Five-Layer AI Governance Framework

• Five-Layer AI Governance Framework is a structured, multi-level model that translates legal and ethical mandates into measurable, operational standards.
• It decomposes AI oversight into five distinct layers—regulations, standards, assessment procedures, tools, and certification—enhancing risk management and compliance.
• The framework facilitates both global harmonization and local adaptability by ensuring traceability and verifiable conformity across AI systems.

A five-layer AI governance framework is a structured, multi-level model that serves to systematically bridge high-level regulatory and ethical imperatives with granular, technical implementation and verification in artificial intelligence systems. This architecture decomposes AI governance into discrete, interoperable layers—each narrowing the scope from broad principles to applied conformity—enabling coordinated oversight and risk management while accommodating sectoral, regional, and global heterogeneity (Agarwal et al., 14 Sep 2025). The framework is motivated by the need to resolve persistent gaps between abstract legal mandates and on-the-ground compliance, facilitating a seamless pathway from regulatory intent to auditable, certifiable AI operations.

1. Layered Structure: Definition and Rationales

The five-layer framework is defined as a stack of governance components with increasing specificity:

1. Laws, Regulations, and Policies: This top layer establishes the overarching legal and ethical boundaries governing AI deployment. Representative sources include the EU AI Act, OECD recommendations, national regulations, and sector-specific policies. Legal mandates at this level codify core values such as safety, fairness, privacy, and human rights. These mandates are essential to ensure a minimum floor for responsible, risk-managed AI development and operation. Ownership of this layer resides primarily with governments and multilateral organizations.
2. Standards: This layer translates broad legal and policy imperatives into actionable, domain-specific requirements. National and international standards bodies (such as ISO/IEC, IEEE, NIST) develop consensus-driven benchmarks (e.g., ISO/IEC 42001 for AI management systems, ISO/IEC TR 24027:2021 for AI bias) that articulate best practices and technical criteria for system design and lifecycle management.
3. Standardized Assessment Procedures: The third layer provides precise, repeatable methodologies for verifying compliance with standards. These can include formalized testing protocols (e.g., IEEE P3198, ITU recommendations on measuring fairness or robustness), lifecycle checklists, and evaluation protocols specific to use cases. The explicit standardization of assessments ensures systematic, comparable, and robust evaluation across contexts.
4. Standardized Assessment Tools and Metrics: This layer consists of operational software libraries (e.g., IBM’s AI Fairness 360, Adversarial Robustness Toolbox), quantitative metrics (such as the Fairness Score, Bias Index), and practical instruments that allow for empirical validation of system properties. These tools enable reproducibility and comparability across implementations, providing an evidence base for the preceding procedural requirements.
5. Certification Ecosystem: The bottom layer institutionalizes formal third-party verification, including self-certification, external audits, and continuous monitoring. Certification programs (such as IEEE CertifAIEd and TUV SÜD) ensure that organizations can signal trustworthy compliance, providing systematic, evidence-based validation to regulators, partners, and the broader public.

This cascading architecture creates a governance pipeline from theory to practice, ensuring that regulatory ambitions are realized through standardized implementation and verifiable conformity (Agarwal et al., 14 Sep 2025).

2. Integration of Regulation, Standards, Assessment, and Certification

The framework achieves its goals through a controlled narrowing of scope and an explicit mapping between key entities at each layer:

• Regulation sets high-level ethical and legal requirements, establishing the permissible operational domain for AI systems.
• Standards disaggregate these general principles into explicit, technical, and process-oriented criteria, using structured normative references to guide design and risk mitigation.
• Assessment Procedures and Tools create repeatable, domain-agnostic and -specific recipes for empirical verification, using automated and auditable metrics.
• Certification ensures that these processes are operationalized in a transparent and reliable manner, providing both ex ante (design-time) and ex post (run-time) assurance.

This structure allows continuous traceability from certification outcomes back to the original regulatory requirements, establishing both legal accountability and technical reproducibility. The bidirectional integration of these layers supports stakeholder engagement, resource planning for compliance, and systematic adaptation as new risks and technologies emerge.

3. Case Study Validation: AI Fairness and Incident Reporting

The paper validates the framework’s applicability through contrasting case studies:

• AI Fairness:

The EU AI Act and related multilateral mandates specify the requirement for fairness via bias mitigation. Standards like ISO/IEC TR 24027:2021 and TSFARAIS operationalize this via measurable criteria. Procedures such as IEEE P3198 and ITU-T SG11, accompanied by tools like AI Fairness 360 and Fairlearn, allow for rigorous, reproducible fairness assessment (e.g., via Fairness Score and Bias Index). In India, regional strategies (National Strategy for AI, Nishpaksh tool) demonstrate adaptation to local priorities while maintaining global benchmarks. The case reveals that, despite global advances, independent certification is often lacking, pinpointing opportunities for further development of trusted verification infrastructure.

• AI Incident Reporting:

Unlike fairness, incident reporting lacks universal legal mandates and has fragmented, non-standardized assessment procedures. There is no shared taxonomy or widely deployed tooling, and certification structures are rudimentary or absent. The five-layer model surfaces these deficiencies at each level and provides a clear roadmap for institutionalizing global reporting standards and third-party verification.

Through these cases, the framework demonstrates its utility in both highlighting gaps—such as the absence of global incident reporting standards—and providing a platform for targeted intervention (Agarwal et al., 14 Sep 2025).

4. Identification and Remediation of Gaps

A salient feature of this model is its ability to reveal “missing links” in both regulation and practice:

Layer	Common Gaps Identified	Framework Intervention
1	Absent or vague legal mandate	Policy advocacy, regulatory harmonization
2	Lack of specific standards	International standard-setting processes
3	Incomplete or ad hoc assessment	Development of standard protocols
4	Inconsistent or unavailable tools	Tool development and open-source release
5	Weak or missing certification	Establishment of independent auditing and certification schemes

Systematic layering allows for modular, targeted updates and resource allocation, especially across divergent jurisdictions or sectors. Once a missing component (such as a standardized assessment for incident reporting) is identified, stakeholders can prioritize the creation or integration of the requisite artifacts.

5. Adaptability Across Jurisdictions and Sectors

The framework is designed to be globally harmonizable yet locally adaptable:

• International Compatibility:

By rooting standards (Layer-2) and assessment procedures in leading organizations (ISO/IEC, IEEE, ITU), the framework ensures a baseline alignment across regions and industries.

• Regional Tailoring:

Local actors (as with India’s Nishpaksh) can customize tools, processes, and even certification priorities to meet unique social, economic, and regulatory environments, without departing from the foundational requirements of upper layers.

• Sectoral Flexibility:

While the approach is validated with fairness (ubiquitous requirement) and incident reporting (sector/region variant), the layered pathway can be extended to other governance challenges such as transparency, accountability, or safety in high-stakes domains.

This structure incentivizes international cooperation and provides guidance for incremental implementation as new technical and legal challenges arise.

6. Practical and Social Implications

For policymakers and industry practitioners, the five-layer model delivers:

• A transparent, stepwise roadmap tying regulatory mandates to measurable implementation steps.
• Explicit cost and resource guidance, enabling targeted support for resource-constrained actors (e.g., SMEs, startups).
• Incentives for harmonization, coordination, and convergence of sectoral and regional governance strategies.
• Assurance mechanisms (through certification) that foster trust, transparency, and public acceptance of AI systems.
• Support for ethical AI use via risk management embedded at every layer, advancing societal benefit and minimizing harm.

For the broader public, standardized and certified assessment engenders confidence that deployed AI reflects both legal compliance and verifiable ethical standards.

7. Distinctive Contributions and Originality

The five-layer framework’s originality lies in:

• The explicit, traceable mapping between the "what" (legal/ethical mandates) and the "how" (technical standards, assessment, tooling, and certification).
• Layered granularity that enables modular upgrades as laws, technologies, and societal expectations evolve.
• Demonstrated empirical validation across governance areas (AI fairness, incident reporting) with clear methodological transferability.
• Inherent flexibility, supporting both international best-practices adoption and region-specific customization, enabling rapid, targeted remediation of compliance and risk management gaps while avoiding regulatory fragmentation.

Diagrammatic Representation

A formal representation of the five-layer structure is:

$$\begin{array}{c} \textbf{Layer 1: Laws, Regulations, and Policies} \ \downarrow \ \textbf{Layer 2: Standards} \ \downarrow \ \textbf{Layer 3: Standardized Assessment Procedures} \ \downarrow \ \textbf{Layer 4: Standardized Assessment Tools and Metrics} \ \downarrow \ \textbf{Layer 5: Certification Ecosystem} \end{array}$$

Each layer ensures validation of requirements from the previous one, establishing a cascaded chain of accountability and operational rigor.

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In summary, the five-layer AI governance framework (Agarwal et al., 14 Sep 2025) provides a comprehensive, structured pathway enabling the sequential translation of regulatory intention into technical reality and verifiable compliance. Through its integration of laws, standards, assessment protocols, quantitative tools, and certification, this model addresses both systemic and practical challenges in global AI governance, enhancing risk management, compliance, and public trust in advanced AI systems.