Centraleyes AI Framework (CAIF)

What is the CAIF?

The Centraleyes AI Framework (CAIF) is a comprehensive compliance and governance tool designed to help organizations meet the diverse and rapidly evolving regulatory requirements surrounding artificial intelligence. It consolidates questions and controls from multiple AI laws and regulatory regimes across the globe – including the EU AI Act (Minimal and Limited Risk categories), the South Korea AI Act, the California AI Law, the Colorado AI Act, and the China AI Regulations – into a single, unified questionnaire.

By answering this one comprehensive questionnaire, organizations can assess their alignment with AI requirements across multiple jurisdictions without needing to navigate each law independently. The CAIF provides a standardized and centralized framework that streamlines compliance, mitigates regulatory risk, and supports responsible AI implementation and oversight.

As global AI regulations continue to expand, the CAIF addresses the growing need for a consistent approach to compliance. It aligns with the core principles of AI governance – transparency, accountability, fairness, data quality, human oversight, and risk management – ensuring that organizations can deploy AI responsibly and in compliance with both local and international laws.

What Topics Does the CAIF Include?

The CAIF covers the key domains of AI governance and compliance, reflecting the core requirements of global AI regulatory frameworks.

1. AI Governance and Accountability

• Risk Classification: Identify and categorize AI systems based on risk levels (e.g., minimal, limited, high, unacceptable).
• Accountability Structures: Define roles and responsibilities for AI governance, including oversight by senior leadership.
• AI Policy Frameworks: Establish policies for ethical AI design, development, and use.

2. Transparency and Explainability

• AI Disclosures: Provide clear information about the purpose, capabilities, and limitations of AI systems.
• Explainability Mechanisms: Ensure users and regulators can understand AI outputs and decision-making logic.
• Human Oversight: Maintain meaningful human involvement in AI-driven decisions, particularly for high-impact use cases.

3. Data Management and Quality

• Training Data Integrity: Ensure datasets are representative, accurate, and free from bias.
• Data Governance: Implement standards for data provenance, labeling, and validation.
• Data Protection: Align AI data processing with privacy and cybersecurity requirements.

4. Bias, Fairness, and Ethical Design

• Bias Detection: Regularly test for and mitigate discriminatory outcomes.
• Fairness Metrics: Define measurable fairness objectives and track progress.
• Ethical Review: Integrate ethical review processes into AI development lifecycles.

5. Security and Technical Robustness

• Adversarial Resilience: Safeguard AI systems against manipulation and misuse.
• System Testing: Conduct pre-deployment and ongoing testing to ensure reliability.
• Incident Response: Establish procedures for identifying, reporting, and remediating AI-related incidents.

6. Compliance and Monitoring

• Documentation: Maintain records of system design, testing, and risk assessments.
• Audits and Reporting: Support internal and external audits of AI systems.
• Continuous Improvement: Monitor for regulatory updates and adapt governance practices accordingly.

The Importance of Technical and Organizational Controls

The CAIF integrates foundational cybersecurity and risk management principles – drawing from standards like CIS Controls, NIST AI RMF, and ISO/IEC 42001 – to ensure that AI systems are secure, transparent, and trustworthy. These controls establish a baseline for responsible AI management:

Technical Controls

• Secure AI development environments and version control systems.
• Continuous vulnerability assessment and patch management.
• Data encryption, access control, and monitoring for AI assets.

Organizational Controls

• AI risk management policies and review boards.
• Governance structures for AI lifecycle management.
• Procedures for third-party AI system evaluation and vendor oversight.

By integrating these controls, organizations can enhance their AI resilience, maintain compliance, and demonstrate due diligence in AI risk management.

Policies and Procedures Relevant to AI Compliance

To support AI governance and compliance, organizations must establish clear policies and procedures that define how artificial intelligence is developed, managed, and monitored throughout its lifecycle. The Centraleyes AI Framework (CAIF) provides detailed guidance and top-of-the-line templates for the essential AI policies every organization should maintain. These policies ensure consistency, accountability, and compliance with global AI laws and ethical standards.

• AI Governance and Accountability Policy
  • What it is: Establishes the organizational structure, responsibilities, and oversight mechanisms for AI systems, including the roles of AI governance committees and senior leadership.
  • Why it matters: Ensures clear accountability and decision-making authority across the AI lifecycle, promoting responsible and transparent governance.

• Ethical AI and Fairness Policy
  • What it is: Defines principles and procedures for developing and deploying AI systems that uphold fairness, transparency, human rights, and non-discrimination.
  • Why it matters: Helps prevent algorithmic bias, supports equitable outcomes, and builds stakeholder trust in AI technologies.

• AI Vendor Management Policy
  • What it is: Outlines the process for evaluating, onboarding, and monitoring third-party AI vendors and systems.
  • Why it matters: Ensures that external AI providers meet organizational standards for security, ethics, and regulatory compliance.

• AI Transparency and Communication Policy
  • What it is: Establishes guidelines for communicating how AI systems operate, including disclosures to users, customers, and regulators.
  • Why it matters: Promotes openness and explainability, supporting user understanding and regulatory expectations around AI transparency.

• AI Data Management Policy
  • What it is: Defines data governance requirements specific to AI, including data sourcing, labeling, retention, and quality assurance.
  • Why it matters: Ensures that AI systems rely on accurate, representative, and compliant data, reducing risks of bias or misuse.

• AI Security and Risk Management Policy
  • What it is: Details the security controls, technical safeguards, and incident response measures applied to AI systems and datasets.
  • Why it matters: Protects AI models and data from unauthorized access, manipulation, and adversarial attacks.

• High-Risk AI Management Policy
  • What it is: Specifies enhanced requirements for the development, testing, monitoring, and human oversight of high-risk AI systems.
  • Why it matters: Provides additional controls for AI applications that significantly impact individuals’ rights, safety, or well-being.

• Cross-Border and Local AI Compliance Policy
  • What it is: Defines how the organization ensures compliance with AI regulations across different jurisdictions and aligns with local legal obligations.
  • Why it matters: Simplifies global compliance by harmonizing AI governance practices across countries, states, and regions.

By leveraging these comprehensive policy templates, organizations can build a strong foundation for AI governance that supports compliance, ethics, and accountability across all AI operations. The CAIF ensures that these policies align with global regulatory standards while remaining flexible enough to adapt to evolving AI laws and technologies.

Why Should You Use the CAIF?

The CAIF provides a unified approach to global AI compliance, enabling organizations to address multiple AI laws and jurisdictions through a single, standardized framework. By mapping requirements across countries and states, it eliminates duplicative efforts and simplifies compliance management. At the same time, the framework promotes responsible AI governance by embedding transparency, fairness, and accountability throughout the AI lifecycle, while ensuring robust oversight and meaningful human involvement where required. CAIF also strengthens risk management by helping organizations identify and mitigate AI-specific risks before they escalate into compliance issues, protecting against reputational, ethical, and regulatory harm. Finally, the framework enhances efficiency and scalability, streamlining AI compliance through automation and centralized management, and allowing organizations to consistently scale governance practices across teams, projects, and geographies.

How Do We Achieve Compliance?

Meeting global AI compliance requirements through the Centraleyes AI Framework (CAIF) is achieved via the Centraleyes Risk & Compliance Management platform, which provides automation, intelligence, and visibility across the AI governance lifecycle.

Organizations begin by cataloging their AI systems, assessing their risk classifications, and evaluating existing governance processes. The platform then guides users through structured assessments, mapping their responses to global AI laws and highlighting any compliance gaps.

Through automated risk registers, AI-specific questionnaires, and actionable remediation workflows, Centraleyes enables organizations to close compliance gaps efficiently. The system’s dashboard and reporting tools provide real-time visibility into AI compliance status, ensuring accountability and continuous improvement.

By adopting the CAIF, organizations can confidently deploy AI technologies that are not only innovative and effective, but also compliant, ethical, and secure.