March 20, 2025

How to Build an Artificial Intelligence Governance Framework

artificial intelligence governance framework

AI use has skyrocketed, with as many as 78% of survey respondents reporting that the organizations they work for use AI in at least one business function. The growing adoption of AI has also led to an increased awareness of its potential dangers. Businesses deploying AI systems must secure their solutions against attacks, adhere to regulatory standards, and ensure they meet regulatory requirements. This is where an artificial intelligence governance framework can help.

AI governance is the structured approach to managing AI risk, complying with legal regulations, and fostering a sense of trust in AI systems. This step-by-step guide will help you understand how to build a comprehensive artificial intelligence governance framework that proactively manages AI risk.

SEE ALSO: How to Choose the Best AI Governance Software

Step 1: Define Artificial Intelligence Governance Objectives and Scope

The first essential step of developing an AI governance framework is defining your organization’s goals. The objective and scope of your framework should be abundantly clear, and it should also align with your business goals, relevant regulatory requirements, and other risk management strategies. By establishing clear objectives, your team can ensure that AI governance initiatives support innovation while maintaining compliance and trust.

Step 2: Establish AI Governance Policies and Standards

Once you’ve identified the objectives and scope of your framework, consider the policies and standards that will ensure your AI systems operate ethically, securely, and in compliance with regulations. In many cases, these policies will be dictated by national or state laws such as the EU AI Act or California’s AI regulations.

Step 3: Implement AI Risk Management Processes

AI risk refers to the unique vulnerabilities an organization takes on when they begin using artificial intelligence. A structured AI risk management process allows your team to understand the potential pitfalls of using AI, and empowers you to address them in advance to mitigate those risks.

To effectively manage AI risks, organizations should implement risk assessment frameworks that quantify potential threats and their impact. This process typically includes:

  • Developing AI Risk Scorecards – Assign risk levels (low, medium, high) to AI models based on fairness, security, and compliance metrics.
  • Using AI Model Validation Techniques – Conduct stress testing, bias analysis, and adversarial testing before deploying models.
  • Automating Risk Assessments – Leverage AI governance platforms to continuously scan models for biases, performance drifts, and security vulnerabilities.

Step 4: Create AI Compliance and Auditing Mechanisms

Since AI risks evolve over time, your framework should continuously monitor for new risks and ongoing compliance. You can streamline this process by leveraging AI governance tools like Lumenova AI that automatically evaluate AI models for compliance with regulatory frameworks.

A well-documented audit trail will also help your team with regulatory reporting, model accountability, and risk tracking. Keep detailed records of these items on file:

  • Model Development and Training Data – Document how AI models are trained, including data sources and preprocessing techniques.
  • Model Performance and Risk Assessments – Track fairness, accuracy, and bias metrics over time.
  • Decision Logs and Explainability Reports – Store records of AI-generated decisions, ensuring transparency for stakeholders.
  • Regulatory Compliance Reports – Maintain logs of compliance checks, regulatory audits, and policy updates.

Step 5: Foster Cross-Functional Collaboration and AI Accountability

AI governance is not the sole responsibility of a single department. Rather, cross-functional teams must work together to create and maintain trustworthy AI systems. Executive leadership should spearhead prioritizing AI governance, with legal teams, engineers, cybersecurity personnel, and risk departments aligned and working toward the same goal.

Simplify Your Artificial Intelligence Governance with Lumenova AI

Lumenova AI simplifies this process and continuously monitors your artificial intelligence solutions for compliance and AI risk. Reach out today to book a demo and see how we can automate many of your AI governance processes.

Frequently Asked Questions

An artificial intelligence governance framework is a structured approach that organizations use to oversee the development, deployment, and monitoring of AI systems. It ensures ethical use, regulatory compliance, risk management, and accountability across all AI applications within a business.

As AI adoption grows, so do data privacy, algorithmic bias, security, and regulatory compliance risks. An AI governance framework helps organizations manage these risks proactively, build trust with stakeholders, and ensure that AI systems align with legal and ethical standards.

Key components include clearly defined objectives, governance policies, risk management processes, compliance monitoring, auditing mechanisms, and cross-functional collaboration. Together, these elements enable companies to safely scale AI while maintaining control and transparency.

AI risk management is a critical part of governance that anticipates, identifies, assesses, and mitigates threats such as bias, model drift, or security vulnerabilities. Organizations use tools like risk scorecards, model validation techniques, and automated monitoring to detect issues and respond effectively.​

AI governance platforms like Lumenova AI support implementation by offering features such as automated risk assessments, compliance reporting, model performance tracking, and audit trail management. These tools streamline governance processes and help teams stay ahead of regulatory requirements.

Related topics: Trustworthy AI AI Transparency

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