ISO/IEC 24030 — AI Assessment Framework

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • ISO/IEC 42001, ISO/IEC 23894, ISO/IEC TR 24028, ISO/IEC 23053 • Governance, Quality, IT, Manufacturing, Compliance

ISO/IEC 24030 provides a formal assessment framework for evaluating the maturity, trustworthiness, governance alignment and operational readiness of AI systems. Unlike standards that prescribe specific lifecycle steps or risk methodologies, ISO/IEC 24030 defines how to assess whether an AI system—or an organisation’s AI capability—is meeting its declared requirements, risk controls and governance expectations. In practice, this standard gives regulators, auditors, customers, vendors and internal governance teams a structured lens for judging whether an AI system is transparent, well-managed and fit for purpose. For regulated manufacturing environments, this moves AI from marketing claims into auditable evidence.

“AI without assessment is guesswork. ISO/IEC 24030 makes AI measurable, reviewable and defensible.”

1) Purpose & Intent of ISO/IEC 24030

ISO/IEC 24030 exists to provide structure to an activity that many organisations previously treated informally: assessing the adequacy, maturity and trustworthiness of AI systems. Its purpose is not to certify AI or dictate how AI should be built, but to define how assessments should be conducted so they are repeatable, evidence-based, comparable across systems and aligned with the broader AI-governance ecosystem. This makes the standard valuable for internal governance boards, vendors, auditors, regulators, procurement teams and risk committees. In regulated manufacturing—where AI decisions can influence sampling, release, deviations, supplier scoring, maintenance scheduling or yield optimisation—the ability to assess AI in a disciplined manner becomes essential to regulatory confidence. ISO/IEC 24030 enables organisations to map an AI system’s strengths, weaknesses and risk posture clearly across lifecycle stages and trustworthiness dimensions.

2) Relationship to 42001, 23894, 24028, 23053 & 22989

Although ISO/IEC 24030 is an assessment standard, it does not exist in isolation. It assumes an AI system is already governed under an AI Management System such as ISO/IEC 42001. It draws on the terminology from ISO/IEC 22989 so assessors use consistent language. Its risk aspects rely on the methodology provided in ISO/IEC 23894. Its trustworthiness properties are based on ISO/IEC TR 24028. And its lifecycle references come from ISO/IEC 23053. ISO/IEC 24030 serves as the “assessment layer” that sits on top of these standards, enabling organisations to evaluate whether AI lifecycle, governance, risk controls and trustworthiness measures have been implemented effectively and consistently.

3) Assessment Categories & Dimensions

ISO/IEC 24030 defines a multi-dimensional assessment model that examines an AI system across categories such as governance alignment, lifecycle maturity, risk controls, trustworthiness, data integrity, robustness, transparency, security and operational performance. The standard does not prescribe a single scoring method—or even require numerical scoring—but it defines the conceptual structure for assessment. Each category contains sub-dimensions and evidence expectations. In regulated contexts, this allows assessors to evaluate, for example, whether data governance practices meet ALCOA+ principles; whether model development followed documented procedures; whether validation covered high-risk and edge-case scenarios; whether monitoring includes drift detection; and whether documentation would withstand a regulatory audit. Assessment categories map naturally onto the building blocks of 42001, 23894, 24028 and 23053, ensuring that no critical dimension is overlooked during evaluation.

4) Governance Alignment & Organisational Readiness

One of the central pillars of ISO/IEC 24030 is evaluating whether an AI system sits within a mature governance environment. Assessors examine whether policies, roles, responsibilities, training and oversight mechanisms—typically defined under ISO/IEC 42001—have been applied consistently. This includes reviewing whether risk tiers have been assigned, whether human oversight has been defined using the frameworks of ISO/IEC 22989, and whether management reviews include AI-specific KPIs. Organisations with immature governance may build promising models but fail assessments due to unclear ownership, inconsistent policies, poor documentation or inadequate cross-functional involvement. ISO/IEC 24030 makes governance visible: it ensures that an AI system is not simply technically competent but embedded within an organisational structure that can manage it responsibly over time.

5) Lifecycle Maturity & Process Discipline

ISO/IEC 24030 requires assessors to evaluate how well an AI system aligns with lifecycle expectations defined in ISO/IEC 23053. This includes examining the completeness and quality of documentation at each lifecycle stage: concept, data preparation, model development, verification, validation, deployment, monitoring and retirement. Assessors look for traceability between requirements, design choices, risk controls and performance results. In GxP environments, lifecycle assessment resembles CSV assessment: evidence must show the system was built intentionally, tested rigorously and integrated safely. Lifecycle maturity is often where AI initiatives fail assessments—not because the model is poor, but because documentation is weak, processes are informal or traceability is missing. ISO/IEC 24030 pushes organisations to raise lifecycle discipline to the level expected for any other critical system.

6) Risk Controls & Criticality Assessment

The standard requires that assessors review risk-management artefacts to determine whether risks identified in line with ISO/IEC 23894 were correctly analysed, prioritised and controlled. This includes evaluating hazard identification, likelihood and impact estimation, control selection, residual-risk acceptance and evidence that controls were implemented. Importantly, ISO/IEC 24030 emphasises verifying that risk controls are proportionate to the AI system’s criticality. A model used for non-critical trend analysis does not need the same level of controls as a model influencing batch disposition or patient-intervention decisions. Assessors examine whether risk classifications match organisational criteria, whether high-risk systems are subject to enhanced governance and whether risk controls remain active and effective throughout the lifecycle. This avoids the common failure mode of treating all AI the same, regardless of consequence.

7) Trustworthiness: Robustness, Fairness, Explainability & Security

Trustworthiness is a major component of ISO/IEC 24030 assessments. Drawing from ISO/IEC TR 24028, assessors evaluate robustness, resilience, reliability, fairness, transparency, explainability, privacy and security. This often includes stress-testing the system under adversarial or edge-case scenarios, reviewing mitigation strategies for bias (linked to ISO/IEC 24027), evaluating interpretability methods, analysing model stability across data variations and verifying that security controls protect training pipelines and model artefacts. In regulated manufacturing, trustworthiness assessments may focus on how sensitive the model is to equipment drift, supplier variability, ingredient substitutions or environmental changes, and whether explainability is sufficient for quality reviewers to understand why the AI recommended a specific action. ISO/IEC 24030 turns trustworthiness from a “nice-to-have” into a scored, reviewed and evidenced dimension of AI maturity.

8) Data Governance, Quality & Integrity

ISO/IEC 24030 requires assessment of data governance, including lineage documentation, representativeness analysis, quality checks, metadata completeness, version control and storage security. This area aligns strongly with data-integrity expectations from 21 CFR Part 11 and Annex 11. Assessors examine whether data used for training, validation and monitoring is accurate, complete, consistent, timely and attributable (ALCOA+). They also review whether dataset limitations were documented and whether mitigations were implemented to avoid operational risk. In quality and manufacturing contexts, data governance assessments consider whether certain suppliers, shifts, products or equipment lines are under-represented, whether historical quality events bias the dataset and whether the AI system inherits systemic weaknesses in data practices. ISO/IEC 24030 forces organisations to deal with the reality that AI is only as reliable as the data that feeds it.

9) Verification, Validation & Evidence Review

Assessors review whether the AI system underwent structured verification and validation aligned with lifecycle expectations in ISO/IEC 23053 and with the principles of CSV. Validation plans, test protocols, acceptance criteria, challenge tests, negative scenarios, statistical assessments, performance comparisons and documented deviations are evaluated. Evidence must show that the AI system works as intended, under intended conditions, with known limitations, and within defined risk thresholds. For high-risk AI systems, validation evidence should include interpretability checks, robustness tests and fairness analyses. ISO/IEC 24030 emphasises that validation must be repeatable, traceable and comprehensible to non-technical stakeholders—especially auditors, regulators and quality reviewers. Weak or undocumented validation is a major cause of assessment failure under this standard.

10) Deployment, Integration & Operational Readiness

Assessment of deployment focuses on whether the AI system has been integrated into the operational environment safely and coherently. This includes evaluating change-control records, configuration documentation, interface specifications, audit-trail settings, human-oversight logic (using 22989 concepts), fallback procedures, alert-handling workflows and user training. Assessors verify that the system cannot make silent, unreviewed changes to critical decisions and that users understand how to interpret and escalate AI-driven outputs. In regulated contexts, deployment assessment intersects with MES/LIMS/QMS integration, ensuring that AI-generated data is captured as a regulated record and that any automation respects 21 CFR Part 11 requirements. ISO/IEC 24030 pushes organisations to treat deployment as an operational transformation, not a technical checkbox.

11) Monitoring, Drift Control & Incident Management

Assessors evaluate monitoring processes for model drift, performance degradation, context changes, data shifts and unexpected behaviour. Monitoring mechanisms should detect when the AI system deviates from validated performance or begins producing anomalous outputs. These signals must feed into operational workflows such as Deviation/NCR management and CAPA. ISO/IEC 24030 assessments verify whether monitoring thresholds are meaningful, whether triggers automatically initiate investigation, and whether monitoring data is reviewed periodically by governance bodies under ISO/IEC 42001. Incident logs should include AI-related anomalies, user escalations, override patterns, unexpected predictions, or failures detected by secondary controls. ISO/IEC 24030 emphasises tight integration between AI monitoring and operational quality systems so that AI does not become an unmanaged variable in production. An AI system with excellent development performance but weak monitoring will fail assessment under this standard.

12) Documentation, Transparency & Lifecycle Evidence

Assessment activities under ISO/IEC 24030 rely heavily on documentation. Assessors expect a complete lifecycle dossier including requirements, design records, model artifacts, dataset documentation, bias assessments, risk analyses, validation outputs, monitoring evidence and retirement criteria. The standard stresses that documentation must not be retrofitted at the end of the lifecycle but maintained continually. For regulated industries, documentation quality is often the determinant of whether an AI system survives audit scrutiny. Assessors review whether documentation is structured, version-controlled, comprehensible to stakeholders outside the technical team and aligned with the definitions and expectations of ISO/IEC 22989, the lifecycle of ISO/IEC 23053 and the governance requirements of ISO/IEC 42001. ISO/IEC 24030 essentially formalises the expectation that “if it isn’t documented, it isn’t controlled.”

13) Procurement, Vendor Risk & Third-Party Assessment

Because many AI systems are developed externally or embedded within larger platforms, ISO/IEC 24030 includes guidance on assessing third-party systems. This includes reviewing vendor documentation, understanding how the vendor aligns to the ecosystem of AI standards, analysing contractual transparency obligations and verifying that vendor risk assessments align with internal risk tiers. Procurement teams should request evidence of model governance, data stewardship, testing procedures, monitoring strategies and change-notification processes. Assessment also includes determining whether the organisation may independently validate the AI system with its own data. For high-risk systems, vendors may be required to disclose model architecture, training methodology or bias-mitigation strategies. ISO/IEC 24030 makes it clear that outsourcing AI does not outsource accountability—organisations must assess third-party AI to the same standard as internal systems.

14) Assessment Outputs, Scoring & Improvement Cycles

ISO/IEC 24030 does not prescribe a mandatory scoring model, but it encourages organisations to create structured outputs: maturity scores, compliance ratings, gap analyses, heatmaps, remediation plans and management-review summaries. Assessors should identify strengths, weaknesses, partially met requirements and missing evidence. These findings feed into continuous-improvement loops under ISO/IEC 42001, where management periodically reviews AI performance, risk posture, incidents, metrics and remediation progress. The intent is not to produce a one-time grade but to build an iterative assessment culture. Over time, organisations improve tooling, documentation, template quality, lifecycle discipline, monitoring maturity and cross-functional collaboration. ISO/IEC 24030 becomes a diagnostic instrument—revealing where AI is well-managed and where further investment or control is required.

15) FAQ

Q1. Does ISO/IEC 24030 certify an AI system?
No. ISO/IEC 24030 does not create a certification scheme. It provides the structure for assessing AI systems and organisational AI maturity. Certification bodies may use this framework, but the standard itself does not prescribe certification.

Q2. How does ISO/IEC 24030 differ from lifecycle standards?
ISO/IEC 23053 defines how to build and manage an AI system. ISO/IEC 24030 defines how to assess whether that lifecycle was followed effectively. One is a process model; the other is an evaluation model.

Q3. Does ISO/IEC 24030 require quantitative scoring?
No. It supports both qualitative and quantitative assessments. Organisations may create scoring systems if helpful, but they are not required. What matters is structured, evidence-based, repeatable evaluation.

Q4. How is ISO/IEC 24030 used in regulated manufacturing?
It is used as part of AI governance, validation, supplier assessment and periodic review. It provides the structured evidence regulators expect when AI influences product quality, batch release, sampling, deviations or safety controls.

Q5. What is a practical first step?
Begin with a lightweight assessment of one existing AI system. Document gaps in lifecycle, governance, validation or monitoring. Use that assessment as a template to develop your organisation’s standard AI-assessment methodology.

Related Reading
• AI Governance & Risk: ISO/IEC 42001 | ISO/IEC 23894 | ISO/IEC TR 24028 | ISO/IEC 23053 | ISO/IEC 22989
• Quality & Systems: ISO 9001 | ISO 13485 | CSV | VMP
• Execution & Records: MES | eBR | eMMR | Deviation/NCR | CAPA