Introduction

In the evolving landscape of cybersecurity and artificial intelligence (AI), executive orders, regulatory frameworks, and governance policies are proliferating and escalating in complexity. Despite their critical role in shaping national and organizational risk posture, these directives remain almost universally authored in natural-language formats, making them difficult to translate into actionable workflows for engineering teams and limiting automation in compliance systems. Governance Schema (GovSCH) is designed to address this disconnect by introducing a standardized, machine-readable schema for authoring cyber and AI governance documents.

The United States has issued multiple major executive orders to guide cybersecurity and AI governance. For example, Executive Order (EO) 14028 set forth secure software development requirements and evidence-based compliance artifacts; EO 14144 extended the focus into AI, quantum-resistant cryptography, and the launch of rules‑as‑code pilots.¹ A further amendment in EO 14306 reiterated the government’s intent to adopt rules‑as‑code, requiring agencies to publish machine-readable versions of policy guidance by mid‑2026.² However, despite these initiatives, most executive orders and frameworks still rely on prose, making systematic automation and interpretation difficult.

This creates a triad of challenges, such as:

• Misalignment and ambiguity: Engineering teams often struggle to derive technical requirements from narrative policy documents, which delays implementation and increases risk.³
• Manual translation burden: Compliance functions frequently engage in labor-intensive interpretation to translate policies into technical artifacts and control mappings.⁴
• Fragmented development: Without a consistent structure across documents, frameworks authored by the National Institute of Standards and Technology (NIST), Office of Management and Budget (OMB), Cybersecurity and Infrastructure Security Agency (CISA), and others are often incompatible or duplicative.⁵

Government and industry have recognized policy-as-code as an essential compliance imperative; organizations that fail to adopt early will face higher costs and reduced reliability.⁶ For instance, Australia’s adoption of rules-as-code has shown transformative efficiency gains (such as greater transparency, version control, and auditability) in compliance and legislative interpretation.⁷

GovSCH introduces a community-founded, open-source JSON (JavaScript Object Notation) schema for authoring cyber and AI governance documents. It defines key structural elements, governance intent, policy objectives, dependencies, actors, timelines, controls, and rationale in a consistent machine-readable format, and it achieves the following:

• Bridges policy and engineering: GovSCH enables frameworks and engineering teams to “read” policy directly by encoding high-level intent in structured data, similar to how Open Security Controls Assessment Language (OSCAL) encodes control-level guidance.⁸
• Supports rules-as-code pilots: As agencies like NIST and CISA begin to pilot rules-as-code implementations, GovSCH offers a production-ready schema that aligns with those mandates and accelerates adoption.⁹
• Promotes transparency and reuse: Policy authors can reuse, extend, and version GovSCH-compliant documents, improving comparability across jurisdictions, faster onboarding, and better traceability.

GovSCH is built for broad scalability and ease of adoption for:

• Policy and regulatory agencies: Agencies drafting executive orders or regulations can author GovSCH-compliant documents from inception and integrate them into legislative and policy life cycles.
• Standards bodies: International standards organizations and regulatory bodies—including European Union legislative frameworks like Network and Information Security Directive (NIS2) and Digital Operational Resilience Act (DORA)—can adopt GovSCH for cross-jurisdictional coordination.
• Technology implementers: Tool vendors; open-source compliance platforms; governance, risk, and compliance (GRC) systems; and infrastructure-as-code frameworks can incorporate GovSCH as a policy input format.
• Global community and open source: As an open-source standard, GovSCH invites contributions from academia, industry, government, and standards groups to refine its structure and broaden its applicability, similar to open schema standards for AI incident reporting.

The concept mirrors recent academic proposals in AI governance, such as the unified control framework, which consolidates controls across risk scenarios and regulatory regimes into a structured taxonomy.¹⁰ Similarly, GovSCH can be a foundation for harmonizing how entities translate executive intent across diverse governance contexts.

Policymakers and Government Agencies

• Clarity of structure: GovSCH imposes a minimum, consistent authoring template as the baseline for policy creation to ensure machine readability, reduce ambiguities, and avoid misinterpretations in policy language.
• Efficient updates and batch mapping: Policy changes like updating compliance timelines and scope can be applied programmatically across GovSCH files.
• Interoperability: Policies authored in GovSCH can be tracked across versions and compared, thereby supporting auditability and iterative collaboration.

Framework Authors and Standards Developers

• Automated mapping: Framework authors can programmatically ingest GovSCH documents to derive mappings to controls (as seen in documents from NIST and International Organization for Standardization, or ISO, frameworks).
• Reduced duplication: By standardizing how intent is expressed, GovSCH enables the reuse of familiar patterns, improving consistency across frameworks.
• Dynamic rule creation: Standard tools can generate rules or guidance automatically from GovSCH definitions, accelerating development cycles.

Engineering Teams and Compliance Operations

• Acceleration of compliance workflows: Instead of manually deciphering policy documents, teams can feed GovSCH input into compliance-as-code pipelines, shipping faster and with fewer errors.
• Improved traceability: Each control, requirement, or objective can be traced back to high-level policy rationale encoded in GovSCH metadata.
• Scalability for transformation: Large organizations managing many policies and jurisdictions can programmatically scale compliance pipelines by applying GovSCH schemas.¹¹

Recent academic studies on automating policy analysis using large language models (LLMs) showed efficiency gains (such as comprehensive coverage and reduced duplication) when structured inputs could be extracted from high-level documents.¹² GovSCH builds on that principle at authoring time, not analysis time, enabling policy documents to be structurally analyzable from their inception.

GovSCH provides a critical missing piece in the emerging policy-as-code and machine-readable governance ecosystem. Offering a scalable, open-source JSON schema for cyber and AI governance documents reduces friction between policymakers, standards developers, and technical implementers. In doing so, it:

• Supports rules-as-code mandates coming into force by mid‑2026;
• Provides a consistent foundation for cross-domain mapping, automation, and compliance; and
• Enables global collaboration around governance structures, control taxonomies, and regulatory frameworks.

GovSCH is a schema and potential standard promoting transparency, efficiency, and interoperability in cyber and AI governance. As agencies move toward machine-readable policies, GovSCH can be the foundational convention for policy authoring, translation, and implementation. Its open-source nature ensures it can evolve through community contributions, supporting emerging needs across industries and jurisdictions.

The value of GovSCH lies in its potential to reduce ambiguity and accelerate the operationalization of complex cybersecurity and AI policies. By offering standardized, machine-readable structures for EOs, frameworks, and regulations, GovSCH could enable policymakers, engineers, and compliance teams to work from a common source of truth.

For example, imagine a federal contractor tasked with implementing requirements from multiple overlapping EOs, each written in dense legal prose and open to interpretation. Today, engineering teams must manually extract responsibilities and timelines, often resulting in delays and inconsistent compliance. With GovSCH, those same orders could be instantly parsed into structured JSON/YAML formats, allowing automated compliance systems to flag deadlines, assign responsible entities, and map directives to technical tasks.