Introduction to ISO/IEC 23894 – AI Risk Management Fundamentals Training by Tonex
Modern AI initiatives succeed when risk is engineered as deliberately as innovation. This course grounds teams in ISO/IEC 23894, translating its lifecycle guidance into clear, actionable practices across strategy, design, deployment, and monitoring. You’ll learn to identify, evaluate, and control AI-specific risks such as bias, drift, misuse, and systemic failure while aligning with governance and business value.
Cybersecurity is woven throughout: we map threat models to AI components, connect controls to adversarial tactics, and show how model integrity and data protection reduce breach blast radius. Participants leave with a common language, repeatable methods, and implementation-ready checklists.
Learning Objectives
- Understand the purpose, scope, and structure of ISO/IEC 23894
- Apply risk identification, analysis, evaluation, and treatment to AI systems
- Integrate governance, accountability, and stakeholder roles across the AI lifecycle
- Operationalize monitoring, metrics, and continuous improvement for risk controls
- Align AI risk management with legal, regulatory, and ethical expectations
- Strengthen security posture by embedding cybersecurity controls into AI pipelines
Audience
- AI and ML Engineers
- Data Scientists and MLOps Practitioners
- Risk and Compliance Managers
- Product and Program Managers
- Enterprise Architects and CTO Office
- Cybersecurity Professionals
Course Modules
Module 1 – Standard Overview
- Purpose and terminology of 23894
- Relationship to ISO risk frameworks
- AI lifecycle and risk touchpoints
- Roles, responsibilities, accountability chains
- Documentation and evidence expectations
- Maturity staging for adoption
Module 2 – Risk Identification
- Context setting and system scoping
- Asset, data, and model inventories
- Use-case and misuse-case mapping
- Bias, safety, and reliability factors
- Third-party and supply chain exposures
- Assumptions, constraints, uncertainties
Module 3 – Analysis Techniques
- Likelihood and consequence modeling
- Control effectiveness assessment
- Metrics, indicators, and thresholds
- Scenario, stress, and sensitivity tests
- Model-specific failure mode analysis
- Aggregation of systemic risks
Module 4 – Evaluation Decisions
- Risk criteria and acceptance rules
- Prioritization across portfolios
- Trade-offs, cost–benefit, proportionality
- Escalation and decision records
- Ethical, legal, and societal filters
- Approval gates and governance forums
Module 5 – Treatment Planning
- Preventive controls and guardrails
- Detective controls and observability
- Corrective actions and rollback plans
- Human-in-the-loop decision patterns
- Vendor and third-party obligations
- Residual risk and acceptance statements
Module 6 – Monitoring and Improvement
- Runtime monitoring and drift detection
- Incident response for AI failures
- Post-incident learning and updates
- Periodic review and revalidation cycles
- Change management for models and data
- Audit readiness and compliance mapping
Ready to embed ISO/IEC 23894 into real projects and reduce AI risk without slowing delivery? Enroll now with Tonex to equip your team with a common framework, practical tools, and repeatable methods that align innovation with resilient, secure outcomes.