Singapore IMDA Model AI Governance Framework for Generative AI (voluntary)

Governance dimensions incl. testing, transparency, incident reporting, security and content provenance

By Jorge García Aibar Reviewed 2026-09-24

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-SG-GENAI. Drawn from chapter 08.

Text alternative

Clause: Singapore GenAI, Model AI Governance Framework….
Duty holder: Not stated.
Applies from: No date, Voluntary.
Artefact: Eval suite.
Layers: Layer 02, Layer 03, Layer 04.
Evidence record: Evidence record v1.
Record schema: Evidence record.
The same topic in 24 other frameworks; the crosswalk section below links each clause.

Id: AIGE-OBL-SG-GENAI
Instrument: Singapore Model AI Governance Framework for Generative AI framework
Clause: Model AI Governance Framework for Generative AI
Applies from: No date Voluntary · published May 2024

The artefact that evidences it

Eval suite; model cards; content provenance and watermarking.

Patterns that build it

No pattern in the catalogue names this clause on its "Maps to" line yet; the artefact above is the engineering answer.

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.

Governance and accountability

EU AI Act Art. 17 Quality management system (core)
ISO 42001 5.1 Leadership and commitment (core)
ISO 42001 5.2 AI policy (core)
ISO 42001 5.3 Roles, responsibilities and authorities (core)
ISO 42001 A.2 Policies related to AI (core)
ISO 42001 A.3 Internal organization (core)
NIST AI RMF GOVERN 1 GOVERN 1: Policies, processes, procedures, and practices across the organization related to the mapping, measuring, and managing of AI risks are in place, transparent, and implemented effectively (core)
NIST AI RMF GOVERN 2 GOVERN 2: Accountability structures are in place so that the appropriate teams and individuals are empowered, responsible, and trained (core)
TC260 Framework 3.0 TC260 4 Comprehensive governance measures (core)
TC260 Framework 3.0 TC260 5.3.12 Traceable chain of responsibility (core)
China GenAI Measures GenAI Art. 9 Provider responsibility as content producer (core)
China Algo. Rec. AlgoRec Art. 7 Algorithm-security responsibility system (core)
China Deep Synthesis DeepSyn Art. 7 Information-security responsibility system (core)
GDPR Art. 5(2) Accountability (core)
ISO 42001 9.3 Management review (core) (clause not verified)
CSA AICM GRC-01 Governance Program Policy and Procedures (core)
CSA AICM GRC-06 Governance Responsibility Model (core)
UK ATRS ATRS 2.1 Owner and responsibility (core)
Singapore Agentic Agentic 2.2.1 Clear allocation of responsibilities within and outside the organisation (core)
OECD AI Principles OECD 1.5 Accountability (core)
GAO AI Accountability 1.2 Roles and responsibilities: define clear roles, responsibilities, and delegation of authority for the AI system (core)
EU AI Act Art. 4 AI literacy
EU AI Act Art. 87 Reporting of infringements and protection of reporting persons
ISO 42001 7.2 Competence (clause not verified)
ISO 42001 9.2 Internal audit (clause not verified)
ISO 42001 10.1 Continual improvement (clause not verified)
NIST AI RMF GOVERN 4 GOVERN 4: Organizational teams are committed to a culture that considers and communicates AI risk
NIST AI RMF GOVERN 5 GOVERN 5: Processes are in place for robust engagement with relevant AI actors
GPAI Code Safety C8 Commitment 8: Systemic risk responsibility allocation
Korea AI Act Art. 36 Domestic representative
CoE Convention CoE Art. 9 Accountability and responsibility
G7 Code G7 Action 5 Develop, implement and disclose AI governance and risk-management policies
EN 18286 EN 18286 Quality management system for EU AI Act regulatory purposes
GAO AI Accountability 1.1 Clear goals: define clear goals and objectives for the AI system
GAO AI Accountability 1.3 Values: demonstrate a commitment to values and principles established by the entity

Data governance

EU AI Act Art. 10 Data and data governance (core)
ISO 42001 A.7 Data for AI systems (core)
TC260 Framework 3.0 TC260 2.1.3 Data safety risks (core)
China GenAI Measures GenAI Art. 7 Training-data lawful sourcing (core)
China GenAI Measures GenAI Art. 8 Data-annotation standards (core)
China GenAI Measures GenAI Art. 11 Protection of user input and records (core)
China Deep Synthesis DeepSyn Art. 14 Training-data management (core)
GB/T 45654 GB/T 45654 Corpus security Training-corpus (data) security requirements (core) (clause not verified)
EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
GDPR Art. 5(1)(c) Data minimisation (core)
GDPR Art. 25 Data protection by design and by default (core)
ISO 42001 A.7.3 Acquisition of data (core) (clause not verified)
CSA AICM DSP-20 Data Provenance and Transparency (core)
UK ATRS ATRS 2.4.3 Development data specification (core)
GAO AI Accountability 2.1 Sources: document sources and origins of data used to develop the models (core)
GAO AI Accountability 2.2 Reliability: assess reliability of data used to develop the models (core)
EU AI Act Art. 4a Special-category data for bias detection
ISO 42001 A.4 Resources for AI systems
NIST AI RMF MAP 2 MAP 2: Categorization of the AI system is performed
NIST AI RMF MEASURE 2.10 MEASURE 2.10: Privacy risk of the AI system is examined and documented
NIST AI RMF MEASURE 2.11 MEASURE 2.11: Fairness and bias are evaluated and results are documented
TC260 Framework 3.0 TC260 5.1 Model R&D safety guidelines
EU AI Act Art. 53 Obligations for providers of general-purpose AI models
EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations
EU AI Act Art. 5(1)(e) Prohibited: untargeted scraping of facial images
GPAI Code Copyright 1.1–1.5 Commitment 1: Copyright policy (Measures 1.1 to 1.5)
GDPR Art. 9 Processing of special categories of personal data
CSA AICM DSP-21 Data Poisoning Prevention & Detection
OWASP LLM LLM05:2026 Data and Model Poisoning
GAO AI Accountability 2.4 Variable selection: assess data variables used in the AI component models
GAO AI Accountability 2.5 Enhancement: assess the use of synthetic, imputed, and/or augmented data

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.6 AI system life cycle (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)
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)
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

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)
ISO 42001 A.6 AI system life cycle (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 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)
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

Incident response and monitoring

EU AI Act Art. 72 Post-market monitoring by providers and post-market monitoring plan (core)
EU AI Act Art. 73 Reporting of serious incidents (core)
ISO 42001 A.8 Information for interested parties (core)
ISO 42001 10.2 Nonconformity and corrective action (core)
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 (core)
TC260 Framework 3.0 TC260 5.3.7 Real-time risk monitoring (core)
TC260 Framework 3.0 TC260 5.3.18 Incident reporting (core)
China GenAI Measures GenAI Art. 14 Handle and report unlawful content (core)
China GenAI Measures GenAI Art. 15 Complaint and reporting mechanism (core)
EU AI Act Art. 26(5) Deployer monitoring, informing the provider and suspending use (core)
GPAI Code Safety C9 Commitment 9: Serious incident reporting (core)
GDPR Arts. 33–34 Notification and communication of a personal data breach (core)
NIST AI RMF MANAGE 4.3 MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented (core)
CSA AICM SEF-07 Incident Management and Response (core)
CSA AICM SEF-08 Security Breach Notification (core)
Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold (core)
Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test (core)
G7 Code G7 Action 2 Identify and mitigate vulnerabilities, incidents and misuse after deployment (core)
G7 Code G7 Action 4 Responsible information sharing and reporting of incidents (core)
GAO AI Accountability 4.1 Planning: develop plans for continuous or routine monitoring of the AI system (core)
GAO AI Accountability 4.2 Drift: establish the range of data and model drift that is acceptable (core)
EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk
TC260 Framework 3.0 TC260 App. 2 II.6 Emergency plans
China Algo. Rec. AlgoRec Art. 7 Security management and emergency response
EU AI Act Art. 3(49) Definition of serious incident
EU AI Act Art. 20 Corrective actions and duty of information
GPAI Code Safety 3.5 Measure 3.5: Post-market monitoring
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
NIST AI RMF GOVERN 4.3 GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing

Privacy and data protection

GDPR Art. 5 Principles relating to processing of personal data (core)
GDPR Art. 6 Lawfulness of processing (core)
GDPR Art. 25 Data protection by design and by default (core)
NIST AI RMF MEASURE 2.10 MEASURE 2.10: Privacy risk of the AI system as identified in the MAP function is examined and documented (core)
CSA AICM DSP-08 Data Privacy by Design and Default (core)
OWASP LLM LLM02:2026 Sensitive Information Disclosure (core)
CoE Convention CoE Art. 11 Privacy and personal data protection (core)
China GenAI Measures GenAI Art. 7(3) Consent or another lawful basis for personal information in training data (core)
China GenAI Measures GenAI Art. 11 Protection of user input and records (core)
GAO AI Accountability 2.8 Security and privacy: assess data security and privacy for the AI system (core)
EU AI Act Art. 59 Further processing of personal data in the AI regulatory sandbox
EU AI Act Art. 4a Special-category data for bias detection
GDPR Art. 35 Data protection impact assessment
ISO 42001 A.7 Data for AI systems
CSA AICM DSP-22 Privacy Enhancing Technologies
UK DUAA UK GDPR Art. 22B Restrictions on automated decision-making
OECD AI Principles OECD 1.2 Rule of law, human rights and democratic values, including fairness and privacy
G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property

Explainability and right to explanation

EU AI Act Art. 86 Right to explanation of individual decision-making (core)
EU AI Act Art. 13(3)(b)(iv)–(v) Information relevant to explain output; performance for specific persons or groups (core)
GDPR Art. 15(1)(h) Meaningful information about the logic involved (core)
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 (core)
CSA AICM GRC-13 Explainability Requirement (core)
CSA AICM GRC-14 Explainability Evaluation (core)
Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview (core)
UK DUAA UK GDPR Art. 22C Safeguards for automated decision-making (core)
OECD AI Principles OECD 1.3 Transparency and explainability (core)
GDPR Art. 13(2)(f) Existence of automated decision-making
GDPR Art. 22(3) Right to obtain human intervention and to contest the decision
ISO 42001 A.8.2 System documentation and information for users (clause not verified)
NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability as identified in the MAP function are examined and documented
UK ATRS ATRS 2.3.5 Appeals and review
CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities
China Algo. Rec. AlgoRec Art. 17 Explain where an algorithm significantly affects user rights

AI literacy and competence

EU AI Act Art. 4 AI literacy (core)
ISO 42001 7.2 Competence (core) (clause not verified)
NIST AI RMF GOVERN 2.2 GOVERN 2.2: The organization's personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements (core)
CSA AICM HRS-14 AI Competency Training (core)
Singapore Agentic Agentic 2.4 Enable end-user responsibility (core)
EU AI Act Art. 26(2) Oversight by people with the competence, training and authority it needs
EU AI Act Art. 95(2)(c) Codes of conduct: promoting AI literacy
GDPR Art. 39(1)(b) DPO tasks: awareness-raising and training of staff (clause not verified)
ISO 42001 7.3 Awareness (clause not verified)
NIST AI RMF MAP 3.4 MAP 3.4: Processes for operator and practitioner proficiency with AI system performance and trustworthiness, and relevant technical standards and certifications, are defined, assessed, and documented
CSA AICM HRS-11 Security Awareness Training
UK ATRS ATRS 2.3.4 Required training
China GenAI Measures GenAI Art. 10 Guide users to understand and use generative AI rationally
GAO AI Accountability 1.4 Workforce: recruit, develop, and retain personnel with multidisciplinary skills and experiences

Conformity assessment and certification

EU AI Act Art. 43 Conformity assessment (core)
ISO 42006 ISO/IEC 42006 Requirements for bodies providing audit and certification of AI management systems (core)
CSA AICM A&A-02 Independent Assessments (core)
EU AI Act Art. 47 EU declaration of conformity
EU AI Act Art. 48 CE marking
EU AI Act Art. 40 Harmonised standards and standardisation deliverables
GDPR Art. 42 Certification (clause not verified)
ISO 42001 9.2 Internal audit (clause not verified)
NIST AI RMF MEASURE 1.3 MEASURE 1.3: Internal experts who did not serve as front-line developers for the system and/or independent assessors are involved in regular assessments and updates
CSA AICM A&A-04 Requirements Compliance
Korea AI Act Art. 33 Confirmation of high-impact AI
EN 18286 EN 18286 Quality management system for EU AI Act regulatory purposes
China GenAI Measures GenAI Art. 17 Security assessment and algorithm filing
GAO AI Accountability 1.8 Compliance: ensure the AI system complies with relevant laws, regulations, standards, and guidance

GPAI and foundation models

EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
EU AI Act Art. 55 Obligations of providers of general-purpose AI models with systemic risk (core)
GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
GPAI Code Safety C1 Commitment 1: Safety and Security Framework (core)
Korea AI Act Art. 32 Safety duties for AI above the compute threshold (core)
EU AI Act Art. 51 Classification of general-purpose AI models as general-purpose AI models with systemic risk
EU AI Act Art. 56 Codes of practice
GPAI Code Safety 3.2 Measure 3.2: Model evaluations
GPAI Code Safety C7 Commitment 7: Safety and Security Model Reports
CSA AICM MDS-12 Open Model Risk Assessment
CSA AICM MDS-03 Model Documentation
G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing
China GenAI Measures GenAI Art. 7 Lawful data and foundation-model sources

IP and copyright

EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations (core)
GPAI Code Copyright 1.1 Draw up, keep up-to-date and implement a copyright policy (core)
GPAI Code Copyright 1.2 Reproduce and extract only lawfully accessible copyright-protected content (core)
GPAI Code Copyright 1.3 Identify and comply with rights reservations when crawling the World Wide Web (core)
GPAI Code Copyright 1.4 Mitigate the risk of copyright-infringing outputs (core)
NIST AI RMF GOVERN 6.1 GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third-party's intellectual property or other rights (core)
G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property (core)
China GenAI Measures GenAI Art. 7(2) No infringement of IP rights in training data (core)
EU AI Act Art. 53(1)(d) Public summary of the content used for training
GPAI Code Copyright 1.5 Designate a point of contact and enable the lodging of complaints
NIST AI RMF MAP 4.1 MAP 4.1: Approaches for mapping AI technology and legal risks of its components, including the use of third-party data or software, are in place, followed, and documented, as are risks of infringement of a third party's intellectual property or other rights
CSA AICM DSP-20 Data Provenance and Transparency
China GenAI Measures GenAI Art. 4(3) Respect IP rights and business ethics

Content provenance and deepfakes

EU AI Act Art. 50(2) Machine-readable marking of synthetic content (core)
EU AI Act Art. 50(4) Disclosure of deep fakes (core)
Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
G7 Code G7 Action 7 Deploy content authentication and provenance mechanisms where feasible (core)
China AI Labelling Label Art. 4 Explicit labels for generated content (core)
China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
EU AI Act Art. 3(60) Definition of deep fake
CSA AICM MDS-09 Model Signing/Ownership Verification
OWASP LLM LLM07:2026 Misinformation
China GenAI Measures GenAI Art. 12 Labelling of generated content

Environmental impact

EU AI Act Annex XI 1(2)(e) Known or estimated energy consumption of the GPAI model (core)
GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
NIST AI RMF MEASURE 2.12 MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities as identified in the MAP function are assessed and documented (core)
OECD AI Principles OECD 1.1 Inclusive growth, sustainable development and well-being (core)
EU AI Act Art. 40(2) Standardisation deliverables on energy and resource performance
EU AI Act Art. 95(2)(b) Codes of conduct: environmental sustainability

Source

Chapter 08, section Other jurisdictions, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-sg-genai.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). Singapore IMDA Model AI Governance Framework for Generative AI (voluntary) (AIGE-OBL-SG-GENAI). 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-sg-genai. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{Singapore IMDA Model AI Governance Framework for Generative AI (voluntary) (AIGE-OBL-SG-GENAI)}},
  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-sg-genai},
  note         = {Version 0.5.0}
}