What is AI Regulatory Compliance?

Overview

As AI adoption grows, organizations must comply with an expanding set of regulations governing how AI systems are developed, deployed, and monitored. For engineering teams, AI compliance is closely tied to software supply chain security, with capabilities like Software Bills of Materials (SBOMs),  AI Bills of Materials (AIBOMs), software provenance, and artifact management providing the traceability needed for audit readiness. While closely related, AI governance focuses on internal oversight, whereas AI regulatory compliance addresses external legal and regulatory requirements.

What is AI Regulatory Compliance?

AI regulatory compliance is the process of ensuring AI systems comply with applicable laws, regulations, and industry standards throughout their lifecycle, from development to deployment and ongoing monitoring.

While requirements vary by jurisdiction, most regulations emphasize transparency, accountability, privacy, security, and documented evidence of compliance. Unlike responsible AI initiatives, which are typically voluntary, AI regulatory compliance focuses on meeting legally enforceable obligations.

As regulations continue to evolve, organizations need flexible compliance programs that integrate with existing software development, security, and governance, risk, and compliance (GRC) practices.

What is the Global AI Regulatory Landscape?

There is no single global AI regulation. Instead, organizations must navigate a growing mix of regional laws, voluntary frameworks, and industry-specific requirements.

The European Union’s EU AI Act introduces a comprehensive, risk-based framework for AI systems. Rather than exploring the regulation in depth here, this guide focuses on the broader practices organizations can use to build compliance programs across multiple jurisdictions.

European regulatory frameworks & sectoral mandates

While the EU AI Act introduces a comprehensive, risk-based classification framework specifically governing AI systems and foundational models, organizations building or deploying AI systems in the European Union must align their AI software supply chains with broader security and resilience regulations:

• EU Cyber Resilience Act (CRA): Imposes mandatory cybersecurity requirements for software and hardware products with digital elements. Notably, early reporting obligations take effect on September 11, 2026, requiring vendors to report actively exploited vulnerabilities and severe incidents to ENISA and national CSIRTs within 24 hours.
• NIS2 Directive: Mandates baseline cybersecurity risk management, supply chain oversight, and incident disclosure practices for essential and important entities operating across critical national infrastructure.
• Digital Operational Resilience Act (DORA): Regulates financial institutions and their third-party technology providers, setting strict rules for governance, threat-led penetration testing, and vendor risk management when deploying AI tools in financial services.

What are the Core Components of an Effective AI Compliance Program?

An effective AI regulatory compliance program combines governance, risk management, technical controls, and continuous monitoring throughout the AI lifecycle.

Governance and accountability

Organizations should establish clear ownership, policies, and review processes for AI systems. A strong AI governance program provides the accountability and decision-making framework needed to support regulatory compliance.

Risk assessment and documentation

Risk assessments help identify legal, security, privacy, and operational risks before AI systems reach production. Regulators also expect organizations to maintain documentation such as model inventories, testing results, deployment history, and data lineage. Artifacts like Software Bills of Materials (SBOMs),  AI Bills of Materials (AIBOMs), and software provenance provide valuable evidence for audits and regulatory reporting.

Testing and continuous monitoring

Compliance extends beyond deployment. Organizations should continuously test AI systems, monitor performance and drift, maintain audit logs, and review controls regularly to keep pace with evolving regulations and emerging risks.

Standards and Certifications Relevant to AI Compliance

While regulations define legal obligations, standards and frameworks provide practical guidance for building and maintaining an AI compliance program. Many organizations use these frameworks to establish consistent processes, demonstrate due diligence, and prepare for regulatory audits.

ISO/IEC 42001

ISO/IEC 42001 is the first international standard for Artificial Intelligence Management Systems (AIMS). It provides a structured framework for governing AI throughout its lifecycle, helping organizations establish policies, manage risks, assign responsibilities, and continually improve AI management practices.

For organizations seeking a certifiable AI management framework, ISO/IEC 42001 serves as the leading international benchmark.

NIST AI Risk Management Framework (AI RMF)

The NIST AI Risk Management Framework (AI RMF) is a voluntary framework developed by the U.S. National Institute of Standards and Technology. Rather than prescribing specific technical controls, it helps organizations identify, assess, and manage AI-related risks using four core functions: Govern, Map, Measure, and Manage.

Although not a certification, NIST AI RMF has become one of the most widely adopted frameworks for organizations building mature AI compliance programs.

ISO/IEC 27001

AI systems also depend on strong information security practices. ISO/IEC 27001 provides a globally recognized framework for establishing, implementing, maintaining, and continually improving an Information Security Management System (ISMS).

Many AI compliance programs build upon existing ISO/IEC 27001 controls for areas such as access management, asset inventories, risk assessments, supplier management, incident response, and continuous monitoring.

Rather than viewing these standards independently, many organizations use them together, combining ISO/IEC 42001 for AI management, ISO/IEC 27001 for information security, and NIST AI RMF for practical risk management guidance.

AI Regulatory Compliance Across the Software Supply Chain

Meeting AI regulatory requirements depends on more than policies and documentation. Organizations also need technical evidence that shows exactly what was built, how it was built, and what was ultimately deployed.

Modern AI systems rely on complex software supply chains that include foundation models, open source packages, datasets, containers, APIs, and proprietary code. Without visibility into these components, demonstrating compliance can quickly become difficult.

Machine-readable inventories such as SBOMs and AIBOMs help organizations document the software components, AI models, and dependencies used throughout development. Combined with software provenance, these artifacts provide traceability that supports both security and regulatory reporting.

Compliance also depends on preventing unapproved components from entering production. Policy-driven controls can verify that software packages, models, and dependencies meet organizational requirements before promotion through the CI/CD pipeline. This approach extends secure software development practices such as the Secure Software Development Framework (SSDF) and the SLSA Framework to AI-enabled applications.

Organizations should also address risks introduced by unmanaged AI adoption. Identifying and governing Shadow AI helps reduce the compliance gaps that can arise when employees use unapproved models, tools, or services outside established security and governance processes.

By integrating compliance into the software supply chain, organizations can generate audit-ready evidence while reducing risk before AI systems reach production.

Building an AI Regulatory Compliance Program

Building an effective AI regulatory compliance program is an ongoing process rather than a one-time initiative. As AI technologies and regulations continue to evolve, organizations should establish repeatable processes that can adapt to new requirements while integrating with existing software development practices.

A practical approach includes four key phases:

1. Assess your AI landscape: Inventory AI models, applications, datasets, and supporting software components, then identify which regulations and industry requirements apply to each use case.
2. Implement governance and technical controls: Establish policies for AI development, perform risk assessments, document models and dependencies, and integrate security and compliance checks into development workflows.
3. Scale across the organization: Expand governance, documentation, monitoring, and audit processes to all AI systems in production while standardizing compliance across engineering teams.
4. Continuously improve: Regularly review AI systems, update policies as regulations evolve, conduct internal audits, and train teams on new compliance requirements and emerging risks.

Rather than treating compliance as a separate activity, organizations achieve better outcomes by embedding governance, security, and compliance into the software development lifecycle. This allows compliance evidence to be generated continuously instead of assembled only when an audit occurs.

How JFrog Helps Support AI Regulatory Compliance

AI regulatory compliance depends on having accurate, auditable records of how AI systems are built, secured, and deployed. As organizations scale their use of models, datasets, and AI-powered applications, automating these compliance controls becomes essential to prevent regulatory drift.

The JFrog Platform moves compliance from post-development auditing to active enforcement by applying automated artifact gating throughout the software supply chain. Rather than relying on manual reviews, JFrog automatically intercepts and evaluates AI artifacts before they advance through the CI/CD pipeline:

• Policy-Driven Evaluation: Using capabilities across JFrog Curation and JFrog ML, security and governance teams define policies that automatically evaluate AI models, dependencies, and datasets against security vulnerabilities, license compliance rules, and regulatory standards.
• Automated Pipeline Gating: When an AI package, dataset, or model is ingested or built, the artifact gate validates its metadata and risk profile. If an artifact contains unapproved open-source licenses, critical security flaws, or unverified provenance, the gate automatically blocks it from being stored in binary repositories or promoted downstream.
• Immutable Compliance Evidence: Every time an artifact successfully passes through a gate, the platform captures cryptographic provenance and updates machine-readable inventories like AIBOMs and SBOMs. This ensures that only fully vetted, policy-compliant artifacts enter production, automatically producing an audit-ready paper trail.

By combining centralized asset visibility in the JFrog AI Catalog with continuous, automated artifact gating, engineering teams can stop non-compliant AI components early in the lifecycle without slowing down delivery.

For more information, start a free trial or set up a one-on-one demo of the JFrog Platform today.