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Can AI Be Part of a Safety Function? ISO 13849, IEC 62061, ISO/IEC TR 5469

Public summaries of ISO 13849-1 and IEC 62061, dated 2021 and 2023, report no AI-specific requirements, and the documents that do address AI near a machine safety function are a technical report, a peer-reviewed survey, test-body and US federal guidance, and EU law. UTEC Industrial designs, engineers, machines, fabricates, and installs custom material handling systems for aerospace and heavy industry from its Spokane Valley, WA facility, integrating Allen-Bradley PLC and motion control with in-house CNC machining, heat treating, and stress relief. This article sets out what each source says, with its date and hedges. On a heavy handling system an AI element sits in the controls, tuning and monitoring links of the build chain, design → engineering → parts machining → fabrication → assembly → weld fatigue → stress relief → drives → controls → tuning → monitoring, while the safety functions around it depend on every link before them.

What counts as AI, and what counts as a safety function?​

The two halves of the question come from different documents. ISO 13849-1:2023 specifies a methodology and provides related requirements, recommendations and guidance for the design and integration of safety-related parts of control systems (SRP/CS) that perform safety functions, including the design of software; it applies to SRP/CS for high demand and continuous modes of operation including their subsystems, regardless of technology and energy, and does not apply to low demand mode of operation. IEC 62061:2021+AMD1:2024+AMD2:2026 specifies requirements and makes recommendations for the design, integration and validation of safety-related control systems (SCS) for machines. It applies to control systems used, either singly or in combination, to carry out safety functions on machines that are not portable by hand while working, including a group of machines working together in a coordinated manner, and its main body covers an SCS intended to be used in high/continuous demand mode.

AI has no single definition. DGUV Test Information 05 (April 2021) said the term had no standard definition at the time. The EU AI Act, Regulation (EU) 2024/1689, Art. 3(1), defines an AI system as "a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments". The December 2025 joint guidance from CISA and partner agencies on AI in operational technology (OT) says non-machine-learning-based AI systems "employ algorithms to automate decision-making and control processes; in OT systems, this includes ladder logic automation routines and a class of safety instrumented systems", while its own scope "focuses on ML- and large language model (LLM)-based AI and AI agents".

The next two sentences are engineering reasoning. A specification that asks whether AI is in a safety function has to name its definition, because under CISA's classification a conventional ladder logic program would already count. The failure mode is a requirement that cannot be verified because its key term is undefined (ISO 13849-1:2023, scope; IEC 62061:2021+AMD1:2024+AMD2:2026, Ed. 2.2, abstract; DGUV Test Information 05, 2021, p. 1; Regulation EU 2024/1689, Art. 3 point 1; CISA et al. 2025, p. 4).

Do ISO 13849-1 and IEC 62061 contain requirements for AI?​

The public statements on the question say the standards contain no AI-specific requirements, and no public source read for this article says that either standard prohibits or permits an AI element. DGUV Test Information 05 stated in April 2021 that "Neither ISO 13849-I, IEC 62061 nor IEC 61508 currently contain specific requirements for the application of AI"; that statement predates ISO 13849-1:2023 and the 2024 and 2026 amendments of IEC 62061.

For the 2023 edition, the IFA's summary of ISO 13849-1 reports that the software requirements are now summarised in a separate section 7, and that "In the introduction, the authors state that the section does not contain any specific requirements on software using artificial intelligence". That is the IFA authors' account; the standard's own clause 7 introduction states that the clause does not contain specific requirements on software using artificial intelligence. The IEC 62061 Ed. 2.2 publisher abstract does not mention AI or machine learning, and states that "The design of complex programmable electronic subsystems or subsystem elements is not within the scope of this document"; the standard's scope and software clauses contain no AI-specific requirements either.

As engineering reasoning, the absence of AI-specific requirements is not a permission: a safety function with an AI element still has to meet what the standard requires of every SRP/CS or SCS (DGUV Test Information 05, 2021, p. 2; Hauke, Bömer and Büllesbach, IFA/DGUV 2023, §7 p. 7; IEC 62061:2021+AMD1:2024+AMD2:2026, Ed. 2.2, abstract, Clause 1 and software clause; ISO 13849-1:2023, scope and clause 7).

What does ISO/IEC TR 5469:2024 cover?​

ISO/IEC TR 5469:2024, Artificial intelligence — Functional safety and AI systems, is a Technical Report, Edition 1, published in January 2024 by ISO/IEC JTC 1/SC 42, 73 pages. Its abstract says it "describes the properties, related risk factors, available methods and processes" for three uses: AI inside a safety-related function, non-AI safety-related functions to ensure safety for AI-controlled equipment, and AI systems used to design and develop safety-related functions. The copilots article quotes the abstract in full and places a PLC code copilot in the third use.

No public page read for this article says how the published report defines its AI technology classes and usage levels, or whether it says an AI element can or cannot be credited with a performance level (PL) or safety integrity level (SIL), and this article states neither.

Follow-on work is at committee draft stage. ISO's pages show ISO/IEC CD TS 22440-1, Requirements, and CD TS 22440-2, Guidance, at stage 30.60 (close of comment period), with no abstract or publication date. The IEC's October 2023 announcement said TS 22440 "will build upon the technical report ISO/IEC TR 5469" and would be developed by joint working group JWG 4 of SC 42 and IEC TC 65/SC 65A, the subcommittee it describes as known for the IEC 61508 series; its list of focus areas is hedged ("will likely emphasize") (ISO/IEC TR 5469:2024, abstract; ISO/IEC CD TS 22440-1, iso.org, accessed October 2026; IEC Editorial Team 2023, IEC Today blog post).

How does the 2024 ACM survey summarise TR 5469's usage levels and classes?​

The public description is in Perez-Cerrolaza et al., a peer-reviewed survey in ACM Computing Surveys (2024), and it describes the 2021 draft, "ISO/IEC AWI TR 5469 ... (draft)", which it cites together with a 2021 TÜV SÜD Rail report by Schneider, not the published report. The survey says the usage-level taxonomy "classifies the use of AI technology using four basic levels (A-D)":

  • A: a safety function implemented using AI technology.
  • B: AI technology used in the safety-critical development process.
  • C: a non-safety-related function "that could interfere with safety function(s)".
  • D: a non-safety-related function that is interference-free.

Levels A and B are split by whether the AI performs automated decisions (A1, B1) or not (A2, B2). The class taxonomy reads: "Class I solutions can be developed and reviewed in compliance with safety standards"; "Class II solutions cannot be developed and reviewed in compliance with safety standards, but the proposed compensation measures are sufficient for that purpose"; "Class III solutions cannot be developed and reviewed in compliance with safety standards, and compensation measures are insufficient". The survey cites ISO 13849-1 in its 2015 edition. This article does not state whether the published TR keeps these levels and classes.

The rest of this answer is engineering reasoning. On a heavy handling machine the taxonomy works as a sorting question. A learned person detector that stops a transfer car would sit at level A in the survey's terms; a learned scheduler that only sets the car's target station would sit at C or D. The failure mode is calling it D without showing that it cannot interfere with the stop, speed or position functions (Perez-Cerrolaza et al. 2024, ACM Computing Surveys 56, no. 7, §3.4 pp. 8–9, Table 2 p. 8 and references p. 32).

What is a safety bag, and does it take the AI out of the safety function?​

The survey's Class II example is the safety bag, or diverse monitor, which it cites to IEC 61508-7 C.3.4 and also calls a run-time checker: a technique that "safely monitors that the results provided by an AI item are safe". The authors conclude: "So, the safety bag becomes the safety function that prevents unsafe states, and the AI item does not require safety standard compliance." They add that with sufficient compensation measures, such as human expert verification or a safety bag, "a Class III solution becomes a Class II solution". They also carry the caveat that the safety bag's use "must consider the safety of the system as a whole because, for example, excessive false alarms could lead to new system-level hazards", and they note human limits such as cognitive overload and reaction time. The second use in the TR 5469 abstract, non-AI safety functions ensuring safety for AI-controlled equipment, describes a comparable arrangement at abstract level.

ISO 13849-1:2023 states that it does not specify the safety functions or required performance levels (PLr) to be used in particular applications. The rest of this answer is engineering reasoning. On an overhead gantry or transfer car, the pattern is a learned planner bounded by a conventional, safety-rated speed and position monitor; the monitor is the SRP/CS, and the machine's risk assessment sets its PLr. Two failure modes follow: limits set for the planner's tested behavior rather than the hazard's full range, such as stopping distance at full speed with the heaviest load, and a monitor that trips often enough that operators work around it (Perez-Cerrolaza et al. 2024, ACM Computing Surveys 56, no. 7, §3.4 p. 9, §3.5 p. 11 and §5.1.1 p. 20; ISO/IEC TR 5469:2024, abstract; ISO 13849-1:2023, scope).

What principles has DGUV Test published for AI near safety functions?​

DGUV Test Information 05, issued in April 2021 by the German Social Accident Insurance, sets out ten principles that it says define requirements for AI technologies with regard to health and safety and serve as a guide for product-specific test requirements. They are a test body's position, not law, and the document says the position in its first principle must be adapted or, if necessary, abandoned as technology advances. The principles that bear directly on safety functions:

  • No. 1: "If a task can be carried out with the help of conventional technology, then that technology should be given preference over the use of AI."
  • No. 2: AI used for assistance systems "cannot be rated as a safety function"; where personal protection is concerned, assistance systems "do not usually meet the requirements of the relevant safety standards – such as ISO 13849-I – and must not therefore be considered as safety functions".
  • No. 3: "Continuous learning systems shall not have any dangerous impact on safety functions."
  • No. 8: "AI technologies shall not suggest a level of safety that does not exist"; AI-based systems are not a substitute for classical protective devices as defined in ISO 12100, a judgement the document ties to the current level of technology.
  • No. 9: "all safety-related parts of the control system (functional safety) shall take priority over process control, including AI", protected against manipulation, with reference to IEC 62443.

As engineering reasoning, Principle No. 2 points to a failure mode relevant to heavy equipment: an assist function, such as a camera that warns a crane operator, treated by its users as if it were the protective device (DGUV Test Information 05, 2021, pp. 1–5).

Is there a US federal rule on AI in machine safety functions?​

The research for this article found no OSHA standard or other US federal rule that addresses AI in a machine safety function; the federal documents are guidance. OSHA's technical manual chapter on industrial robots says advancements in artificial intelligence "also make it possible for robot systems to adjust or change programming functions based on sensory input changes" and "can also introduce additional hazards that need to be recognized and addressed", and, for collaborative applications, says "The required safety functions should be determined during the RA". A January 2026 NIOSH Science Bulletin summarises a proposed framework in which algorithms "cannot directly create any new 'tangible' (physical, chemical, or biological) hazards", but "they do alter the risk profile of physical platforms or substances they control or interact with"; the bulletin calls the framework a starting point.

The December 2025 CISA joint guidance says "Ultimately, humans are responsible for functional safety", advises "failsafe mechanisms that revert to traditional automation or manual for any AI-enabled system processes", and says integrating an AI system into OT networks "inevitably generates new failure states", which operators should add to existing functional safety and incident response processes. NIST AI 100-1, which describes itself as voluntary and non-sector-specific, says safety risks "that pose a potential risk of serious injury or death call for the most urgent prioritization and most thorough risk management process", and that AI safety approaches should "align with existing sector- or application-specific guidelines or standards".

As engineering reasoning, the failure mode this guidance targets is an AI-enabled process with no defined fallback: when the model faults, nobody has decided whether the machine reverts to conventional automation, to manual control, or stops (OSHA Technical Manual, Section IV Chapter 4, accessed September 2026; Sadowski and Howard 2026, NIOSH Science Bulletin; CISA et al. 2025, pp. 15, 18 and 20; NIST AI 100-1, 2023, §3.2 pp. 14–15).

How does EU law treat AI safety components in machinery after the 2026 amendment?​

EU law binds products placed on the EU market and is not a US requirement. As published in 2024, AI Act Art. 6(1) made an AI system high-risk only where both conditions were met: (a) it is intended to be used as a safety component of a product, or is itself a product, covered by the legislation listed in Annex I; and (b) that product is required to undergo a third-party conformity assessment under that legislation. The original Annex I Section A listed Directive 2006/42/EC on machinery.

Regulation (EU) 2026/1744, the Digital Omnibus on AI (OJ 24 July 2026, in force on the third day after publication), changed this for machinery:

  • Safety component. The rewritten Art. 3(14) adds that "a component fulfils a safety function where its intended purpose is to prevent or mitigate risks to health and safety of persons or property".
  • Exclusion, with its condition. New Art. 6(1a): AI systems "that are solely used for non-safety related aspects of user assistance, performance optimisation, service efficiency, automation or convenience or quality control shall not qualify as safety components". New Art. 6(1b): notwithstanding 1a, AI systems "the failure or malfunctioning of which would endanger health and safety shall qualify as safety components".
  • Machinery moved to Annex I Section B. Regulation (EU) 2023/1230 becomes Section B point 21, and for those Art. 6(1) systems "only Article 6(1), Article 60a and Articles 102 to 112 shall apply".
  • Requirements through the Machinery Regulation. Its new Art. 8 text requires delegated acts adding AI requirements to its Annex III, and "Those delegated acts shall apply by 2 August 2028". The AI Act's Chapter III Sections 1–3, except Art. 6(5), now apply from 2 August 2028 for systems high-risk under Art. 6(1) and Annex I.

The amendment does not change the Machinery Regulation's 14 January 2027 application date or its Annex I Part A points 5 and 6, which the copilots article covers. The research for this article did not find the Art. 8 delegated acts, and this article does not describe their content (Regulation EU 2024/1689, Art. 6 para. 1 and Annex I as published; Regulation EU 2026/1744, Art. 1 points 2, 4, 8, 40 and 41, Art. 3 point 1, and Art. 4).

What does the EU Machinery Regulation require of control systems with self-evolving behaviour?​

Regulation (EU) 2023/1230 Annex III Part B, General principles, point 1 requires the risk assessment to include hazards foreseeable at placing on the market as an intended evolution of fully or partially self-evolving behaviour or logic as a result of the machinery being designed to operate with varying levels of autonomy. Section 1.2.1 requires control systems of machinery "with fully or partially self-evolving behaviour or logic that are designed to operate with varying levels of autonomy" to be designed and constructed in such a way that:

  • (a) "they shall not cause the machinery or related product to perform actions beyond its defined task and movement space";
  • (b) recording of data on the safety-related decision-making process for software-based safety systems ensuring safety functions is enabled after placing on the market, and the data is retained for one year after collection, exclusively to demonstrate conformity on a reasoned request from a competent national authority;
  • (c) "it shall be possible at all times to correct the machinery or related product in order to maintain its inherent safety".

For all control systems, section 1.2.1 says the limits of the safety functions are established in the manufacturer's risk assessment, and modifications to settings or rules generated by the machinery or by operators, "including during the machinery or related product learning phase", are not allowed where they could lead to hazardous situations.

The next two sentences are engineering reasoning. Requirement (a) fits the safety-bag pattern above: a defined task and movement space enforced outside the learning element. The failure mode it targets on a positioner or transfer car is learned behaviour that drifts past the envelope the risk assessment assumed (Regulation EU 2023/1230, Annex III Part B, General principles point 1 and section 1.2.1).

What would an assurance case for a machine-learning component contain?​

The University of York's AMLAS guidance, Version 1 (February 2021), "comprises a set of safety case patterns and a process" for integrating safety assurance into the development of ML components and for generating the evidence that justifies their acceptable safety. Its scope covers six ML lifecycle stages: ML safety assurance scoping, safety requirements elicitation, data management, model learning, model verification and model deployment.

Three limits come with it. "The AMLAS process requires as input the system safety requirements generated from the system safety process"; "The scope of AMLAS is limited to the ML component. As such, this document should not be used in isolation from other standards and guidelines that specify best practices in safety-critical systems"; and it "has a primary focus on off-line supervised learning". It describes itself as guidance only and does not mention ISO 13849-1, IEC 62061 or machinery. DGUV's Principle No. 5 adds "Data quality shall be monitored and ensured".

The next two sentences are engineering reasoning. Applying AMLAS to a machine safety function puts the machine's risk assessment and safety requirements first, with the ML evidence built under them. The failure mode this order guards against is a model validated on a test set that does not represent the loads, lighting or dust of the actual plant (Hawkins et al. 2021, AMLAS Version 1, pp. 2–3; DGUV Test Information 05, 2021, p. 3).

Can AI help design a safety function without being part of it?​

Designing safety-related functions with AI is the third use in the TR 5469 abstract. One published example is the AI-based expert system for functional safety of machinery described by Iyenghar in Risk Analysis (2025). Its abstract describes a chatbot for tasks "such as hazard identification, risk assessment, risk reduction, and safety function recommendation", a knowledge base that "can be populated by functional safety experts", and use by inexperienced machinery design personnel "before consulting with safety experts"; only the abstract was read for this article. Classifying the system as the third TR 5469 use is engineering reasoning, since the abstract does not cite TR 5469.

The copilots article covers Rockwell's documented statement that its Copilot can explain, but cannot modify, safety-related PLC code and device configuration. NIST AI 600-1 names the human-side failure mode: humans "may over-rely on GAI systems or may unjustifiably perceive GAI content to be of higher quality than that produced by other sources", which it calls "an example of automation bias, or excessive deference to automated systems". As engineering reasoning, a hazard list or PLr proposal drafted with AI help goes through the same review and sign-off as one drafted by hand (Iyenghar 2025, Risk Analysis 45, no. 12, abstract; ISO/IEC TR 5469:2024, abstract; Rockwell Automation 2026, FactoryTalk Design Studio Copilot, undated web documentation, accessed September 2026; NIST AI 600-1, 2024, §2.7 p. 9).

What controls and sensing does an AI-adjacent safety architecture need?​

DGUV's Principle No. 9 says the safety-related parts of the control system shall take priority over process control, including AI, and CISA calls for failsafe reversion to traditional automation or manual control. DGUV's data-quality example is periodic fault detection on safe and reduced speeds of machine tools by monitoring the encoder: if the encoder values are also given plausibility checks during the process, faults are detected at the time of occurrence.

The list below and the closing failure-mode sentence are engineering reasoning. An arrangement that fits these sources keeps the learned element on the standard side and the safety functions on conventional, validated hardware and logic:

  • Position and speed sensing: encoders on drives and axes, with plausibility checks, feeding safety-rated speed and position monitoring.
  • Protective devices: light curtains and laser scanners built to IEC 61496-1:2020, which covers electro-sensitive protective equipment designed specifically to detect persons or parts of persons; a production camera running a learned model is not that equipment, as the machine vision article explains.
  • Load and zone interlocks: load cells on lifts and positioners and zone interlocks on transfer cars and conveyors, wired to the safety-related control system.
  • Drives: VFD and servo drives whose safe stop functions are commanded by the safety logic, not by the model.
  • Monitoring: logs of the AI element's inputs, outputs and every safety-function demand, for DGUV's Principle No. 10 field-behaviour review and the decision-data recording Annex III section 1.2.1(b) requires for self-evolving systems.

UTEC Industrial builds this layer as a Rockwell Automation Recognized System Integrator on Allen-Bradley ControlLogix and CompactLogix platforms, with UL 508A panel building, factory acceptance testing and on-site commissioning, and integrates FANUC robotic cells, including vision, with a FANUC design and engineering partner. The failure mode to test for at FAT is a model output that reaches a safety input, such as a standard-side tag the safety logic reads as a permissive (DGUV Test Information 05, 2021, pp. 4–5; CISA et al. 2025, p. 15; IEC 61496-1:2020; Regulation EU 2023/1230, Annex III section 1.2.1).

What should a specification say about AI near a safety function?​

The IFA summary of ISO 13849-1:2023 says safety functions now need to be defined in detail as part of a safety requirements specification (SRS), each based on the EN ISO 12100 risk assessment and reduction process, with required details that include the triggering event, the required reaction, the PLr, the permitted response time, behaviour on energy loss, demand rate and restart conditions. DGUV's Principle No. 8 says the manufacturer shall provide unambiguous descriptions of the functions and performance limits of the system, and CISA advises taking inventory of AI components and the components that rely on them.

The rest of this answer is engineering reasoning. For a handling system with an AI element, a specification can state:

  • the definition of AI the contract uses, and which functions contain an AI element;
  • that no AI element is part of a safety function without the functional-safety lead's written approval, naming the standard and route;
  • for each AI element, its relation to the safety functions (inside, monitored by, or interference-free) and the evidence for it;
  • the fallback state when the AI faults or is bypassed, and whether any learning continues in service.

These items exist to prevent an AI element from ending up carrying safety without anyone having decided that it should (Hauke, Bömer and Büllesbach, IFA/DGUV 2023, §5 pp. 3–4; DGUV Test Information 05, 2021, p. 4; CISA et al. 2025, p. 18).

Related Articles

References​

  • ISO 13849-1:2023: Safety of machinery — Safety-related parts of control systems — Part 1: General principles for design. International Organization for Standardization, 2023.
  • IEC 62061:2021+AMD1:2024+AMD2:2026: Safety of machinery — Functional safety of safety-related control systems. IEC, 2026 (Ed. 2.2, consolidated version).
  • Hauke, M., Bömer, T., Büllesbach, K.-H. Fourth edition of EN ISO 13849-1: Most important new features in 2023 at a glance. German Social Accident Insurance (DGUV), 2023.
  • DGUV Test Information 05: General Principles for Assessing the Safety of Artificial Intelligence (AI). German Social Accident Insurance (DGUV), 2021.
  • Regulation (EU) 2024/1689: Laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, 2024.
  • Regulation (EU) 2026/1744: Amending Regulations (EU) 2024/1689, (EU) 2018/1139 and (EU) 2023/1230 as regards the simplification of the implementation of harmonised rules on artificial intelligence (Digital Omnibus on AI). Official Journal of the European Union, 2026.
  • Official Journal of the European Union. Regulation (EU) 2023/1230 on machinery, 2023.
  • CISA et al. Principles for the Secure Integration of Artificial Intelligence in Operational Technology. U.S. Cybersecurity and Infrastructure Security Agency, 2025.
  • ISO/IEC TR 5469:2024: Artificial intelligence — Functional safety and AI systems. ISO/IEC, 2024.
  • ISO/IEC CD TS 22440-1: Artificial intelligence — Functional safety and AI systems — Part 1: Requirements (committee draft, under development). ISO/IEC, accessed October 2026.
  • IEC Editorial Team. IEC and ISO launch working group to advance functional safety of AI systems (IEC Today blog post). International Electrotechnical Commission, 2023.
  • Perez-Cerrolaza, J., Abella, J., Borg, M., Donzella, C., Cerquides, J., Cazorla, F. J., Englund, C., Tauber, M., Nikolakopoulos, G., Flores, J. L. (2024). "Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey." ACM Computing Surveys, 56(7), 1-40.
  • OSHA. OSHA Technical Manual (OTM), Section IV: Chapter 4 - Industrial Robot Systems and Industrial Robot System Safety. U.S. Department of Labor (undated web documentation, accessed September 2026).
  • Sadowski, J. P., Howard, J. Practical Strategies to Manage AI Hazards in the Workplace (NIOSH Science Bulletin). National Institute for Occupational Safety and Health, 2026.
  • NIST AI 100-1: Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology, 2023.
  • Hawkins, R., Paterson, C., Picardi, C., Jia, Y., Calinescu, R., Habli, I. Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS), Version 1. Assuring Autonomy International Programme, University of York, 2021.
  • Iyenghar, P. (2025). "Implementation of an AI-Based Expert System for Functional Safety of Machinery." Risk Analysis, 45(12), 4818-4842.
  • Rockwell Automation (2026). FactoryTalk Design Studio Copilot. FactoryTalk Design Studio Online Documentation. Rockwell Automation, 2026 (undated web documentation, accessed September 2026).
  • Autio, C., Schwartz, R., Dunietz, J., Jain, S., Stanley, M., Tabassi, E., Hall, P., and Roberts, K. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1. National Institute of Standards and Technology, 2024.
  • IEC 61496-1:2020: Safety of machinery — Electro-sensitive protective equipment — Part 1: General requirements and tests. IEC, 2020 (Ed.4).

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