AI Frameworks → Business Frameworks

How It Maps

COSO ERM: Each trustworthiness characteristic (validity, safety, fairness, accountability) defines a risk category that COSO ERM should assess. This framework tells you WHAT to measure for AI risk; COSO ERM tells you HOW to manage the risks you find.

NIST CSF: Same NIST family. AI 100-1's security and resilience characteristics extend CSF's cybersecurity functions to AI-specific concerns — adversarial robustness, model integrity, and secure deployment. Your security team can read both.

ISO 27001: AI 100-1's security and privacy characteristics map to ISO 27001 control objectives. If you're 27001 certified, you have the management system — AI 100-1 tells you which AI-specific security risks to plug into it.

ISO 9001: AI 100-1's validity, reliability, and accuracy characteristics ARE quality management applied to AI. Same measurement rigor, same continuous improvement discipline, same documentation requirements — applied to model outputs instead of manufacturing outputs.

COBIT: AI 100-1's accountability and transparency characteristics align with COBIT's governance objectives. Both require clear roles, oversight structures, and documentation for technology-driven decisions.

CMMI: AI 100-1's systematic approach to defining and measuring trustworthiness maps naturally to CMMI maturity levels. You can assess your organization's AI trustworthiness maturity using the same progression: initial, managed, defined, quantitatively managed, optimizing.

Strong Mappings

COSO ERM: ISO 42001's AI management system (Plan-Do-Check-Act) integrates naturally with COSO ERM. Both use risk-based governance with assessment, treatment, and monitoring cycles.

ISO 27001: Same ISO management system structure (PDCA). If you've implemented ISO 27001, you know exactly how to implement 42001 — same certification model applied to AI instead of information security.

ISO 9001: Quality management system structure mirrors AIMS. Continuous improvement, process control, documentation, management review — all the same disciplines, applied to AI management.

COBIT: Both are governance frameworks with complementary scope. COBIT governs IT broadly; ISO 42001 governs AI specifically. Nest ISO 42001 AI governance within your COBIT IT governance structure.

GDPR: Data protection requirements within the AI management system map directly to GDPR principles. Same data handling, consent, purpose limitation, and individual rights frameworks.

CMMI: Process maturity model for AI capabilities. Same maturity progression: initial, managed, defined, quantitatively managed, optimizing. CMMI provides the maturity assessment framework for 42001 implementation.

Strong Mappings

COSO ERM: EU AI Act's risk classification tiers (unacceptable, high, limited, minimal) directly feed COSO ERM risk assessment. Organizations already doing ERM can classify AI systems within their existing risk taxonomy.

GDPR: Same regulatory jurisdiction, same enforcement philosophy (risk-based, rights-focused), same DPA oversight. If you've built GDPR compliance, you have the regulatory framework for the AI Act.

Strong Mappings

COSO ERM: OECD's accountability, robustness, and transparency principles inform what risks COSO ERM should cover for AI. Both emphasize governance structures and executive accountability.

Strong Mappings

ISO 27001: Security and privacy standards for autonomous systems map directly to information security management. Same control families applied to AI-specific threats and design requirements.

ISO 9001: IEEE's quality-by-design principles for AI mirror ISO 9001 quality management requirements. Same design control discipline applied to ethical AI system engineering.

NIST CSF: IEEE's safety and security standards align directly with NIST CSF's cybersecurity framework functions. Same security posture management applied to AI-specific systems.

GDPR: IEEE's privacy and data agency principles map directly to GDPR's data protection rights. Both emphasize individual control over personal data in automated systems.

How It Maps

COSO ERM: EO 14110 requires federal agencies to conduct AI risk assessments and establish governance structures — the same risk identification, assessment, and response cycle COSO ERM prescribes. If you already run enterprise risk management, the EO's requirements slot into your existing risk framework.

NIST CSF: The EO directs NIST to develop AI safety and security standards, building directly on NIST's existing cybersecurity work. CSF's Identify/Protect/Detect/Respond/Recover functions extend naturally to the AI security requirements the EO mandates.

ISO 27001: EO 14110's security requirements for AI systems (particularly dual-use foundation models) align with ISO 27001 controls. Your existing ISMS covers many of the security expectations — extend it to AI-specific threats.

SOX: For publicly traded AI companies, the EO's transparency and reporting requirements create additional disclosure considerations that complement SOX's financial control framework.

How It Maps

COSO ERM: Singapore's governance framework mirrors COSO ERM's structure: establish governance, assess risk, implement controls, monitor performance. The framework's four focus areas (internal governance, determining AI decision-making, operations management, stakeholder communication) map to COSO's governance, risk assessment, control activities, and monitoring components.

COBIT: Both are governance-focused. Singapore's accountability structures, role definitions, and oversight mechanisms parallel COBIT's governance objectives. If you've implemented COBIT IT governance, Singapore's AI governance slots into the same structure.

ITIL: Singapore's operations management guidance (monitoring, retraining, incident handling for AI) overlaps with ITIL's service operation and continual improvement practices. Your IT service management practices extend to AI operations.

PMBOK: Singapore includes practical implementation guidance — stakeholder analysis, risk assessment, resource planning — that aligns with PMBOK knowledge areas. Useful for organizations treating AI governance implementation as a program.

Prosci: Singapore's emphasis on stakeholder communication and organizational readiness for AI aligns with Prosci's change management approach. The framework recognizes that governance adoption requires the same people-side management as any organizational change.

How It Maps

COSO ERM: ISO 23894 explicitly extends ISO 31000 risk management to AI. COSO ERM and ISO 31000 are the two dominant enterprise risk frameworks globally — they share the same DNA (risk identification, analysis, evaluation, treatment, monitoring). If you run COSO ERM, ISO 23894 tells you which AI-specific risks to add to your existing registers and assessments.

ISO 27001: ISO 23894's AI security risk categories plug into ISO 27001's security controls. Your existing security risk assessment process gains AI-specific risk scenarios: model tampering, training data poisoning, adversarial inputs.

ISO 9001: ISO 23894's quality-related AI risks (accuracy, reliability, validity) complement ISO 9001's quality management. Both use the ISO management system structure — same documentation, review, and improvement rhythms.

SOX: When AI systems participate in financial processes, ISO 23894's risk assessment approach helps identify which AI risks could impact financial reporting integrity — feeding directly into SOX risk assessment.

PMBOK: Both ultimately derive from ISO 31000 risk management principles. PMBOK's risk management knowledge area uses similar probability-impact assessment methodologies. Same risk language, different scope (project vs. AI system).