A tailored course, built for your situation
Mastering ISO 22000 for Senior AI/Data Scientists in Agentic AI Systems
Build production-grade ML systems with food safety and compliance integrity from design to deployment
$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.
Technical rigor in AI meets compliance visibility, bridging the gap between data science execution and executive oversight
The situation this course is for
High-performing AI teams build robust models, but their work often remains invisible to senior leadership because it lacks structured alignment with compliance frameworks. This invisibility limits impact, influence, and career upside, even when the work is mission-critical.
Who this is for
Senior AI/Data Scientists operating at the intersection of advanced ML systems and regulated environments, particularly in food safety and supply chain integrity. They lead technical delivery but seek greater recognition from leadership for their compliance-integrated engineering.
Who this is not for
Junior data scientists learning foundational ML, professionals outside regulated ML deployment, or teams not working under food safety or quality management standards.
What you walk away with
- Map ISO 22000 controls directly to model validation stages in the ML lifecycle
- Generate audit-ready documentation from routine development outputs
- Surface technical work to leadership through traceable compliance artefacts
- Own the narrative between engineering rigor and regulatory expectations
- Accelerate deployment sign-off by aligning with food safety compliance gates
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 22000 in AI-Driven Food Safety Systems
Establish foundational alignment between AI workflows and ISO 22000 requirements, including hazard analysis and critical control points.
12 chapters in this module
- Scope of ISO 22000 in non-manufacturing settings
- Hazard identification in data pipelines
- Role of AI in HACCP planning
- Linking ML predictions to food safety risks
- Compliance boundaries for agentic systems
- Documentation expectations for auditors
- Key differences from ISO 9001
- Interaction with HACCP principles
- Regulatory recognition in India and EU
- Integration with Cargill-level food safety policies
- Auditor interview preparation
- Common certification pitfalls
Module 2. ML Lifecycle Stages and ISO 22000 Control Mapping
Align each phase of model development to specific ISO 22000 clauses, ensuring compliance is embedded by design.
12 chapters in this module
- Requirement gathering with compliance in mind
- Data sourcing and hazard documentation
- Version control with audit trails
- Model training within controlled environments
- Validation against CCP thresholds
- Deployment with change logs
- Monitoring for deviation signals
- Retraining triggers based on risk
- Decommissioning with records
- Linking pipeline steps to clause 8.5.2
- Controlled updates without audit disruption
- Traceability from code to certificate
Module 3. Developing Compliance-Aware KPIs for ML Systems
Build performance metrics that reflect both technical accuracy and regulatory adherence.
12 chapters in this module
- Defining food safety KPIs in ML contexts
- Precision vs. risk tolerance tradeoffs
- Downtime impact on critical control points
- Incident response time benchmarks
- False negative cost modeling
- Threshold setting with auditors in mind
- Dashboarding for compliance teams
- Automated alerting for deviations
- Reporting cycle integration
- Executive summary templates
- KPI ownership in cross-functional teams
- Linking model drift to audit findings
Module 4. Documentation Frameworks for ISO 22000 Audits
Generate structured, reusable documentation artefacts that satisfy auditors and accelerate certification.
12 chapters in this module
- Required documents under Clause 7.5
- Version-controlled SOP templates
- Data lineage records
- Model decision logs
- Hazard analysis worksheets
- Critical limit justifications
- Verification activity logs
- Internal audit checklists
- Corrective action reporting
- Non-conformance tracking
- Document retention policies
- Automating document generation
Module 5. Integrating HACCP Principles into AI Models
Ensure ML systems directly support HACCP planning and execution.
12 chapters in this module
- Critical control point identification
- Setting critical limits with ML
- Monitoring systems using real-time models
- Automated corrective actions
- Verification using historical data
- Record-keeping automation
- Validation of ML-based monitoring
- Integration with HACCP team workflows
- Handling false alarms
- Training HACCP teams on AI outputs
- Updating plans based on model insights
- Audit preparation for HACCP integrations
Module 6. Building Traceability Across Model Development
Ensure full traceability from regulatory requirements to code implementation.
12 chapters in this module
- Mapping ISO 22000 clauses to code functions
- Traceability matrix templates
- Code comments linked to controls
- Change request tracking
- Impact analysis for updates
- Audit trail generation
- Tooling for traceability (Git, Jira, etc)
- Cross-team alignment on traceability
- Documentation for external auditors
- Handling gaps in traceability
- Automated traceability reporting
- Maintaining matrices across versions
Module 7. Validating Models Against ISO 22000 Requirements
Ensure AI outputs meet food safety standards through structured validation.
12 chapters in this module
- Defining validation scope
- Test data selection for risk coverage
- Accuracy thresholds for safety-critical outputs
- Validation under edge conditions
- Third-party validation approaches
- Documentation of validation results
- Revalidation triggers
- Handling model drift in validation
- Using historical incidents as test cases
- Validation frequency planning
- Cross-functional sign-off process
- Auditor review preparation
Module 8. Implementing Change Control in ML Pipelines
Apply ISO 22000 change control principles to model updates and retraining.
12 chapters in this module
- Change request submission
- Impact assessment templates
- Approval workflows
- Rollback procedures
- Communication to stakeholders
- Documentation updates
- Testing after changes
- Validation of updated models
- Audit trail maintenance
- Minimizing downtime during changes
- Emergency change protocols
- Post-change review
Module 9. Managing Supplier and Third-Party Risks
Extend ISO 22000 compliance to external data sources and model components.
12 chapters in this module
- Third-party data risk assessment
- Vendor onboarding checklists
- Contractual compliance terms
- Auditing external models
- Data sharing agreements
- Due diligence for AI vendors
- Monitoring third-party performance
- Incident response coordination
- Penetration testing for AI APIs
- Compliance verification frequency
- Exit strategies for underperforming vendors
- Reporting third-party issues to auditors
Module 10. Preparing for Certification and Surveillance Audits
Navigate the certification process with confidence and minimal rework.
12 chapters in this module
- Pre-certification gap assessment
- Internal audit planning
- Corrective action timelines
- Audit day logistics
- Interview preparation
- Document pack assembly
- Handling auditor questions
- Addressing findings
- Surveillance audit readiness
- Maintaining certification
- Continuous improvement planning
- Auditor feedback incorporation
Module 11. Driving Continuous Improvement in AI Systems
Use audit findings and operational data to improve model performance and compliance.
12 chapters in this module
- Feedback loop design
- Root cause analysis of incidents
- Model retraining triggers
- Updating risk assessments
- Improving documentation processes
- Training updates for teams
- Benchmarking against industry standards
- Incident trend analysis
- Corrective action effectiveness
- Stakeholder communication
- Regulatory change adaptation
- Lessons learned integration
Module 12. Scaling Compliance Across AI Portfolio
Replicate compliance success across multiple models and teams.
12 chapters in this module
- Developing standardized templates
- Training other teams
- Centralized oversight mechanisms
- Knowledge sharing frameworks
- Tooling standardization
- Cross-team audits
- Compliance maturity assessment
- Leadership reporting structure
- Budgeting for compliance scaling
- External benchmarking
- Certification expansion
- Organizational learning
How this maps to your situation
- When building first AI model with food safety implications
- Ahead of internal ISO 22000 audit cycle
- During cross-functional alignment with food safety teams
- When scaling AI systems across global operations
Before vs. after
Before
AI work remains technically strong but invisible to executive leadership; compliance alignment is ad hoc and reactive.
After
Model lifecycle contributions are systematically surfaced with ISO 22000 traceability, gaining executive recognition and accelerating audit readiness.
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 to fit around active project cycles.
If nothing changes
Without structured integration of ISO 22000 into AI workflows, high-impact technical work risks remaining unseen by leadership, limiting influence and career growth, even as compliance demands increase.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to senior AI practitioners working in food safety-critical environments, with direct mappings from code to ISO 22000 controls and real-world audit preparation scenarios.
Frequently asked
Do I need prior experience with ISO 22000?
No. The course starts with foundational concepts and builds to advanced integration techniques.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get certified?
The course prepares you to lead compliance efforts and pass audits, though certification is issued by external bodies.
$199 one-time. Approximately 3-4 hours per module, designed to fit around active project cycles..
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