What is AI Governance?

What is the Difference Between AI Governance and Traditional Governance?

While AI governance is a relatively new concept, IT governance has been a key part of IT operations for decades. Looking at some of the differences between AI governance and traditional governance can help you understand the challenges involved with AI governance.

AI Governance, Ethics, and Compliance

Ethics and compliance are two key aspects of AI governance, despite the lack of solidified regulatory frameworks surrounding AI. In terms of compliance, the European Union AI Act (EU AI Act) is the primary concern for AI tools. However, organizations must also comply with privacy regulations like General Data Protection Regulation (GDPR) when handling user data.

Ethics are perhaps the most commonly discussed obstacle when it comes to AI. AI has the potential to cause harm if it’s making biased decisions or using incorrect information to generate reports, so AI developers are responsible for making sure users don’t encounter these problems. Human oversight helps ensure AI is beneficial to humans.

AI Governance Across the AI Lifecycle

AI governance doesn’t start once an AI tool is deployed; it’s part of the process from start to finish. Software artifact repositories play a key role by centralizing AI components before deployment. Governance begins in the planning and design stages, where developers establish a purpose and identify potential risk. Data quality controls are implemented to ensure AI tools offer privacy and security.

While building models, developers frequently test for bias to ensure AI models are fair and the decisions they make are clearly explainable. AI models are validated before deployment to ensure they’re compliant with regulations and are meeting performance goals. After an AI model is deployed, it’s continuously monitored to prevent model drift and other long-term issues.

Why is AI Governance Important?

AI governance is important because AI significantly impacts society, business operations, and decision-making. Whether you’re an organization using AI to automate repetitive tasks or an individual asking an AI model a question, it’s important to be able to trust the outputs of an AI model. Left unchecked, biases in AI can have a negative impact on groups. AI governance helps prevent biased decision-making and minimize the negative impact AI has on society.

Ungoverned AI risks

AI governance frameworks are a crucial part of safely developing and deploying AI tools. There are several risks that come with ungoverned AI tools, including:

• Bias: AI models have a tendency to form biases based on the data they’re trained on, and those biases can lead to unfair or incorrect decisions. Conducting bias analysis allows AI developers to discover and mitigate bias to ensure AI outputs are fair and truthful.
• Privacy violations: When you’re inputting your information into an AI tool, it should go without saying that your information is secure. Ungoverned AI models often result in privacy violations, which have a negative impact on consumer trust. Privacy violations can also result in penalties and fines.
• Security vulnerabilities: Security is one of the core principles of governance in AI, which is why security is a common issue with ungoverned AI models. Implementing controls and enforcing security policies helps identify and mitigate vulnerabilities before they negatively impact anyone.
• Lack of explainability: When an AI model makes a decision, users need to clearly understand how and why it made that decision. A lack of explainability makes it difficult to build trust, and this is a common issue with ungoverned AI tools.

Key Components of an Effective AI Governance Framework

In order for an AI governance framework to be effective, there are several key components it must have. Understanding the key parts of an AI governance framework makes it easier to put guardrails in place to ensure AI is secure and fair.

Frameworks and policies for responsible AI usage

Just like artifact management is a key part of the software supply chain, responsible AI policies are essential in AI governance. Organizations should have established frameworks and policies for responsible AI usage to ensure fairness, transparency, accountability, privacy, and security.

There’s no single solution that works for every business, so it’s important to develop your own policies for responsible AI usage. These frameworks and policies help prevent issues such as shadow AI, which can result in security and legal blind spots. It’s important to provide training after developing frameworks and policies for responsible AI usage to ensure teams are up to speed on the new guidelines.

What are the roles of stakeholders in AI governance?

Stakeholders play a key role in AI governance, from executives and legal teams to designers and developers. Ensuring stakeholders understand your policies for responsible AI usage is important because it ensures AI governance from development to deployment. Stakeholders are responsible for ensuring compliance, security, privacy, and fairness, with legal teams and executives collaborating closely with development teams.

AI governance processes

Establishing AI governance processes simplifies governance in AI, allowing you to follow a standard set of procedures throughout the development and deployment stages:

• Risk assessment: Identify potential risks an AI model might present, including security, privacy, and bias risks.
• Model review: Review models to ensure outputs are explainable and fair, mitigating bias if it has an impact on outputs.
• Approval: After ensuring AI is fair, secure, and private, approve the model so it can be deployed. AI governance doesn’t stop here.
• Ongoing oversight: After launching AI tools, it’s important to continuously monitor them and maintain human oversight to prevent model drift.

AI governance metrics

Using metrics to measure the success of AI governance can help you get a better understanding of an AI model. Ethical considerations are an important aspect of this process , as ethics in AI mostly center around the societal impacts AI can have. With AI becoming increasingly common in the decision-making process, it’s more important than ever to ensure the decisions AI makes are fair. When deploying an AI model, you’re responsible for ensuring that the model is outputting information that’s fair and accurate.

There are several metrics you can use to measure AI governance, including:

• Rates: Key metrics like the accuracy, precision, and recall rates help developers ensure AI is accurate and fair.
• Incident tracking: Incidents are tracked through error rates, which represent the total percentage of failed requests.
• Monitoring outcomes: AI models are continuously monitored to ensure outcomes are fair and accurate.

Bias testing and statistical parity

To identify bias in AI models and gain a deeper understanding of how different groups are impacted, you can use methods like statistical parity. This metric helps you determine whether a positive outcome is equally likely in different groups. If bias testing reveals that positive outcomes are significantly less likely for one group, you need to address that problem before deployment.

What are Best Practices for Implementing Responsible AI Governance?

Implementing responsible AI governance introduces operational complexity as model deployment cycles accelerate. Following best practices makes it easier to implement an AI governance framework that aligns with your business while accounting for factors like ethics and compliance.

Aligning AI governance with business goals and risk tolerance

Aligning AI governance with business goals and risk tolerance is essential. AI governance should support your larger business objectives, whether your goals are related to financial growth, increasing your customer base, or increasing operational efficiency.

When you’re creating an AI governance framework, map AI initiatives to specific strategic objectives so you know what objectives each initiative supports. Use key performance indicators (KPIs) to measure the success of AI initiatives and ensure they’re helping you reach strategic objectives.

It’s also important to create a cross-functional team that’s dedicated to AI governance and compliance. This team should combine stakeholders from several key teams, including legal, development, IT, compliance, and executive teams.

Building Controls for Human Oversight, Explainability, and Continuous Monitoring

Creating guardrails and building controls for human oversight and continuous monitoring are essential aspects of AI governance.

AI models are trained on human data, and that human data contains biases. Unfortunately, models have a tendency to pick up on those biases and integrate them into outputs. Human oversight helps mitigate these biases to ensure AI is fair and accurate. This can be achieved through regular audits and human-in-the-loop (HITL) systems.

Explainability ensures AI models are transparent and users understand and trust their outputs. Focusing on explainable AI is one of the best ways to build a trustworthy, transparent model. Key aspects of AI explainability include traceability, decision understanding, and prediction accuracy.

After an AI model is deployed, it needs to be continuously monitored to prevent model drift. As the world and the data in it change over time, the accuracy of an AI model can decline. Continuous monitoring ensures AI models are fair and accurate well after they’re deployed.

Training and awareness programs

In order to successfully implement AI governance, you have to make sure key stakeholders are up to speed on AI governance frameworks and policies.

Training and awareness programs should target team members who will play an essential role in ensuring AI governance, including developers, legal teams, and compliance teams. The goal here is to make sure stakeholders understand the importance of AI governance and how it’s being implemented into business operations.

You can supplement training and awareness programs by creating documentation for AI governance. Written policies and guidelines can help teams align AI development and usage with strategic objectives.

It’s important to keep in mind that AI governance training isn’t a one-time occurrence. AI governance policies can change as the AI landscape changes, and teams can lose focus on governance over time. Routine training and awareness programs help keep everyone sharp and up to date with the latest AI governance policies.

What are Examples of AI Governance in Practice?

Looking at examples of governance in AI can help you gain a better understanding of why AI governance is important and how it applies to the real world.

What is the EU AI Act?

The EU AI Act was introduced in 2024 in an attempt to govern AI and mitigate the potential risks associated with AI models. The EU AI Act has specific transparency requirements, requiring models to disclose that content was generated by AI and prevent models from generating illegal content. This act also classifies some AI systems as high risk, which means they must be assessed before deployment. AI systems that pose unacceptable risks are banned under the EU AI Act.

What is the NIST AI Risk Management Framework?

The National Institute of Standards and Technology (NIST) AI Risk Management Framework was created in collaboration with private and public sectors to manage risks associated with AI.

The NIST Risk Management Framework doesn’t just focus on the technical risks of AI, it also looks at the social and ethical considerations. Because of this, the NIST framework requires a diverse team of stakeholders to implement. NIST also designed its Risk Management Framework to be flexible, so it can be used by anyone from small businesses with low-risk AI systems to massive corporations with high-risk systems.

What are the Google AI Principles?

The Google AI Principles were developed by Google to ensure AI models are safe, fair, and innovative. There are three core Google AI Principles:

• Bold innovation
• Responsible development and deployment
• Collaborative progress, together

Google uses these principles to guide AI development, creating an AI progress report at the end of each year. Users can view past AI progress reports to verify the safety and trustworthiness of Google AI tools.

What is the Microsoft Responsible AI Standard?

Microsoft’s Responsible AI Standard is designed to achieve six key goals:

• Fairness
• Inclusiveness
• Reliability and safety
• Transparency
• Privacy and security
• Accountability

These are the essential goals you’re trying to achieve with any form of AI governance. Microsoft achieves these goals by setting rules for responsible AI use, empowering teams to adopt responsible AI practices, reviewing sensitive use cases, and helping to shape the public policy that guides AI governance.

AI Governance with the JFrog Software Supply Chain Platform

While human oversight remains a vital part of responsible technology use, automated compliance tooling is essential for streamlining AI governance. As technology continues to evolve, simplifying these complex processes is exactly the goal of the JFrog AI Catalog. Acting as a centralized hub, the catalog empowers organizations to securely discover, manage, and govern their AI and machine learning models directly within existing DevSecOps workflows.

The JFrog Platform is built upon the foundation of the JFrog Responsible AI Principles, which ensure AI technologies are developed and deployed in a fair, transparent, and ethical manner. Focusing on key pillars such as accountability, privacy, security, reliability, and fairness, JFrog enforces these goals through a robust internal governance framework, strict data protection policies, and regular compliance reviews. By integrating these principles into their tooling, organizations can confidently manage risk and mitigate bias.

Start a free trial to evaluate automated model compliance policies within your environment, or book a targeted technical demo of the JFrog AI Catalog.