Overview

This two-day training course provides an in-depth introduction to ISO/IEC TS 22440 and its approach to functional safety for AI systems. Designed for organizations developing Physical AI and other AI-enabled safety-critical products, the course examines how the standard’s direction can be applied to the development, validation, and operation of AI safety-related systems and AI-based development tools. ISO/IEC TS 22440 is under development. This course covers the foundational AI safety concepts that align with the trajectory of the emerging standard and may evolve as the standard progresses.

The increasing deployment of Artificial Intelligence (AI) in safety-critical systems is fundamentally changing how functional safety must be approached. Traditional safety standards such as IEC 61508 and ISO 26262 were developed around systems whose behavior can be fully specified, traced, and systematically analyzed, enabling predictable identification and mitigation of failures.

AI-based systems, particularly those using machine learning, introduce behavior that is difficult to predict, explain, or fully specify using traditional deterministic safety assumptions. Their performance depends on data, training processes, and operational conditions, which can vary over time. These characteristics challenge core assumptions of established safety frameworks and require new approaches to hazard analysis, validation, risk reduction, and operational monitoring.

Developed by the same senior practitioners who lead SRES consulting engagements, this training draws on decades of hands-on experience and deep expertise in functional safety and AI safety standards, including IEC 61508, ISO 26262, ISO/IEC TR 5469, and ISO/PAS 8800.

The training addresses the specialized approach required for the inherently dynamic nature of AI decision-making. Participants will examine the AI safety lifecycle, classification schemes for AI software components, hazard and risk assessment, AI fault analysis, mitigation techniques such as diverse redundancy and runtime monitoring, and approaches for quantifying residual failures and validating non-deterministic software through statistical performance assessments.

Intended Audience

This course is designed for engineers and leaders involved in the development, integration, or oversight of Physical AI systems operating in safety-critical applications. It is particularly relevant for:

• Systems, hardware, software, and safety engineers working on autonomous robotics, humanoid systems, industrial automation, mobile robots (AGVs/AMRs), surgical robotics, unmanned aerial systems, or other embodied AI applications
• Engineering managers and technical leads responsible for guiding teams through safety-critical development processes for Physical AI products
• CTOs and technical executives evaluating organizational readiness, risk posture, or strategic direction for responsible robotics and Physical AI deployment in safety-critical applications

Objectives

By the end of this course, participants will be able to:

• Understand the purpose, structure, and emerging direction of ISO/IEC TS 22440 for functional safety and AI systems
• Understand the fundamental differences between traditionally specified, logic-driven safety systems and data-driven AI-based systems, and the implications for safety engineering
• Interpret how AI technologies are classified within safety-related systems and how this influences safety strategy and required rigor
• Recognize how AI systems are realized across data, training, and inference stages, and where safety risks emerge throughout this lifecycle
• Identify key AI-specific risk factors—including uncertainty, environmental complexity, and data-related issues—that challenge traditional safety approaches
• Understand how established functional safety concepts, including HARA, risk reduction, and safety functions, from IEC 61508 and ISO 26262 extend to AI-based systems
• Understand how AI faults differ from traditional system failures and how they can manifest across development and operation
• Understand practical architectural and system-level approaches for managing AI-related risks, including supervision, fallback, redundancy, and monitoring strategies
• Develop an intuition for how safety arguments are constructed for AI-based systems in the context of ISO/IEC TS 22440 and established functional safety practice

Agenda

Below you will find an outline of the training course schedule.

DAY 1

• The Intersection of AI & Safety: Bridging the gap between traditional functional safety and AI-driven system challenges
• ISO/IEC TS 22440 and Related Standards: Understanding ISO/IEC TS 22440 in the context of IEC 61508, ISO 26262, ISO/IEC TR 5469, and ISO/PAS 8800
• Terminology & Classification: Understanding AI safety-related control systems, AI faults, functional insufficiencies, and how AI systems are categorized according to safety relevance and required rigor
• The AI Safety Life Cycle: Understanding ISO/IEC TS 22440’s direction for AI classification, lifecycle activities, data, training, inference, and continuous monitoring
• Hazard & Risk Assessment: Integrating system misbehavior into the HARA and evaluating risk in systems without fully specified behavior
• AI Fault Analysis: Identifying faults across model development, input handling, and runtime system behavior

DAY 2

• Architectural Mitigations: Designing safety mechanisms, employing redundancy, and managing uncertainty in AI-based systems
• Data Quality & Training: Managing Operational Design Domain (ODD) coverage, addressing bias, and mitigating data and concept drift
• Testing & Validation: Applying statistical performance metrics, robustness testing, and assessing residual risk in systems with data-dependent and non-fully specified behavior
• AI-Based Development Tools: Considering how AI-assisted tools can be evaluated and managed within safety-critical development environments