AI Governance Audits 101: Conducting Internal and External Assessments
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TL:DR

This guide examines why AI governance matters for business, including making certain ethical AI use and compliant practices with AI and data regulations. AI governance comes with the need for audits — regular reviews of governance practices to minimise AI risks, incorporate new AI trends or regulations and address any gaps in AI systems or governance practices. Learn AI governance audit best practices and how to address common challenges that arise during these audits.

Introduction

Artificial intelligence (AI) has become a top industry priority for many companies. About 65% of businesses are using AI in their processes currently, and that is almost twice as many as the year before, according to McKinsey & Company. AI and machine learning (ML) can help streamline everything from customer service to content creation to fraud prevention.

AI governance is a necessary concern for businesses, and audits are an important part of governance, though they differ from other types of business audits. 

AI governance audits should be planned and executed regularly to confirm ongoing compliance and accountability with AI tools. These audits help companies maintain compliance with legal standards and the ethical use of AI.

This guide covers the basics of conducting AI governance audits internally and externally.

Key Takeaways

1. AI Governance Matters: Regular audits are the key to keeping up with government and industry standards during this "great unknown" phase of AI development. 

2. Best Practices for AI Governance Audits: The audit process involves preparation, data collection and analysis, evaluation of AI systems, detailed reporting and continuous improvement based on what is learned from the audit.

3. Every Good Practice Has Challenges: The complexity of AI systems and a constantly updated list of rules and regulations create challenges to developing a proper workflow for audits of AI. Crafting clear criteria, understanding the real risks of AI, setting goals, hiring external auditors and striving for consistency will help an organisation overcome these challenges.

What Are AI Governance Audits?

AI tools are fairly young, and experts are still learning the best and most dangerous ways to use them. During this initial period of use, the need for extra oversight is higher for most businesses. 

This is where AI governance practices come into play. They make sure that AI is deployed ethically and in compliance with all relevant regulations across the business. AI governance is a key component of any business that leverages AI tools, and it needs consistent care and attention.

AI governance audits assess how AI is being used while keeping tabs on any changing regulations. Here are a few key objectives of AI audits:

  • Evaluating AI risks and impacts through a risk assessment
  • Verifying AI use aligns with the business’s mission and goals
  • Reviewing the infrastructure in place that oversees governance and AI models
  • Confirming that all AI regulations and standards are being followed and incorporated
  • Ensuring that data privacy and security laws are being considered within AI systems
  • Integrating any new legislation, best practices or trends into current AI practices
  • Working with and reporting audit findings to organisation leaders
  • Establishing a robust set of policies and procedures for ongoing AI compliance

AI governance audits may involve different departments and expertise, including legal, data, business and compliance, to view information from multiple perspectives. 

AI governance assessors need to conduct both internal audits and external audits. This means that not only are internal processes and uses being evaluated but so are those of any external stakeholders, whether investors or technology partners. Both types of audits are important in understanding risks and AI usage.

The Importance of AI Governance Audits

As with any business regulatory concern, AI governance verifies all regulations are being followed for ongoing compliance. Governance audits help businesses confirm they’re meeting ethical requirements, which is a big topic of debate related to AI systems in particular.

AI governance audits go beyond regulatory and ethical considerations and help companies build transparency. Audits show external stakeholders that a business is committed to implementing best practices, and transparent about using AI and minimising risks.

Additionally, regular audits strengthen a company’s AI risk management practices and help teams identify and minimise risks that come from AI integrations and ML models.

For example, AI models are essentially dependent upon the data that a company feeds into them. If that data is inaccurate or out of date, AI outputs could lead to incorrect decision-making or even bias. With AI governance audits, companies can monitor data that goes into AI models to instill accuracy, minimising AI-related risks.

5 Steps for Conducting AI Governance Audits

It’s helpful to see what an AI governance audit looks like in practice. Follow these five steps to get started:

  1. Preparation: First, you need to define your audit objectives. What are you reviewing and why? Outline the scope of the audit, detailing what will be evaluated. This is also when you will assign roles on the audit team so that each person understands their responsibility and the timing of their role.
  2. Data Collection and Analysis: Next is gathering information. Create a plan to gather any data related to AI use and governance practices. Analyse this data to check for business compliance and to confirm AI tools and ML models are performing as intended. Robust data mapping and data discovery practices can help with data collection and analysis.
  3. Evaluating AI Systems: This step involves reviewing your AI systems' findings against AI standards and regulations. Are your systems in compliance? Are your AI objectives being met? What risks exist within AI governance and AI practices?
  4. Reporting and Documentation: After conducting the audit, summarise audit findings in a report. This documentation needs to be thorough and detail each component of the AI governance audit. Reporting will be used to share findings with business leaders and others within the organisation.
  5. Continuous Improvement: With the report in hand, you can now create a plan for addressing the audit findings and improving governance practices. Regular audits guarantee that the company is always improving its regulatory practices so that there are never any unnecessary risks or non-compliance.

Best Practices for AI Governance Audits

For AI governance audits to be effective, there are a few best practices you need to follow. Here are additional strategies to incorporate into your audits:

  • Define Clear Criteria: The best audits are focused on specific areas of AI governance that need review. Define criteria beforehand and outline what is the most relevant, where the risks lie and that the scope is specific. This helps make sure the audit is successful, and outcomes are clear.
  • Understand Potential AI Risks: Evaluate each way you’re using AI and what the risks of that function are for the business as a whole. For example, what would the impact be if a given AI system started leveraging false data?
  • Establish Goals for AI Systems: Successful governance audits require clearly defined goals for each AI system or ML model within the business. During audits, these goals should be reviewed to determine if they’re being met in the current system.
  • Consider Using an External Auditor: It can be a challenge for businesses to be certain of objectivity during AI governance audits. Consider working with an outside auditor who can take on an objective mindset when reviewing systems. These professionals are also experts in AI governance, which strengthens the audit with added credibility.
  • Be Consistent with AI Audits: Audits should be an ongoing concern for the company. Establish a cadence for conducting AI governance audits on a regular basis. This helps you minimise risks and confirm that the business is staying compliant and accountable.
  • Engage Stakeholders: Apart from the audit team, make sure you engage other stakeholders who use AI or should have oversight over processes. It can help the team find gaps they wouldn’t have otherwise noticed and address additional questions and concerns from stakeholders.

Top Challenges in AI Governance Audits

Even when following a clear auditing process and implementing best practices, you may run into common AI governance challenges. AI is not easy to understand for the average business, and it requires care to manage properly. Being prepared for these roadblocks will help you stay proactive.

One common challenge is the sheer complexity of AI systems and ML models. The more humans learn about what AI can do, the more regulations are put in place. As such, the regulatory boundaries are always shifting and responding to trends and new technologies. It can be difficult to keep up with all of these moving parts.

Another issue is confirming that the audits are complete. For example, many companies may be assessing risks and claiming compliance, but they may not understand how AI is impacting all of their applications or core business practices. AI governance audits should review all policies and procedures throughout the business to make certain everything is in line with AI risk management and that the right controls are implemented.

In many cases, emphasising collaboration can help you with these challenges. When multiple departments are working together, each can contribute their own concerns and applications to create a fuller picture of AI governance within the organisation. Don’t be afraid to bring in other stakeholders who understand how AI is being used in the company and why it’s important.

Make good use of the new technologies that are being perfected every day. AI platforms and data tools can help you better plan and facilitate AI governance processes, including incorporating continuous AI and performance monitoring tools. Sometimes, embracing one platform is all you need to build effectiveness and efficiency with AI governance.

Staying proactive, monitoring laws and regulations, embracing technology and collaborating effectively are all keys to successful AI governance audits. Take another look at your current practices to see where there may be gaps that need to be addressed.

Final Thoughts

Modern businesses must emphasise their commitment to transparency and the ethical use of AI. By improving AI governance processes with regular audits, you can create compliance and establish a system of accountability. 

Within your larger AI governance framework, establish a regular cadence for AI governance audits. Make sure everyone involved knows their role in audits. Consider working with an outside party that specialises in these ongoing reviews. By keeping AI governance top of mind, you’ll be able to continue improving AI systems and minimising AI-related risks. 

With Zendata, you get a platform that gives you a strong data set to put the right data protection practices in place. We help you reduce risks through a variety of strategies, from redaction to synthetic data to increased collaboration. With better data protection and data lineage, you’ll establish stronger privacy practices that feed into your AI systems and ML models. We help you craft ongoing AI governance best practices with our data and risk management tools.

Find out more about how the Zendata platform can assist with AI governance by taking a look at our plans and pricing.

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Contact Us For More Information

If you’d like to understand more about Zendata’s solutions and how we can help you, please reach out to the team today.

AI Governance Audits 101: Conducting Internal and External Assessments

July 5, 2024

TL:DR

This guide examines why AI governance matters for business, including making certain ethical AI use and compliant practices with AI and data regulations. AI governance comes with the need for audits — regular reviews of governance practices to minimise AI risks, incorporate new AI trends or regulations and address any gaps in AI systems or governance practices. Learn AI governance audit best practices and how to address common challenges that arise during these audits.

Introduction

Artificial intelligence (AI) has become a top industry priority for many companies. About 65% of businesses are using AI in their processes currently, and that is almost twice as many as the year before, according to McKinsey & Company. AI and machine learning (ML) can help streamline everything from customer service to content creation to fraud prevention.

AI governance is a necessary concern for businesses, and audits are an important part of governance, though they differ from other types of business audits. 

AI governance audits should be planned and executed regularly to confirm ongoing compliance and accountability with AI tools. These audits help companies maintain compliance with legal standards and the ethical use of AI.

This guide covers the basics of conducting AI governance audits internally and externally.

Key Takeaways

1. AI Governance Matters: Regular audits are the key to keeping up with government and industry standards during this "great unknown" phase of AI development. 

2. Best Practices for AI Governance Audits: The audit process involves preparation, data collection and analysis, evaluation of AI systems, detailed reporting and continuous improvement based on what is learned from the audit.

3. Every Good Practice Has Challenges: The complexity of AI systems and a constantly updated list of rules and regulations create challenges to developing a proper workflow for audits of AI. Crafting clear criteria, understanding the real risks of AI, setting goals, hiring external auditors and striving for consistency will help an organisation overcome these challenges.

What Are AI Governance Audits?

AI tools are fairly young, and experts are still learning the best and most dangerous ways to use them. During this initial period of use, the need for extra oversight is higher for most businesses. 

This is where AI governance practices come into play. They make sure that AI is deployed ethically and in compliance with all relevant regulations across the business. AI governance is a key component of any business that leverages AI tools, and it needs consistent care and attention.

AI governance audits assess how AI is being used while keeping tabs on any changing regulations. Here are a few key objectives of AI audits:

  • Evaluating AI risks and impacts through a risk assessment
  • Verifying AI use aligns with the business’s mission and goals
  • Reviewing the infrastructure in place that oversees governance and AI models
  • Confirming that all AI regulations and standards are being followed and incorporated
  • Ensuring that data privacy and security laws are being considered within AI systems
  • Integrating any new legislation, best practices or trends into current AI practices
  • Working with and reporting audit findings to organisation leaders
  • Establishing a robust set of policies and procedures for ongoing AI compliance

AI governance audits may involve different departments and expertise, including legal, data, business and compliance, to view information from multiple perspectives. 

AI governance assessors need to conduct both internal audits and external audits. This means that not only are internal processes and uses being evaluated but so are those of any external stakeholders, whether investors or technology partners. Both types of audits are important in understanding risks and AI usage.

The Importance of AI Governance Audits

As with any business regulatory concern, AI governance verifies all regulations are being followed for ongoing compliance. Governance audits help businesses confirm they’re meeting ethical requirements, which is a big topic of debate related to AI systems in particular.

AI governance audits go beyond regulatory and ethical considerations and help companies build transparency. Audits show external stakeholders that a business is committed to implementing best practices, and transparent about using AI and minimising risks.

Additionally, regular audits strengthen a company’s AI risk management practices and help teams identify and minimise risks that come from AI integrations and ML models.

For example, AI models are essentially dependent upon the data that a company feeds into them. If that data is inaccurate or out of date, AI outputs could lead to incorrect decision-making or even bias. With AI governance audits, companies can monitor data that goes into AI models to instill accuracy, minimising AI-related risks.

5 Steps for Conducting AI Governance Audits

It’s helpful to see what an AI governance audit looks like in practice. Follow these five steps to get started:

  1. Preparation: First, you need to define your audit objectives. What are you reviewing and why? Outline the scope of the audit, detailing what will be evaluated. This is also when you will assign roles on the audit team so that each person understands their responsibility and the timing of their role.
  2. Data Collection and Analysis: Next is gathering information. Create a plan to gather any data related to AI use and governance practices. Analyse this data to check for business compliance and to confirm AI tools and ML models are performing as intended. Robust data mapping and data discovery practices can help with data collection and analysis.
  3. Evaluating AI Systems: This step involves reviewing your AI systems' findings against AI standards and regulations. Are your systems in compliance? Are your AI objectives being met? What risks exist within AI governance and AI practices?
  4. Reporting and Documentation: After conducting the audit, summarise audit findings in a report. This documentation needs to be thorough and detail each component of the AI governance audit. Reporting will be used to share findings with business leaders and others within the organisation.
  5. Continuous Improvement: With the report in hand, you can now create a plan for addressing the audit findings and improving governance practices. Regular audits guarantee that the company is always improving its regulatory practices so that there are never any unnecessary risks or non-compliance.

Best Practices for AI Governance Audits

For AI governance audits to be effective, there are a few best practices you need to follow. Here are additional strategies to incorporate into your audits:

  • Define Clear Criteria: The best audits are focused on specific areas of AI governance that need review. Define criteria beforehand and outline what is the most relevant, where the risks lie and that the scope is specific. This helps make sure the audit is successful, and outcomes are clear.
  • Understand Potential AI Risks: Evaluate each way you’re using AI and what the risks of that function are for the business as a whole. For example, what would the impact be if a given AI system started leveraging false data?
  • Establish Goals for AI Systems: Successful governance audits require clearly defined goals for each AI system or ML model within the business. During audits, these goals should be reviewed to determine if they’re being met in the current system.
  • Consider Using an External Auditor: It can be a challenge for businesses to be certain of objectivity during AI governance audits. Consider working with an outside auditor who can take on an objective mindset when reviewing systems. These professionals are also experts in AI governance, which strengthens the audit with added credibility.
  • Be Consistent with AI Audits: Audits should be an ongoing concern for the company. Establish a cadence for conducting AI governance audits on a regular basis. This helps you minimise risks and confirm that the business is staying compliant and accountable.
  • Engage Stakeholders: Apart from the audit team, make sure you engage other stakeholders who use AI or should have oversight over processes. It can help the team find gaps they wouldn’t have otherwise noticed and address additional questions and concerns from stakeholders.

Top Challenges in AI Governance Audits

Even when following a clear auditing process and implementing best practices, you may run into common AI governance challenges. AI is not easy to understand for the average business, and it requires care to manage properly. Being prepared for these roadblocks will help you stay proactive.

One common challenge is the sheer complexity of AI systems and ML models. The more humans learn about what AI can do, the more regulations are put in place. As such, the regulatory boundaries are always shifting and responding to trends and new technologies. It can be difficult to keep up with all of these moving parts.

Another issue is confirming that the audits are complete. For example, many companies may be assessing risks and claiming compliance, but they may not understand how AI is impacting all of their applications or core business practices. AI governance audits should review all policies and procedures throughout the business to make certain everything is in line with AI risk management and that the right controls are implemented.

In many cases, emphasising collaboration can help you with these challenges. When multiple departments are working together, each can contribute their own concerns and applications to create a fuller picture of AI governance within the organisation. Don’t be afraid to bring in other stakeholders who understand how AI is being used in the company and why it’s important.

Make good use of the new technologies that are being perfected every day. AI platforms and data tools can help you better plan and facilitate AI governance processes, including incorporating continuous AI and performance monitoring tools. Sometimes, embracing one platform is all you need to build effectiveness and efficiency with AI governance.

Staying proactive, monitoring laws and regulations, embracing technology and collaborating effectively are all keys to successful AI governance audits. Take another look at your current practices to see where there may be gaps that need to be addressed.

Final Thoughts

Modern businesses must emphasise their commitment to transparency and the ethical use of AI. By improving AI governance processes with regular audits, you can create compliance and establish a system of accountability. 

Within your larger AI governance framework, establish a regular cadence for AI governance audits. Make sure everyone involved knows their role in audits. Consider working with an outside party that specialises in these ongoing reviews. By keeping AI governance top of mind, you’ll be able to continue improving AI systems and minimising AI-related risks. 

With Zendata, you get a platform that gives you a strong data set to put the right data protection practices in place. We help you reduce risks through a variety of strategies, from redaction to synthetic data to increased collaboration. With better data protection and data lineage, you’ll establish stronger privacy practices that feed into your AI systems and ML models. We help you craft ongoing AI governance best practices with our data and risk management tools.

Find out more about how the Zendata platform can assist with AI governance by taking a look at our plans and pricing.