The Framework Landscape in 2026
AI governance has moved from voluntary guidelines to enforceable standards. Organizations deploying AI agents face a complex landscape of overlapping frameworks: the EU AI Act (mandatory in the EU), NIST AI RMF 1.0 (voluntary but widely adopted in the US), ISO/IEC 42001 (the first international AI management system standard), and sector-specific regulations in healthcare (FDA AI/ML guidance), finance (SEC AI disclosure rules), and defense (DoD AI ethical principles). Most enterprises need to comply with multiple frameworks simultaneously, making a unified governance approach essential.
NIST AI Risk Management Framework
NIST AI RMF 1.0 organizes AI governance into four functions: Govern (establish organizational AI risk management culture and processes), Map (identify and contextualize AI risks), Measure (analyze and assess identified risks), and Manage (prioritize and act on risks based on assessment). DRD maps directly to these functions. The policy engine handles Govern, agent registration and risk classification handle Map, the DRD Score and monitoring handle Measure, and graduated enforcement (warn/block/kill) handles Manage. NIST's framework is voluntary but increasingly referenced in procurement requirements and regulatory guidance.
ISO/IEC 42001: The Certification Standard
ISO/IEC 42001, published in December 2023, is the first certifiable AI management system standard. It follows the Annex SL structure (like ISO 27001 for information security) and requires: an AI policy with leadership commitment, risk assessment specific to AI systems, AI system lifecycle management controls, data quality and bias monitoring, transparency and explainability measures, and continuous improvement through internal audits. Certification by an accredited body provides external validation of your AI governance. DRD's audit trail, compliance reporting, and trust badge system provide evidence for ISO 42001 audits.
EU AI Act vs. NIST vs. ISO: Key Differences
The three major frameworks differ in fundamental ways. The EU AI Act is prescriptive and mandatory — it tells you exactly what to do and enforces it with fines. NIST AI RMF is descriptive and voluntary — it provides a structure for thinking about AI risk without mandating specific controls. ISO 42001 is process-oriented and certifiable — it requires you to have a management system but gives flexibility in implementation. The EU AI Act focuses on risk classification (prohibited, high, limited, minimal). NIST focuses on organizational capability maturity. ISO focuses on management system completeness. An organization that implements all three has the most robust governance posture.
Building a Unified Compliance Program
Rather than treating each framework separately, DRD recommends a unified compliance approach. Start with ISO 42001 as the management system foundation. Map EU AI Act requirements to specific ISO 42001 controls. Use NIST AI RMF functions as the operational structure within the ISO management system. Layer sector-specific requirements on top as additional controls. DRD's governance platform supports this unified approach through customizable policy templates that map to multiple frameworks, a compliance dashboard showing status across all active frameworks, automated evidence collection for audit preparation, and trust badges that indicate multi-framework compliance.
DRD's Framework Alignment Engine
DRD's Framework Alignment Engine automatically maps your agent governance configuration to multiple compliance frameworks. When you define a policy, the engine identifies which framework requirements it satisfies, flags gaps where additional controls are needed, and generates compliance reports formatted for each framework's audit requirements. For example, a single DRD policy that requires human oversight for high-value agent decisions simultaneously satisfies EU AI Act Article 14 (human oversight), NIST AI RMF Manage function (risk response), and ISO 42001 Annex A Control A.10.4 (human intervention capability). One policy, three frameworks covered.
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