# ISO/IEC TR 5469:2024

> ISO/IEC TR 5469:2024 is the bridge between classical functional safety (IEC 61508 and its sector children) and machine-learned components. It describes properties, risk factors and available methods for using AI inside, or alongside, a safety-related function. Provael sits on the verification-and-validation side of that document: Adversarial-robustness ASR + benign-FPR control as V&V evidence for an AI element used in or alongside a safety function (the report is one input to the AI-safety lifecycle)

Regulation/standard: ISO/IEC TR 5469:2024
Timing: Published January 2024. A Technical Report, not a certifiable standard - it informs a functional-safety argument, it does not confer one.

## What it is

- A Technical Report from ISO/IEC JTC 1/SC 42, published January 2024 and applicable across sectors rather than tied to one.
- It covers three distinct situations, and they are not interchangeable: AI used inside a safety-related function, non-AI safety functions used to constrain AI-controlled equipment, and AI used to design or develop a safety function.
- A TR is informative. It carries no clauses you can be certified against, so nothing here is a conformity route - it is vocabulary and method that a safety argument can cite.
- Related: ISO/IEC 23894:2023 (AI risk management) and ISO/IEC 42001:2023 (AI management system). TR 5469 is the functional-safety-facing one of the three.

## Where a red-team result fits

- **Verification & validation of an AI element** - AI — Functional safety and AI systems (verification & validation evidence) - an adversarial-robustness rate with a matched benign control is V&V evidence about the element, produced repeatably. It is one input to the AI-safety lifecycle, not a determination within it.

## What Provael maps to it

- An attack-success rate per EAI risk with a 95% interval and a benign false-positive control, as V&V evidence for the AI element.
- The benign control is the part a functional-safety reviewer will ask for first: a rate with no baseline cannot separate an induced failure from a noisy detector.
- Retained, digest-bound run artifacts (report.json#/by_attack and report.json#/benign_fpr) so the evidence can be re-checked rather than taken on trust.

## Dates (verified 26 Jul 2026)

- Published: January 2024 (ISO/IEC JTC 1/SC 42; verified 10 Aug 2026)

## Sources

- ISO/IEC TR 5469:2024 (ISO): https://www.iso.org/standard/81283.html
- AI Standards Hub entry: https://aistandardshub.org/ai-standards/artificial-intelligence-functional-safety-and-ai-systems-iso-iec-tr-54692024/

Canonical: https://www.provael.com/compliance/iso-iec-tr-5469

---
Provael · Prove it. Prevail. · Apache-2.0 · https://github.com/provael/provael
Not legal advice; verify regulatory dates against the primary source.