review process
  8. Stakeholder tailoring
  9. Regulatory alignment tips
  10. Update management
  11. Visualization best practices
  12. Case study: L3 system
Module 6. Human-Machine Collaboration Design
Design interfaces and handover protocols that maintain human oversight where needed. Ensure fallbacks are usable and timely.
12 chapters in this module
  1. Operational mode definitions
  2. Takeover request design
  3. Situational awareness cues
  4. Workload monitoring
  5. Transition timing rules
  6. Driver state sensing
  7. Alert hierarchy setup
  8. Degraded mode behavior
  9. Training integration
  10. Usability testing plan
  11. Fallback success metrics
  12. Case study: highway exit
Module 7. Data Governance for AI Assurance
Ensure data used in training and validation supports safety claims. Implement controls for data quality, representativeness, and lineage.
12 chapters in this module
  1. Data quality dimensions
  2. Bias detection methods
  3. Representativeness scoring
  4. Scenario coverage matrix
  5. Data versioning strategy
  6. Annotation consistency rules
  7. Synthetic data validation
  8. Privacy-risk balance
  9. Storage integrity controls
  10. Access logging setup
  11. Chain of custody design
  12. Case study: urban night scene
Module 8. Incident Response for AI Failures
Prepare for and respond to AI-related incidents with structured protocols. Minimize downtime and reputational damage while preserving evidence.
12 chapters in this module
  1. Incident classification levels
  2. Response team roles
  3. Evidence preservation steps
  4. Root cause analysis method
  5. Stakeholder communication plan
  6. Regulatory reporting triggers
  7. System rollback procedure
  8. Customer notification rules
  9. Post-mortem framework
  10. Lessons learned integration
  11. Legal-readiness checklist
  12. Case study: false positive
Module 9. Audit and Certification Readiness
Prepare for audits with curated evidence packs and clear narratives. Understand what assessors look for in AI-augmented safety cases.
12 chapters in this module
  1. Audit scope definition
  2. Evidence pack structure
  3. Compliance mapping table
  4. Gap analysis method
  5. Pre-audit rehearsal
  6. Assessor communication rules
  7. Documentation standards
  8. Toolchain output formatting
  9. Non-conformance handling
  10. Certification body expectations
  11. Timeline planning
  12. Case study: audit outcome
Module 10. Scaling Governance Across Teams
Extend governance practices across multiple teams and projects. Maintain consistency without stifling innovation.
12 chapters in this module
  1. Governance role definitions
  2. Cross-team alignment
  3. Standard template library
  4. Centralized review board
  5. Decentralized execution model
  6. Toolchain standardization
  7. Knowledge sharing rhythm
  8. Maturity assessment
  9. Feedback loop design
  10. Change adoption strategy
  11. Performance metrics
  12. Case study: global rollout
Module 11. Ethical Design in High-Stakes AI
Embed ethical considerations into system design without compromising safety or performance. Address fairness, transparency, and accountability.
12 chapters in this module
  1. Ethical risk identification
  2. Stakeholder impact mapping
  3. Fairness metrics selection
  4. Transparency levels by use case
  5. Accountability framework
  6. Bias mitigation strategies
  7. Value alignment methods
  8. Red teaming process
  9. Public trust factors
  10. Escalation pathways
  11. Documentation requirements
  12. Case study: pedestrian detection
Module 12. Future-Proofing AI Systems
Anticipate next-generation challenges in AI governance. Design systems that adapt to new regulations, technologies, and threat models.
12 chapters in this module
  1. Regulatory trend monitoring
  2. Technology horizon scanning
  3. Threat model updates
  4. Architecture flexibility
  5. Modular design principles
  6. Re-certification planning
  7. Stakeholder engagement
  8. Incident learning loop
  9. Public communication
  10. Long-term data strategy
  11. Decommissioning plan
  12. Case study: fleet evolution

How this maps to your situation

  • You're scaling AI into production systems where safety is non-negotiable
  • You need to satisfy internal and external auditors with clear evidence
  • You're bridging between AI teams and functional safety experts
  • You're designing systems that must adapt without compromising assurance

Before vs. after

Before
Uncertain how to govern AI in safety-critical contexts, relying on ad-hoc reviews and fragmented documentation.
After
Confidently lead AI assurance with a structured, auditable governance framework tailored to high-assurance systems.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 3 hours per module, designed for self-paced learning with implementation milestones.

If nothing changes
Without a clear governance approach, teams risk costly rework, failed audits, delayed certifications, or safety incidents that damage trust and credibility.

How this compares to the alternatives

Unlike generic AI ethics courses or academic treatments, this program delivers actionable, safety-aligned governance tools specifically for engineering leaders in regulated environments.

Frequently asked

Who is this course designed for?
Engineering and technical leaders responsible for deploying AI in safety-critical systems where assurance and compliance matter.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if I'm not in automotive?
Yes. The principles apply to any safety-critical domain, including medical devices, industrial automation, and aerospace.
$199 one-time. Approximately 3 hours per module, designed for self-paced learning with implementation milestones..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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