A tailored course, built for your situation
Mastering ISO 31000 for LLM & Multimodal AI Engineers
Build decision-grade risk intelligence into AI systems with confidence
$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.
Who this is for
Senior software engineer working on large language models and multimodal AI systems in high-velocity environments with compliance and governance implications
Who this is not for
Entry-level developers, non-technical risk analysts, or practitioners outside AI/ML engineering who lack hands-on system design responsibility
What you walk away with
- Map ISO 31000 principles directly to AI system architecture decisions
- Produce audit-ready risk documentation aligned with governance expectations
- Anticipate compliance requirements before they become rework cycles
- Lead cross-functional risk discussions with authority on framework intent
- Integrate risk-aware components into model pipelines without sacrificing speed
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 31000 in AI Context
Understand how ISO 31000’s principles apply specifically to AI systems, including risk identification in training data, model behavior, and multimodal output generation.
12 chapters in this module
- Core definitions in risk management
- Risk framework lifecycle overview
- AI-specific risk categories
- Linking uncertainty to model outputs
- Risk appetite in algorithmic systems
- Stakeholder roles in AI risk
- Integrity thresholds for AI agents
- Risk tolerance in real-time inference
- Documentation standards for AI risk
- Versioning risk assessments
- Traceability across model iterations
- First-line ownership models
Module 2. Risk Identification in Multimodal Systems
Systematically uncover risks across text, audio, video, and sensor inputs using ISO 31000-based patterns validated in high-scale environments.
12 chapters in this module
- Input modality threat modeling
- Cross-modal consistency checks
- Data provenance mapping
- Latent space vulnerability scanning
- Adversarial prompt surface detection
- Bias propagation pathways
- Feedback loop risks
- Temporal coherence thresholds
- Context drift detection
- Output combinatorics explosion
- Identity spoofing vectors
- Real-world grounding failures
Module 3. Risk Analysis Techniques for LLMs
Apply structured analysis methods to large language models, including failure mode sequencing and probabilistic impact scoring.
12 chapters in this module
- Failure mode taxonomy for LLMs
- Chain-of-thought risk tracing
- Hallucination frequency baselines
- Prompt injection susceptibility scoring
- Knowledge cutoff impact analysis
- Toxic output propagation models
- Context window overflow risks
- Multi-turn escalation pathways
- Semantic drift detection
- Output attribution challenges
- Model memory retention risks
- Cross-session contamination
Module 4. Risk Evaluation Against AI Standards
Benchmark identified risks against ISO 31000 criteria and external benchmarks like NIST AI RMF and EU AI Act requirements.
12 chapters in this module
- Setting risk significance thresholds
- Mapping to NIST AI RMF domains
- EU AI Act high-risk classification
- Comparable industry baselines
- Regulatory alignment checklist
- Escalation criteria for review
- Risk velocity assessment
- Cascading failure modeling
- Reputation impact scoring
- Legal liability exposure bands
- User harm severity tiers
- Incident response triggers
Module 5. Risk Treatment Planning for Engineers
Design technical controls that satisfy ISO 31000 treatment requirements while maintaining system performance and innovation velocity.
12 chapters in this module
- Control design trade-off analysis
- Model interpretability enhancements
- Confidence threshold tuning
- Output filtering strategies
- Human-in-the-loop integration
- Red team feedback loops
- Adversarial training incorporation
- Model watermarking options
- Input sanitization layers
- Output disclaimer frameworks
- Context window management
- Fallback mechanism design
Module 6. Integrating Risk into CI/CD Pipelines
Embed risk assessment gates and automated checks into deployment workflows without slowing release cycles.
12 chapters in this module
- Risk-aware CI/CD gate design
- Automated risk regression testing
- Model performance vs risk score
- Version-controlled risk registers
- Pull request risk checks
- Deployment impact scoring
- Canary rollout risk monitoring
- Rollback trigger automation
- Audit trail integration
- Compliance checklist automation
- Stakeholder notification workflows
- Post-deployment validation hooks
Module 7. Documentation for Audit and Governance
Produce clear, defensible records that satisfy internal audit, compliance, and external regulator expectations.
12 chapters in this module
- SoA structure for AI systems
- Control mapping to ISO 31000
- Risk register field requirements
- Evidence collection protocols
- Third-party audit preparation
- Regulator-facing narrative drafting
- Version reconciliation methods
- Cross-team documentation standards
- Data lineage for risk claims
- Model update justification logs
- Incident post-mortem templates
- Continuous monitoring reports
Module 8. Stakeholder Communication Strategies
Translate technical risk findings into actionable insights for product, legal, compliance, and executive teams.
12 chapters in this module
- Risk communication frameworks
- Executive summary templates
- Legal team briefing standards
- Product manager alignment
- Cross-functional risk workshops
- Escalation path clarity
- Visual risk dashboards
- Risk appetite articulation
- Trade-off negotiation scripts
- Incident comms preparation
- Media response coordination
- Board-level risk summaries
Module 9. Monitoring and Review Mechanisms
Implement ongoing surveillance of AI system risk performance and adapt controls based on real-world data.
12 chapters in this module
- Real-time risk telemetry
- Model drift detection systems
- User feedback aggregation
- Adversarial attack monitoring
- Compliance deviation alerts
- Threshold breach responses
- Quarterly risk reassessment
- External environment scanning
- Benchmark updates integration
- Peer comparison tracking
- Incident trend analysis
- Control effectiveness reviews
Module 10. Continuous Improvement in AI Risk
Institutionalize learning from incidents, audits, and near-misses to strengthen future system design.
12 chapters in this module
- Post-mortem facilitation
- Root cause analysis methods
- Corrective action tracking
- Knowledge sharing protocols
- Playbook update cycles
- Lessons learned databases
- Cross-project risk libraries
- Vendor risk feedback loops
- Industry incident analysis
- Framework evolution tracking
- Internal audit follow-up
- Regulatory change monitoring
Module 11. Advanced Integration Patterns
Combine ISO 31000 with SOC 2, ISO 27001, and NIST CSF for comprehensive governance in complex environments.
12 chapters in this module
- Control overlap identification
- Unified control mapping
- Audit efficiency strategies
- Cross-framework reporting
- Resource optimization
- Vendor assessment alignment
- Third-party attestation planning
- Integrated risk dashboards
- Common control libraries
- Automated evidence collection
- Framework update coordination
- Executive oversight simplification
Module 12. Implementation Playbook Application
Apply the hand-built playbook to a real-world AI system, demonstrating end-to-end risk integration.
12 chapters in this module
- System selection criteria
- Stakeholder onboarding
- Baseline risk assessment
- Control gap analysis
- Treatment plan execution
- Documentation generation
- Audit readiness check
- Peer review process
- Leadership presentation
- Continuous monitoring setup
- Post-implementation review
- Playbook refinement
How this maps to your situation
- Designing a new multimodal AI feature
- Responding to internal audit findings
- Preparing for external compliance review
- Leading a cross-functional AI risk initiative
Before vs. after
Before
Risk assessments are reactive, fragmented, and require extensive cross-team coordination to justify.
After
You produce structured, framework-aligned risk documentation on demand, with authority and precision.
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 3-4 hours per module, designed for engineers balancing delivery cycles and deep work.
If nothing changes
Without deeper command of ISO 31000, AI systems may pass technical review but fail governance scrutiny, leading to rework, delayed launches, and eroded trust in engineering leadership.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to LLM & multimodal AI engineers, with direct application to real-world system design and governance challenges.
Frequently asked
Is this course only for compliance professionals?
No, it's designed specifically for AI engineers who need to integrate risk thinking into system design without becoming full-time compliance staff.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-AI systems?
While focused on AI, the ISO 31000 mastery applies broadly, engineers in data, infrastructure, and product have used it to strengthen system resilience.
$199 one-time. Approximately 3-4 hours per module, designed for engineers balancing delivery cycles and deep work..
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