As artificial intelligence (AI), autonomous systems, and data-driven digital services continue to shape modern economies, policymakers face mounting pressure to design regulatory models that are both effective and adaptable. Traditional regulatory methodologies—including principle-based, rule-based, risk-based, performance-based, co-regulation, and self-regulation—have served the ICT sector well for decades. Yet, when applied to highly dynamic AI ecosystems, these legacy models reveal significant limitations.

This evolving landscape has sparked interest in Granular Regulations, an approach that translates broad policy goals into operational, risk-proportionate, and technically grounded obligations. While detailed regulatory tiers have long existed in financial and safety-critical regulation, their systematic application as a unified hybrid model for AI governance represents a meaningful step forward.

1. Legacy Regulatory Paradigms: Strengths and Structural Limitations

1.0 Other Legacy Regulatory Approaches (High-Level List)

Before focusing on the two central paradigms, it is worth noting that regulatory history includes a range of well-established approaches, such as:

• Risk-based regulation
• Performance or outcome-based regulation
• Co-regulation and self-regulation
• Standards-based regulation
• Command-and-control
• Adaptive regulation and sandboxes

These approaches have shaped modern governance but, like all legacy frameworks, face difficulty addressing the technical depth and contextual variability inherent in AI.

1.1 Principle-Based Regulation (PBR)

Principle-based regulation relies on broad, outcome-oriented statements that set ethical or policy objectives without prescribing the means of achieving them. This model gives regulated entities wide discretion, enabling innovation, sector-specific adaptation, and internal governance mechanisms. However, its strength—flexibility—can also become a fundamental weakness. Because principles are inherently abstract, they often lack the specificity needed for consistent enforcement, objective measurement, or detailed technical auditing.

When applied to AI, the limitations become more pronounced. AI systems operate through probabilistic outputs, complex data interactions, and model behaviors that change over time. High-level principles cannot provide the coarseness required to validate fairness metrics, measure model drift, audit training data, or monitor ongoing risks. As a result, purely principle-based approaches may create interpretational ambiguity, uneven compliance, and difficulty detecting harmful outcomes early.

1.2 Rule-Based Regulation (RBR)

Rule-based regulation establishes explicit, prescriptive requirements that leave little room for interpretation. This model offers legal certainty, strong enforceability, and straightforward compliance measurement. It remains highly effective in traditional telecommunications, infrastructure, and safety-critical environments where technologies are stable, predictable, and well-understood.

However, the rigidity that makes rule-based regulation reliable in static environments becomes a liability for AI. Prescriptive rules cannot keep pace with rapidly evolving AI techniques, emerging risks, and new development methods. Static obligations can quickly become outdated, overbroad, or mismatched to the nuanced behavior of AI models. For example, defining a fixed set of documentation or testing procedures often fails to accommodate variations in architecture, training data, or deployment context. This rigidity may unintentionally hinder innovation or lead to compliance that is procedurally complete but substantively ineffective.

1.3 The Shortcomings of Legacy Models in AI

Taken together, legacy frameworks struggle with AI’s defining characteristics:

• dynamic learning and model evolution
• context-dependent risks
• opacity of model reasoning
• complex interactions between data, algorithms, and deployment environments

This mismatch creates regulatory uncertainty, weak enforceability, and challenges in ensuring accountability. These structural gaps provide the rationale for exploring a more adaptable yet operationally precise model.

2. Granular Regulations: The Hybrid Model for Modern AI Governance

Granular Regulations represent a structured approach that synthesizes the strengths of legacy paradigms while addressing their most significant shortcomings. This model translates high-level principles into operational, risk-tiered, measurable obligations—without adopting the rigidity of fully prescriptive rules.

3. Defining Attributes of Granular Regulations

Granular regulations establish a middle ground by offering precision where needed and flexibility where beneficial. Key attributes include:

• Risk-proportionate detail, where obligations scale with model impact
• Modular technical annexes, allowing updates without legislative overhaul
• Quantitative and qualitative thresholds, covering accuracy, drift, robustness, and bias
• Lifecycle governance, including data provenance, testing intervals, and audit requirements
• Alignment with global standards (ISO/IEC 42001, NIST AI RMF, IEEE, ITU-T)

AI-Specific Examples of Granular Regulation

• High-risk biometric identification systems must undergo pre-deployment stress testing, post-market monitoring, and defined accuracy thresholds.
• Algorithmic credit scoring models must maintain bias measurement audits, with quarterly reporting, subject to independent validation.
• Generative AI models must implement documented safeguards against harmful outputs, with prompt transparency requirements and periodic red-teaming.
• Healthcare diagnostic AI must incorporate drift detection systems, triggering re-certification when accuracy falls below thresholds.

These examples demonstrate how granular, operational detail can coexist with adaptive regulatory mechanisms.

4. Positioning Granular Regulations Among Legacy

Granular regulation is not intended to replace legacy approaches entirely but to bridge them, offering a coherent model better suited to AI’s complexity.

5. Conclusion

AI governance demands regulatory approaches that balance flexibility, accountability, and technical precision. Principle-based and rule-based models—despite their historical value—are insufficient on their own for dynamic, high-impact AI systems. Their limitations in specificity, adaptability, and contextual applicability create gaps that hinder effective oversight.

Granular Regulations provide a modern, hybrid solution. They transform broad principles into enforceable, risk-aligned obligations while avoiding the rigidity of prescriptive rules. Granular regulations incorporate modular updates, lifecycle governance, quantitative metrics, and alignment with global standards—all essential for governing advanced AI, cloud intelligence, IoT autonomy, and emerging ICT ecosystems.

Beyond AI, granular regulation offers a future-ready paradigm for the broader digital landscape, including quantum communications, autonomous IoT infrastructure, digital twins, and complex decentralized networks. Its ability to combine technical detail, regulatory adaptability, and global interoperability positions granular regulation as a practical blueprint for the next generation of ICT governance.