Artificial intelligence is being used across organizations for an increasing number of use cases everyday. However, AI is still a relatively new technology with new and unknown risks that come with it. A well-designed AI governance program allows you to keep your risks low and maintain integrity and trust throughout your organization.
AI governance is a set of rules, policies, and practices used by an organization to dictate how it uses AI in alignment with its business objectives. An AI governance program should contribute to your regulatory and security compliance obligations, support your data governance, and improve transparency through your AI practices.
Levels of AI governance
AI governance can be implemented in a variety of ways and at varying levels of sophistication. AI governance programs are often classified in one of three levels:
- Informal AI governance: This is limited AI governance, often without an official framework or structured strategy, but there are some informal AI practices in place.
- Ad-hoc AI governance: Ad hoc AI governance has several policies and processes in place for AI oversight, but they are often implemented in response to a threat or incident, resulting in these policies existing as one-offs rather than part of an overarching strategy.
- Formal AI governance: A proactive set of rules and practices that are constructed and implemented to the organization's use of AI. At this level, stakeholders have invested time in developing a unified, organization-wide governance program.
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Who is responsible for AI governance?
The executive leaders of the organization are ultimately responsible for the way the business uses AI, but there are also many other departments that should be involved in the organization’s AI governance program.
Your compliance team should provide insights into the organization’s regulatory requirements and legal should weigh in on potential legal risks. Your CFO and finance team should offer input about the financial risks associated with your AI governance as well. It’s important to mitigate any risks that are identified by these teams as part of your AI governance strategy.
AI governance frameworks
Below are some frameworks, documents, and resources that can help you build out your AI governance strategy:
- ISO 42001: ISO 42001 defines the requirements of an Artificial Intelligence Management System (AIMS) that helps organizations responsibly develop and use AI — emphasizing ethical considerations, transparency, and the necessity of continuous learning.
- White House Office of Science and Technology Policy’s AI Bill of Rights: This guide is designed as a starting framework that includes a collection of policies and practices related to: some text
- Safe and effective systems for AI guidance
- Algorithmic discrimination protections
- Data privacy
- Notice and explanation of how you’re using AI
- Human alternatives, consideration, and fallback
- The Organization of Economic Cooperation and Development (OECD): OECD has developed a collection of AI practices for international adoption that outline ways to maintain the ethical and low-risk use of AI.
- NIST AI Risk Management Framework: Developed by the National Institute for Standards and Technology, the NIST AI Risk Management Framework is an early program for identifying and mitigating risks in your AI use.
Implementing an AI governance program
To make your AI governance program effective and sustainable, consider these implementation best practices:
- Audit your use of AI: Find out how AI is being used throughout your organization currently. Knowing what activities and tasks are being performed with AI can help you identify the risks associated with your AI use and how you can mitigate those risks.
- Consider your business objectives: Understand the ways AI can be used to contribute to the organization’s goals. Be sure to consider whether this is done in a responsible way and factor in potential risks.
- Engage stakeholders in the process: Connect with leaders and teams throughout the organization to understand how they use AI and hear their concerns and suggestions. This will increase transparency and improve the potential impact of implementation.
- Establish tracking metrics: Measure the success of your governance program by identifying KPIs and ensure that you’re using this information to identify opportunities for improvement.
- Use the ISO 42001 framework: Developed by the International Standards Organization, ISO 42001 can help organizations responsibly develop and use AI — emphasizing ethical considerations, transparency, and the necessity of continuous learning.
Ready to get started with your AI governance strategy?
Get your ISO 42001 certification fast with Vanta’s ISO 42001 solution that gives you a framework for responsibly developing and using AI through an AI Management system (AIMS) that can be certified by third-party auditors.
With Vanta, you’re equipped to get ISO 42001 certified quickly and seamlessly so you can show that your company uses and deploys AI responsibly while avoiding extra work. Learn more about Vanta's ISO 42001 framework by requesting a demo.
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Governance
What is AI governance?
Governance
Artificial intelligence is being used across organizations for an increasing number of use cases everyday. However, AI is still a relatively new technology with new and unknown risks that come with it. A well-designed AI governance program allows you to keep your risks low and maintain integrity and trust throughout your organization.
AI governance is a set of rules, policies, and practices used by an organization to dictate how it uses AI in alignment with its business objectives. An AI governance program should contribute to your regulatory and security compliance obligations, support your data governance, and improve transparency through your AI practices.
Levels of AI governance
AI governance can be implemented in a variety of ways and at varying levels of sophistication. AI governance programs are often classified in one of three levels:
- Informal AI governance: This is limited AI governance, often without an official framework or structured strategy, but there are some informal AI practices in place.
- Ad-hoc AI governance: Ad hoc AI governance has several policies and processes in place for AI oversight, but they are often implemented in response to a threat or incident, resulting in these policies existing as one-offs rather than part of an overarching strategy.
- Formal AI governance: A proactive set of rules and practices that are constructed and implemented to the organization's use of AI. At this level, stakeholders have invested time in developing a unified, organization-wide governance program.
{{cta_withimage6="/cta-modules"}}
Who is responsible for AI governance?
The executive leaders of the organization are ultimately responsible for the way the business uses AI, but there are also many other departments that should be involved in the organization’s AI governance program.
Your compliance team should provide insights into the organization’s regulatory requirements and legal should weigh in on potential legal risks. Your CFO and finance team should offer input about the financial risks associated with your AI governance as well. It’s important to mitigate any risks that are identified by these teams as part of your AI governance strategy.
AI governance frameworks
Below are some frameworks, documents, and resources that can help you build out your AI governance strategy:
- ISO 42001: ISO 42001 defines the requirements of an Artificial Intelligence Management System (AIMS) that helps organizations responsibly develop and use AI — emphasizing ethical considerations, transparency, and the necessity of continuous learning.
- White House Office of Science and Technology Policy’s AI Bill of Rights: This guide is designed as a starting framework that includes a collection of policies and practices related to: some text
- Safe and effective systems for AI guidance
- Algorithmic discrimination protections
- Data privacy
- Notice and explanation of how you’re using AI
- Human alternatives, consideration, and fallback
- The Organization of Economic Cooperation and Development (OECD): OECD has developed a collection of AI practices for international adoption that outline ways to maintain the ethical and low-risk use of AI.
- NIST AI Risk Management Framework: Developed by the National Institute for Standards and Technology, the NIST AI Risk Management Framework is an early program for identifying and mitigating risks in your AI use.
Implementing an AI governance program
To make your AI governance program effective and sustainable, consider these implementation best practices:
- Audit your use of AI: Find out how AI is being used throughout your organization currently. Knowing what activities and tasks are being performed with AI can help you identify the risks associated with your AI use and how you can mitigate those risks.
- Consider your business objectives: Understand the ways AI can be used to contribute to the organization’s goals. Be sure to consider whether this is done in a responsible way and factor in potential risks.
- Engage stakeholders in the process: Connect with leaders and teams throughout the organization to understand how they use AI and hear their concerns and suggestions. This will increase transparency and improve the potential impact of implementation.
- Establish tracking metrics: Measure the success of your governance program by identifying KPIs and ensure that you’re using this information to identify opportunities for improvement.
- Use the ISO 42001 framework: Developed by the International Standards Organization, ISO 42001 can help organizations responsibly develop and use AI — emphasizing ethical considerations, transparency, and the necessity of continuous learning.
Ready to get started with your AI governance strategy?
Get your ISO 42001 certification fast with Vanta’s ISO 42001 solution that gives you a framework for responsibly developing and using AI through an AI Management system (AIMS) that can be certified by third-party auditors.
With Vanta, you’re equipped to get ISO 42001 certified quickly and seamlessly so you can show that your company uses and deploys AI responsibly while avoiding extra work. Learn more about Vanta's ISO 42001 framework by requesting a demo.
{{cta_testimonial6="/cta-modules"}}
Scaling your compliance doesn't have to SOC 2 much.
Learn how to add new frameworks to your compliance program without adding to your workload.
Scaling your compliance doesn't have to SOC 2 much.
Learn how to add new frameworks to your compliance program without adding to your workload.
Scaling your compliance doesn't have to SOC 2 much.
Learn how to add new frameworks to your compliance program without adding to your workload.
Without Vanta, we’d be looking at hiring another person to handle all the work that an audit and its preparation creates.”
Willem Riehl, Director of Information Security and Acting CISO | CoachHub
| Role: | GRC responsibilities: |
|---|---|
| Board of directors | Central to the overarching GRC strategy, this group sets the direction for the compliance strategy. They determine which standards and regulations are necessary for compliance and align the GRC strategy with business objectives. |
| Chief financial officer | Primary responsibility for the success of the GRC program and for reporting results to the board. |
| Operations managers from relevant departments | This group owns processes. They are responsible for the success and direction of risk management and compliance within their departments. |
| Representatives from relevant departments | These are the activity owners. These team members are responsible for carrying out specific compliance and risk management tasks within their departments and for integrating these tasks into their workflows. |
| Contract managers from relevant department | These team members are responsible for managing interactions with vendors and other third parties in their department to ensure all risk management and compliance measures are being taken. |
| Chief information security officer (CISO) | Defines the organization’s information security policy, designs risk and vulnerability assessments, and develops information security policies. |
| Data protection officer (DPO) or legal counsel | Develops goals for data privacy based on legal regulations and other compliance needs, designs and implements privacy policies and practices, and assesses these practices for effectiveness. |
| GRC lead | Responsible for overseeing the execution of the GRC program in collaboration with the executive team as well as maintaining the organization’s library of security controls. |
| Cybersecurity analyst(s) | Implements and monitors cybersecurity measures that are in line with the GRC program and business objectives. |
| Compliance analyst(s) | Monitors the organization’s compliance with all regulations and standards necessary, identifies any compliance gaps, and works to mitigate them. |
| Risk analyst(s) | Carries out the risk management program for the organization and serves as a resource for risk management across various departments, including identifying, mitigating, and monitoring risks. |
| IT security specialist(s) | Implements security controls within the IT system in coordination with the cybersecurity analyst(s). |
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