ISO 42001 A.6: AI system life cycle
Responsible design, development, deployment
AIGE-OBL-ISO42001-A6. Drawn from chapter 08.
Text alternative
- Clause: ISO 42001, A.6.
- Duty holder: Not stated.
- Applies from: No date, Voluntary.
- Artefact: Pipeline controls.
- Layers: Layer 01, Layer 03, Layer 04.
- Evidence record: Eval result, +10 more.
- Record schemas: Eval result , Agent register entry , Test report , Go/no-go decision , Test plan , Post-market monitoring plan , Decommissioning runbook , Use-case record , AI system register entry , Model card , Design record .
- The same topic in 26 other frameworks; the crosswalk section below links each clause.
- Id
AIGE-OBL-ISO42001-A6- Instrument
- ISO/IEC 42001 standard
- Compared side by side
- ISO 42001 vs EU AI Act · NIST AI RMF vs ISO 42001
- Clause
- A.6
- Applies from
- No date Voluntary · Voluntary management-system standard (2023); no presumption of conformity
The artefact that evidences it
Pipeline controls; eval gates; change management.
Data table: every relation
| Family | Item | Relation |
|---|---|---|
| Patterns | AI Threat Model | Direct |
| Patterns | Deactivation, Localisation & Retirement Runbook | Direct |
| Patterns | Drift & Fairness Monitor | Direct |
| Patterns | Fairness Eval Suite | Direct |
| Patterns | Model Artefact Integrity | Direct |
| Patterns | Staged Rollout with Rollback Criteria | Direct |
| Open controls | AIGE-CTL-EVAL-009 Evaluation Validity Checks | Direct |
| Open controls | AIGE-CTL-EVAL-008 Harness and Configuration Attestation | Direct |
| Open controls | AIGE-CTL-ASSURE-010 Model Artefacts Signed at Build and Verified Before Load | Direct |
| Open controls | AIGE-CTL-ASSURE-003 Signed Test Report Against the Plan | Direct |
| Open controls | AIGE-CTL-ASSURE-001 Test Plan Frozen Before Evaluation | Direct |
| Same crosswalk topic | AI Verify 4.3.1, 4.7.1, 4.2.1, 1.1.6, 1.7.2, 1.1.1, 3.4.1, 9.8.1, 5.10.1, 4.8.1, 4.1.1, 6.1.1, 5.2.1, 5.9.1, 9.4.1, 5.14.1, 6.5.1, 4.6.1 | Core clause |
| Same crosswalk topic | CSA AICM GRC-02, MDS-12, MDS-03, MDS-04, LOG-09, LOG-12, TVM-13, AIS-09, AIS-10, MDS-06, MDS-07, AIS-05, AIS-13, AIS-06, CCC-01, DSP-02 | Core clause |
| Same crosswalk topic | China AI Labelling Label Art. 4, Label Art. 5 | Core clause |
| Same crosswalk topic | China Deep Synthesis DeepSyn Art. 16, DeepSyn Art. 17, DeepSyn Art. 10, DeepSyn Art. 15, DeepSyn Art. 20 | Core clause |
| Same crosswalk topic | China GenAI Measures GenAI Art. 17, GenAI Art. 12, GenAI Art. 19, GenAI Art. 10, GenAI Art. 14 | Core clause |
| Same crosswalk topic | CoE Convention CoE Art. 16, CoE Art. 14(2), CoE Art. 15(2), CoE Art. 16(2)(g), CoE Art. 13 | Core clause |
| Same crosswalk topic | EU AI Act Art. 9, Art. 3, Art. 11, Art. 13, Art. 53, Art. 50, Art. 86, Art. 18, Art. 43, Art. 53(1)(d), Art. 50(2), 50(4), Art. 12, Art. 26(6), Art. 26, Art. 19, Art. 5, Art. 15, Art. 5(1)(a)–(b), Art. 55, Art. 60, Art. 15(3), Art. 42(3), Art. 57, Art. 58, Art. 59, Art. 61, Art. 25, Art. 43(4), Art. 20, Art. 79 | Core clause |
| Same crosswalk topic | G7 Code G7 Action 3, G7 Action 1 | Core clause |
| Same crosswalk topic | GAO AI Accountability 1.6, 1.9, 3.5, 1.7, 4.3, 3.7, 3.2, 4.4, 4.5 | Core clause |
| Same crosswalk topic | GB/T 45654 GB/T 45654 Content labelling, GB/T 45654 Security assessment | Core clause |
| Same crosswalk topic | GDPR Arts. 13–14, Art. 30, Art. 32 | Core clause |
| Same crosswalk topic | GPAI Code Safety C1, Safety C3, Transparency 1.1, Transparency 1.2, Safety C5, Safety 3.2, Safety C6 | Core clause |
| Same crosswalk topic | ISO 23894 23894 6.4, 23894 6.5, 23894 6.6 | Core clause |
| Same crosswalk topic | ISO 42001 6.1.2, 6.1.3, 8.2, 8.3, 6.1.4, 7.5, A.8, A.9, 9.1 | Core clause |
| Same crosswalk topic | Korea AI Act Art. 34(1)(1), Art. 31, Art. 34(1)(2), Art. 34(1)(5), Art. 32(1) | Core clause |
| Same crosswalk topic | NIST AI RMF MAP 1, MAP 5, MANAGE 1, GOVERN 1.3, MAP 1.5, MANAGE 1.3, MANAGE 1.4, MEASURE 3, MEASURE 2, MEASURE 2.8, MAP 1.6, MEASURE 2.9, MANAGE 4, MANAGE 2, MEASURE 2.7, MEASURE 2.1, MEASURE 1, MEASURE 2.3, MANAGE 2.4, MANAGE 4.1, GOVERN 1.7 | Core clause |
| Same crosswalk topic | OECD AI Principles OECD 1.5(c), OECD 1.3, OECD 1.5(b), OECD 1.4 | Core clause |
| Same crosswalk topic | OWASP LLM LLM01:2026, LLM10:2026, LLM06:2026 | Core clause |
| Same crosswalk topic | Singapore Agentic Agentic 2.1, Agentic 2.3.3, Agentic 2.3.1, Agentic 2.3.2 | Core clause |
| Same crosswalk topic | Singapore GenAI GenAI 3, GenAI 5, GenAI 6 | Core clause |
| Same crosswalk topic | TC260 Framework 3.0 TC260 2, TC260 Summary table, TC260 5.3.19, TC260 App. 2 II.6, TC260 5.3.6, TC260 App. 2 II.5, TC260 3.2.1, TC260 3, TC260 5.3.14, TC260 5.3 | Core clause |
| Same crosswalk topic | UK ATRS ATRS 2.5.2, ATRS Tier 1, ATRS 2.2 | Core clause |
| Same crosswalk topic | prEN 18228 prEN 18228 | Core clause |
| Same crosswalk topic | prEN 18229-1 prEN 18229-1 | Core clause |
| Same crosswalk topic | China Algo. Rec. AlgoRec Art. 27, AlgoRec Art. 16, AlgoRec Art. 8, AlgoRec Art. 9 | Related clause |
| Same crosswalk topic | OWASP Agentic ASI05 | Related clause |
Patterns that build it
- AI Threat Model (layer 1 and 3)
- Fairness Eval Suite (layer 3)
- Model Artefact Integrity (layer 2 and 4)
- Staged Rollout with Rollback Criteria (layer 4)
- Drift & Fairness Monitor (layer 4 and 5)
- Deactivation, Localisation & Retirement Runbook (layer 4 and 2)
The same topic in other frameworks
From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.
Risk management
- EU AI Act Art. 9 Risk management system (core)
- ISO 42001 6.1.2 AI risk assessment (core)
- ISO 42001 6.1.3 AI risk treatment (core)
- ISO 42001 8.2 AI risk assessment (operation) (core)
- ISO 42001 8.3 AI risk treatment (operation) (core)
- NIST AI RMF MAP 1 MAP 1: Context is established and understood (core)
- NIST AI RMF MAP 5 MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized (core)
- NIST AI RMF MANAGE 1 MANAGE 1: AI risks based on assessments and other analytical output are prioritized, responded to, and managed (core)
- TC260 Framework 3.0 TC260 2 Classification of AI safety risks (core)
- TC260 Framework 3.0 TC260 Summary table Risks × technological × governance measures (core)
- ISO 23894 23894 6.4 Risk assessment (core) (clause not verified)
- ISO 23894 23894 6.5 Risk treatment (core) (clause not verified)
- NIST AI RMF GOVERN 1.3 GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance (core)
- NIST AI RMF MAP 1.5 MAP 1.5: Organizational risk tolerances are determined and documented (core)
- NIST AI RMF MANAGE 1.3 MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented (core)
- NIST AI RMF MANAGE 1.4 MANAGE 1.4: Negative residual risks to both downstream acquirers of AI systems and end users are documented (core)
- NIST AI RMF MEASURE 3 MEASURE 3: Mechanisms for tracking identified AI risks over time are in place (core)
- CSA AICM GRC-02 Risk Management Program (core)
- Korea AI Act Art. 34(1)(1) Risk management plan for high-impact AI (core)
- UK ATRS ATRS 2.5.2 Risks and mitigations (core)
- Singapore Agentic Agentic 2.1 Assess and bound the risks upfront (core)
- CoE Convention CoE Art. 16 Risk and impact management framework (core)
- prEN 18228 prEN 18228 AI risk management (draft; supports Art. 9) (core) (clause not verified)
- GAO AI Accountability 1.6 Risk management: implement an AI-specific risk management plan to systematically identify, analyze, and mitigate risks (core)
- AI Verify 4.3.1 Safety: assess risks, risk metrics and risk levels of the AI system in each specific use case (core)
- AI Verify 4.7.1 Safety: identify and track residual risk that cannot be measured or mitigated, and assess the organisation's tolerance for it (core)
- NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics
- TC260 Framework 3.0 TC260 5.3.19 Re-assessment on material change
- China GenAI Measures GenAI Art. 17 Security assessment and algorithm filing
- China Algo. Rec. AlgoRec Art. 27 Security assessment
- EU AI Act Art. 3 Definitions
- ISO 42001 6.1.4 AI system impact assessment
- ISO 23894 23894 6.6 Monitoring and review (clause not verified)
- GPAI Code Safety C1 Commitment 1: Safety and Security Framework
- GPAI Code Safety C3 Commitment 3: Systemic risk analysis
- CSA AICM MDS-12 Open Model Risk Assessment
- OECD AI Principles OECD 1.5(c) Systematic risk management at each lifecycle phase
- AI Verify 4.2.1 Safety: carry out an assessment of materiality on key stakeholders, highlighting the risks of the proposed AI solution
Documentation and transparency
- EU AI Act Art. 11 Technical documentation (core)
- EU AI Act Art. 13 Transparency and provision of information to deployers (core)
- EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
- ISO 42001 7.5 Documented information (core)
- ISO 42001 A.8 Information for interested parties (core)
- China AI Labelling Label Art. 4 Explicit labels for generated content (core)
- China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
- China GenAI Measures GenAI Art. 12 Labelling of generated content (core)
- China Deep Synthesis DeepSyn Art. 16 Implicit technical labels (core)
- China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
- GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
- GDPR Arts. 13–14 Information to be provided to the data subject (core)
- CSA AICM MDS-03 Model Documentation (core)
- Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
- UK ATRS ATRS Tier 1 Summary information (core)
- Singapore GenAI GenAI 3 Trusted Development and Deployment (core)
- CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities (core)
- OECD AI Principles OECD 1.3 Transparency and explainability (core)
- G7 Code G7 Action 3 Publicly report capabilities, limitations and domains of use (core)
- GAO AI Accountability 1.9 Transparency: enable external stakeholders to access information on the design, operation, and limitations of the AI system (core)
- GAO AI Accountability 3.5 Documentation: document the methods for assessment, performance metrics, and outcomes of the AI system (core)
- AI Verify 1.1.6 Transparency: publicly report accuracy, intended use cases, limitations, risk assessment and mitigations to stakeholders (core)
- AI Verify 1.7.2 Transparency: make model or system cards publicly available where warranted by public interest (core)
- EU AI Act Art. 50 Transparency obligations for providers and deployers of certain AI systems
- NIST AI RMF MAP 1 MAP 1: Context is established and understood
- NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability are examined and documented
- China GenAI Measures GenAI Art. 19 Disclosure to regulators
- China Algo. Rec. AlgoRec Art. 16 Notice that recommendation is used
- GB/T 45654 GB/T 45654 Content labelling Generated-content labelling requirements (clause not verified)
- EU AI Act Art. 86 Right to explanation of individual decision-making
- EU AI Act Art. 18 Documentation keeping
- EU AI Act Art. 43 Conformity assessment
- EU AI Act Art. 53(1)(d) Public summary of the content used for training
- EU AI Act Art. 50(2), 50(4) Machine-readable marking of synthetic content; disclosure of deep fakes
- GPAI Code Transparency 1.2 Providing relevant information
- GDPR Art. 30 Records of processing activities
- NIST AI RMF MAP 1.6 MAP 1.6: System requirements are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks
- NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance
- CSA AICM MDS-04 Model Documentation Requirements
- Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview
- UK ATRS ATRS 2.2 Description and rationale
- CoE Convention CoE Art. 15(2) Notification of interaction with an AI system
- GAO AI Accountability 1.7 Specifications: establish and document technical specifications
- AI Verify 1.1.1 Transparency: design an in-house policy on communication to consumers that articulates the principles for transparency
Logging and traceability
- EU AI Act Art. 12 Record-keeping (core)
- TC260 Framework 3.0 TC260 App. 2 II.6 Continuous monitoring and auditing (core)
- TC260 Framework 3.0 TC260 5.3.6 Logs kept and audited (core)
- EU AI Act Art. 26(6) Deployers keep the automatically generated logs (core)
- CSA AICM LOG-09 Log Records (core)
- Korea AI Act Art. 34(1)(5) Documents showing the measures taken (core)
- OECD AI Principles OECD 1.5(b) Traceability of datasets, processes and decisions (core)
- prEN 18229-1 prEN 18229-1 AI trustworthiness framework, Part 1: logging (draft; supports Art. 12) (core) (clause not verified)
- GAO AI Accountability 4.3 Traceability: document results of monitoring activities and any corrective actions taken (core)
- AI Verify 3.4.1 Reproducibility: log all inputs and inference outputs of the model and keep them for a reasonable time for review (core)
- AI Verify 9.8.1 Accountability: process or governance-by-design technology for auditability: versioned code, models and data, and an audit trail of deployments and inputs/outputs (core)
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
- NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored
- NIST AI RMF MEASURE 3 MEASURE 3: Mechanisms for tracking identified AI risks over time are in place
- China AI Labelling Label Art. 5 Implicit metadata labels
- EU AI Act Art. 19 Automatically generated logs
- CSA AICM LOG-12 Transaction/Activity Logging
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test
- AI Verify 5.10.1 Security: monitor and log inputs to the AI system (queries, prompts, requests)
Runtime guardrails
- ISO 42001 A.9 Use of AI systems (core)
- NIST AI RMF MANAGE 2 MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by relevant AI actors (core)
- TC260 Framework 3.0 TC260 App. 2 II.5 Dynamic runtime management (core)
- TC260 Framework 3.0 TC260 3.2.1 Technological countermeasures for agentic AI (core)
- China GenAI Measures GenAI Art. 10 Guided, bounded use (core)
- China GenAI Measures GenAI Art. 14 Stop unlawful generation (core)
- China Deep Synthesis DeepSyn Art. 10 Input and output review (core)
- CSA AICM TVM-13 Guardrails (core)
- CSA AICM AIS-09 Input Validation (core)
- CSA AICM AIS-10 Output Validation (core)
- OWASP LLM LLM01:2026 Prompt Injection (core)
- OWASP LLM LLM10:2026 Improper Output Handling (core)
- Singapore Agentic Agentic 2.3.1 During design and development, use technical controls (core)
- EU AI Act Art. 5 Prohibited AI practices
- EU AI Act Art. 15 Accuracy, robustness and cybersecurity
- China Algo. Rec. AlgoRec Art. 8 Periodic algorithm review
- China Algo. Rec. AlgoRec Art. 9 Feature database for unlawful content
- EU AI Act Art. 5(1)(a)–(b) Manipulative techniques; exploitation of vulnerabilities
- GPAI Code Safety C5 Commitment 5: Safety mitigations
- OWASP LLM LLM06:2026 Unbounded Consumption
- AI Verify 4.8.1 Safety: assess harms and implement measures that prevent the model generating unlawful or harmful content (CSAM, NCII, CBRN, offensive cyber)
Robustness, security and evaluations
- EU AI Act Art. 15 Accuracy, robustness and cybersecurity (core)
- EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk (core)
- NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics (core)
- TC260 Framework 3.0 TC260 3 Technological countermeasures (core)
- TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming (core)
- China Deep Synthesis DeepSyn Art. 15 Technology management and algorithm verification (core)
- China Deep Synthesis DeepSyn Art. 20 Security assessment of new products (core)
- GB/T 45654 GB/T 45654 Security assessment Security-assessment requirements for generative AI services (core) (clause not verified)
- GPAI Code Safety 3.2 Measure 3.2: Model evaluations (core)
- NIST AI RMF MEASURE 2.7 MEASURE 2.7: AI system security and resilience as identified in the MAP function are evaluated and documented (core)
- CSA AICM MDS-06 Adversarial Attack Analysis (core)
- CSA AICM MDS-07 Robustness against Adversarial Attack / Model Hardening (core)
- Singapore GenAI GenAI 5 Testing and Assurance (core)
- Singapore GenAI GenAI 6 Security (core)
- Singapore Agentic Agentic 2.3.2 Before deploying, test agents (core)
- CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified (core)
- OECD AI Principles OECD 1.4 Robustness, security and safety (core)
- G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing (core)
- GAO AI Accountability 3.7 Assessment: assess performance against defined metrics to ensure the AI system functions as intended and is sufficiently robust (core)
- AI Verify 4.1.1 Safety (technical test): run use-case benchmarks and red-teaming to evaluate for safety and possible harms (e.g. hallucination, toxicity) (core)
- AI Verify 6.1.1 Robustness (technical test): compare accuracy with and without added noise to document and monitor performance (core)
- AI Verify 5.2.1 Security: conduct security risk assessments that model threats and risks to the AI system (core)
- EU AI Act Art. 60 Testing of high-risk AI systems in real-world conditions outside AI regulatory sandboxes
- ISO 42001 9.1 Monitoring, measurement, analysis and evaluation
- TC260 Framework 3.0 TC260 5.3.14 Resilience
- China GenAI Measures GenAI Art. 17 Security assessment
- EU AI Act Art. 15(3) Declared accuracy levels and metrics
- EU AI Act Art. 9 Risk management system
- EU AI Act Art. 42(3) Presumption of conformity for cybersecurity (Cyber Resilience Act)
- GPAI Code Safety C6 Commitment 6: Security mitigations
- GDPR Art. 32 Security of processing
- NIST AI RMF MEASURE 2.1 MEASURE 2.1: Test sets, metrics, and details about the tools used during TEVV are documented
- NIST AI RMF MEASURE 1 MEASURE 1: Appropriate methods and metrics are identified and applied
- CSA AICM AIS-05 Application Security Testing
- OWASP LLM LLM01:2026 Prompt Injection
- OWASP Agentic ASI05 Unexpected Code Execution (RCE)
- Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold
- GAO AI Accountability 3.2 Metrics: define performance metrics that are precise, consistent, and reproducible
- AI Verify 5.9.1 Security: release AI systems only after appropriate and effective security checks and evaluation
Sandboxes and real-world testing
- EU AI Act Art. 57 AI regulatory sandboxes (core)
- EU AI Act Art. 60 Testing of high-risk AI systems in real world conditions outside AI regulatory sandboxes (core)
- CoE Convention CoE Art. 13 Safe innovation (controlled testing environments) (core)
- EU AI Act Art. 58 Detailed arrangements for, and functioning of, AI regulatory sandboxes
- EU AI Act Art. 59 Further processing of personal data in the AI regulatory sandbox
- EU AI Act Art. 61 Informed consent to participate in testing in real world conditions
- NIST AI RMF MEASURE 2.3 MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s)
- CSA AICM AIS-13 AI Sandboxing
- Singapore Agentic Agentic 2.3.2 Before deploying, test agents
- TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming
Deployment, change and decommissioning
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems (core)
- NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use (core)
- NIST AI RMF MANAGE 4.1 MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management (core)
- NIST AI RMF GOVERN 1.7 GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness (core)
- CSA AICM AIS-06 Secure Application Deployment (core)
- AI Verify 9.4.1 Accountability: a policy to deactivate, decommission and phase out AI systems safely (core)
- AI Verify 5.14.1 Security: proper and secure disposal of data and models (core)
- AI Verify 6.5.1 Robustness: review changes in code, input data and output data to decide when a new robustness review is due (core)
- EU AI Act Art. 25 Responsibilities along the AI value chain
- EU AI Act Art. 43(4) New conformity assessment on substantial modification
- EU AI Act Art. 20 Corrective actions and duty of information
- EU AI Act Art. 79 Procedure at national level for dealing with AI systems presenting a risk
- EU AI Act Art. 86 Right to explanation of individual decision-making
- ISO 42001 A.9 Use of AI systems
- CSA AICM CCC-01 Change Management Policy and Procedures
- CSA AICM DSP-02 Secure Disposal
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test
- CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified
- OECD AI Principles OECD 1.4 Robustness, security and safety
- TC260 Framework 3.0 TC260 5.3 Operators' safety guidelines
- TC260 Framework 3.0 TC260 5.3.19 Re-assessment on material change
- GAO AI Accountability 4.4 Ongoing assessment: assess the utility of the AI system to ensure its relevance to the current context
- GAO AI Accountability 4.5 Scaling: identify conditions, if any, under which the AI system may be scaled or expanded beyond its current use
- AI Verify 4.6.1 Safety: deployment strategies such as blue-green and canary deployments
Open controls that evidence it
Draft controls in the open control profiles that map to this row: each states a requirement and the evidence it must leave behind.
-
AIGE-CTL-EVAL-008Harness and Configuration Attestation (Evaluation environment profile) -
AIGE-CTL-EVAL-009Evaluation Validity Checks (Evaluation environment profile) -
AIGE-CTL-ASSURE-001Test Plan Frozen Before Evaluation (Assurance and evidence profile) -
AIGE-CTL-ASSURE-003Signed Test Report Against the Plan (Assurance and evidence profile) -
AIGE-CTL-ASSURE-010Model Artefacts Signed at Build and Verified Before Load (Assurance and evidence profile)
Source
Chapter 08, section ISO/IEC 42001, 42005 and 42006, checked against its sources on the review date above.
Machine-readable
- This obligation:
/api/v1/obligations/aige-obl-iso42001-a6.json - The register:
/api/v1/obligations.json· CSV - Schema and stability promise: open data and API
Cite this obligation
García Aibar, J. (2026). ISO 42001 A.6: AI system life cycle (AIGE-OBL-ISO42001-A6). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-iso42001-a6. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{ISO 42001 A.6: AI system life cycle (AIGE-OBL-ISO42001-A6)}},
howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
year = {2026},
version = {0.5.0},
doi = {10.5281/zenodo.22956197},
url = {https://aigovernanceengineer.com/obligations/aige-obl-iso42001-a6},
note = {Version 0.5.0}
}