ISO/IEC 23894 (AI Risk Management) Fundamentals Training by Tonex
Elevate your organization’s AI confidence with a practical foundation in ISO/IEC 23894. Participants learn how to identify, assess, and treat AI risks across the model lifecycle while aligning governance, assurance, and measurement. The course translates standard language into actionable controls for real projects. You will map roles, evidence, and decision rights, and build risk registers that stand up to scrutiny. On cybersecurity, the program highlights threat modeling for AI-enabled systems, protection of model artifacts, and secure MLOps guardrails. It also addresses adversarial risks, data poisoning, and model exfiltration, tying security controls to risk appetite and compliance outcomes.
Learning Objectives
- Explain the structure and intent of ISO/IEC 23894 across the AI lifecycle
- Apply governance, roles, and accountability to AI risk decisions
- Perform risk identification, analysis, evaluation, and treatment with traceable evidence
- Integrate risk controls into model design, data pipelines, and deployment workflows
- Align documentation and metrics for audits, assurance, and continuous improvement
- Strengthen cybersecurity by embedding controls for adversarial threats, data integrity, and secure operations
Audience
- AI and ML Engineers
- Data Scientists and Architects
- Risk and Compliance Managers
- Product Owners and Program Managers
- CISOs and Security Architects
- Cybersecurity Professionals
Course Modules
Module 1 – Standard Overview Essentials
- Scope and terminology
- Lifecycle and contexts
- Principles and outcomes
- Stakeholders and roles
- Documentation requirements
- Assurance and audits
Module 2 – Governance And Accountability
- Decision rights model
- Risk appetite mapping
- Policy and controls
- Ethics and alignment
- Third-party oversight
- Escalation pathways
Module 3 – Risk Identification And Analysis
- Use case scoping
- Hazard and harms
- Threat modeling AI
- Data and bias risks
- Model failure modes
- Impact likelihood scoring
Module 4 – Treatment And Control Design
- Control objectives
- Preventive safeguards
- Detective monitoring
- Corrective actions
- Residual risk criteria
- Risk acceptance records
Module 5 – Secure AI Lifecycle Integration
- Secure data intake
- Model provenance tracking
- Validation and testing
- Drift and resilience
- Deployment guardrails
- Change management gates
Module 6 – Measurement And Continuous Improvement
- Risk metrics taxonomy
- Quality and safety KPIs
- Monitoring dashboards
- Incident postmortems
- Audit-ready evidence
- Maturity roadmapping
Ready to operationalize ISO/IEC 23894 and make AI risk decisions defensible, repeatable, and secure? Enroll now with Tonex to equip your team with practical templates, governance patterns, and improvement playbooks that accelerate compliant, trustworthy AI at scale.