Over the past two years, AI has moved from experiment to everyday infrastructure. It drafts customer emails, screens job applicants, flags fraud, writes code, and sits inside tools your teams already use, often without anyone deciding it should. Adoption has been fast, cheap, and mostly bottom up, which means most organizations are now running more AI than their leaders can name.
That speed has outrun the controls around it. Regulators have taken notice, with the EU AI Act now phasing in duties through 2027 and more rules close behind, and enterprise buyers, investors, and boards have started asking harder questions about how you manage the AI you build and buy. A single unreviewed tool, or one AI answer nobody can vouch for, is now enough to stall a deal or land a company in a headline, which means the pressure to get this right is arriving from customers and regulators at once. The people newly answerable for it, often a security lead, a GRC team, or an executive who inherited the topic, rarely started with a map.
The encouraging news is that the path is well marked. The frameworks, roles, and moves that hold AI accountable are established enough to borrow rather than invent, so a credible program is a matter of weeks of focused work, not quarters. What you need first is a clear picture of the pieces and the order they belong in. This article walks through why the topic turned urgent, the frameworks and roles that anchor a program, and the six steps to build one that keeps pace as your AI and the rules around it change.
What is AI governance?
AI governance is the set of policies, processes, controls, and accountability structures an organization uses to build, buy, deploy, and oversee artificial intelligence responsibly and in line with its goals, legal duties, and values. Put plainly, it's how a company decides which AI it will rely on, who answers for it, and where the guardrails sit, before a model is ever pointed at a real decision.
In practice, governance is what turns good intentions into something real. Most teams already agree their AI should be fair, accurate, and transparent. Governance is the part that makes those words hold up, translating them into rules people follow, owners who answer for them, and monitoring that surfaces problems while they're still small. Without it, those principles live on a slide and nowhere else.
It has also stopped being optional. AI is spreading through most companies faster than the oversight around it, often through tools that showed up without a review, and binding rules are now catching up to hold organizations accountable for how their AI behaves. More AI in more places, and more scrutiny on all of it, is why governance has moved from an afterthought to a question your buyers, regulators, and board expect you to answer.
Why is AI governance important?
AI governance matters now because organizations are adopting AI faster than they can secure or oversee it, and that gap is already expensive.
In its 2025 Cost of a Data Breach Report, IBM found that 13% of organizations had a breach involving an AI model or application, and 97% of those lacked proper AI access controls. Another 63% of breached organizations either don't have an AI governance policy or are still developing a policy. Shadow AI on its own added around $670,000 to the average breach cost.
Those failures share a root cause. AI behaves in ways traditional IT controls were not built to catch. Models produce biased or discriminatory outputs, invent facts that read as authoritative, drift as the underlying data shifts, and shape decisions with little human review. Layer on data leaking into outside models and fresh duties under regulation, and the exposure turns financial, legal, and reputational at once.
Governance is how you move fast without absorbing that risk. It gives every use of AI an owner, a policy, and a check, so you adopt AI deliberately instead of discovering months later that you have lost track of what your own tools are doing. AI adoption is outpacing the security and governance meant to keep it in check.
The key components of an AI governance program
A working AI governance program is built from a recognizable set of parts. Name them, and you have a checklist for what you are missing.
AI inventory
Start with a living record of every AI tool and model in use, whether your team built it or bought it, including the ones nobody formally approved. You cannot govern what you cannot see, and most companies underestimate how much AI has already crept into their workflows through everyday tools. The inventory is the foundation every other component builds on.
Clear ownership
Name accountability that runs from an executive sponsor down to the person responsible for each AI use case, so every decision has someone behind it. Ownership is what separates a policy that gets enforced from one that gets ignored. When something goes wrong, you want to know who answers for it before the incident, not during.
Policies and standards
Write the rules for acceptable use, data handling, model quality, and disclosure that tell teams what good looks like before they build. Clear standards let people move quickly without guessing where the lines are, which is what keeps governance from turning into a bottleneck. Keep them short enough that teams read them and specific enough that they change behavior.
Risk assessment
Score each AI use case by its potential impact and the likelihood of harm, so your attention goes where it matters most. A model recommending a playlist and a model screening loan applications carry very different stakes, and your controls should reflect that. A repeatable scoring method keeps these calls consistent as your AI use grows.
Controls and guardrails
Put the approval gates, access limits, and bias and quality testing in place that keep AI inside the lines your policies draw. Controls are where intent turns into enforcement, and they are the difference between a rule and a habit. Match the strength of each control to the risk it covers, so low stakes uses do not drown in process.
Transparency and documentation
Keep model documentation, decision logs, and clear disclosure to the people affected when AI makes or shapes a decision about them. Good records let you explain what a model did and why, which matters the moment a customer, an auditor, or a regulator asks. They also make it far easier to spot and fix a problem after the fact.
Human oversight
Keep a person in the loop for the calls that carry real consequences, with the authority to pause or override the model. Automation earns its keep, but a human should still hold the final say on decisions that affect someone's job, money, or health. Oversight only works if that person has the context and the power to act, not just a rubber stamp.
Continuous monitoring
Run ongoing checks on how models behave and whether your controls still hold, rather than a review that happens once a year. Models drift, data shifts, and new AI shows up between audits, so a point in time check goes stale fast. Continuous monitoring is what keeps a program honest the other 364 days of the year.
AI governance frameworks and standards
Most programs anchor on a small number of frameworks. Some are voluntary standards you adopt to put good practice to work. One is binding law. Knowing which is which, and what each gives you, saves you from running them on parallel tracks.
ISO 42001
ISO 42001 is the one standard on this list you can certify against. It sets out the requirements for an AI management program that a third party auditor can assess, which is what makes it useful for proving responsible AI rather than just claiming it. Vanta was among the first companies to earn ISO 42001 certification, and the standard anchors a growing number of programs because a certificate travels well in enterprise deals and diligence.
NIST AI Risk Management Framework
The NIST AI Risk Management Framework is a voluntary, risk first structure from the US National Institute of Standards and Technology. Its value is a usable operating model built on four functions, govern, map, measure, and manage, that carry a team from naming its AI risks through to treating them. It pairs well with ISO 42001, since NIST gives you the how and ISO gives you the certifiable what.
EU AI Act
The EU AI Act is binding law, and it sorts AI into tiers by risk, placing the heaviest duties on the uses most likely to cause harm. It took effect in 2024 and is phasing in its obligations across 2025 to 2027. One point catches many teams off guard. You do not have to build AI to fall under it. Companies that only use AI tools from other vendors can still count as deployers with duties of their own. If any of your AI touches the EU market, the Act's requirements are worth mapping early.
Levels of AI governance maturity
AI governance is not all or nothing. Programs tend to fall into one of three levels, and locating yourself on the scale tells you what to build next.
Informal
Some good practices exist in pockets, but there's no framework, owner, or shared strategy behind them. Governance rides on individual judgment, which works right up until the person who cared about it moves on. Most companies sit here without realizing it.
Ad hoc
Policies and processes exist, but they went in as one off reactions to a specific incident or request rather than as part of a plan. Coverage is patchy, so some AI uses are tightly controlled while others sit untouched. It can feel like progress, but the gaps are usually where the next problem surfaces.
Formal
A deliberate, unified program runs across the organization, with named owners, an adopted framework, and monitoring that keeps it current. New AI use cases enter a known process instead of slipping through, and you can show an auditor or a customer exactly how the program works. This is the level that holds up as your AI footprint grows.
Moving up a level comes down to three moves. Formalize ownership, adopt a framework to anchor the work, and put monitoring in place so the program stays live between audits instead of resetting each time.
{{cta_withimage6="/cta-blocks"}}
Who is responsible for AI governance?
AI governance is a shared responsibility with a single point of accountability at the top. Executives own how the business uses AI, and a group drawn from across the company does the daily work. What sets it apart from ordinary IT governance is who sits at the table, because the people building and training the models belong there too.
You do not need a large team to start. A small standing group with a clear owner and the right functions represented will carry a program much further than a long document nobody maintains.
How to implement an AI governance program
A first program comes together in a repeatable order. These six steps take you from finding your AI to keeping it in line, and they map loosely onto the frameworks above so the pieces connect.
Step 1. Inventory where AI is used
Start by finding every place AI shows up, whether your team built it or bought it, and include the unofficial tools people picked up on their own. You cannot write a policy for AI you do not know exists, and shadow AI is where much of the risk hides.
Step 2. Choose a framework to anchor the program
Pick one framework as your spine so you are not assembling controls from scratch. ISO 42001 suits teams that want something certifiable, the NIST AI Risk Management Framework offers a flexible operating model, and the EU AI Act sets the floor if you touch the EU market. You can layer others on later.
Step 3. Assign ownership and accountability
Stand up your governance group and name an owner for each AI use case. Ownership is what turns a policy from a document into something that gets enforced, reviewed, and answered for.
Step 4. Assess and map AI risks
Score each use case by how much harm it could do and how likely that harm is, then map controls to the risks that rank highest. This is where the NIST functions to map and measure earn their place, and it keeps effort focused instead of spread evenly across things that do not need it.
Step 5. Set policies and controls
Write the rules that govern acceptable use, data handling, and model quality, then put the guardrails behind them, the approval gates, access limits, and testing for bias and accuracy. Policies without controls are suggestions.
Step 6. Monitor continuously and improve
Track how models behave and whether your controls still hold over time, and feed what you learn back into the program. AI changes, data shifts, and regulation moves, so governance has to be a loop, not a one time project. This is also where a platform that monitors controls continuously does the heavy lifting a spreadsheet cannot.
Done in this order, governance stops being a scramble before each audit and becomes something that runs quietly in the background.
Make AI governance stick
AI is not slowing down, and the rules around it are only getting firmer. The teams that stay ahead treat governance as a living program, not a document they revisit once a year. They inventory their AI, anchor on a framework, give every use an owner, and keep watch as models and regulations change.
That last part, keeping a program current, is where tooling matters most. Vanta brings an AI governance program onto one platform and monitors the controls behind it continuously, with ISO 42001 as the certifiable anchor that proves your practices to the people asking. If you are ready to turn AI governance from a scramble into something that runs on its own, start with ISO 42001 or see how Vanta handles it in a demo.
{{cta_testimonial6="/cta-blocks"}}
Frequently asked questions
What is an AI governance framework?
An AI governance framework is a structured set of principles, processes, and controls for managing how AI gets built and used. Frameworks fall into two camps, voluntary standards like ISO 42001, the NIST AI Risk Management Framework, and the OECD AI Principles, and binding law like the EU AI Act. Most mature programs use a voluntary standard to put a legal requirement into practice.
What is the difference between AI governance and AI ethics?
AI ethics is the set of values that defines what your organization considers fair and acceptable. AI governance is the structure of policies, owners, and controls that puts those values into practice. Ethics says the AI should be fair, and governance builds the testing and monitoring that make it so.
Is AI governance the same as AI risk management?
No. AI risk management is one function inside AI governance, focused on finding and treating risks like bias, inaccuracy, and regulatory exposure. Governance is broader, adding the ownership, policies, and accountability that turn risk decisions into lasting practice.
Which AI governance framework should we use?
It depends on what you are trying to prove. Choose ISO 42001 if you want a certificate that reassures buyers and investors, the NIST AI Risk Management Framework if you want a flexible operating model to start with, and the EU AI Act as your baseline if any of your AI touches the EU market. Many teams combine a voluntary standard with the legal one.
Governance
What is AI governance and how do you build a program?
Looking to upgrade to continuous, automated GRC and get visibility across your entire program?
Over the past two years, AI has moved from experiment to everyday infrastructure. It drafts customer emails, screens job applicants, flags fraud, writes code, and sits inside tools your teams already use, often without anyone deciding it should. Adoption has been fast, cheap, and mostly bottom up, which means most organizations are now running more AI than their leaders can name.
That speed has outrun the controls around it. Regulators have taken notice, with the EU AI Act now phasing in duties through 2027 and more rules close behind, and enterprise buyers, investors, and boards have started asking harder questions about how you manage the AI you build and buy. A single unreviewed tool, or one AI answer nobody can vouch for, is now enough to stall a deal or land a company in a headline, which means the pressure to get this right is arriving from customers and regulators at once. The people newly answerable for it, often a security lead, a GRC team, or an executive who inherited the topic, rarely started with a map.
The encouraging news is that the path is well marked. The frameworks, roles, and moves that hold AI accountable are established enough to borrow rather than invent, so a credible program is a matter of weeks of focused work, not quarters. What you need first is a clear picture of the pieces and the order they belong in. This article walks through why the topic turned urgent, the frameworks and roles that anchor a program, and the six steps to build one that keeps pace as your AI and the rules around it change.
What is AI governance?
AI governance is the set of policies, processes, controls, and accountability structures an organization uses to build, buy, deploy, and oversee artificial intelligence responsibly and in line with its goals, legal duties, and values. Put plainly, it's how a company decides which AI it will rely on, who answers for it, and where the guardrails sit, before a model is ever pointed at a real decision.
In practice, governance is what turns good intentions into something real. Most teams already agree their AI should be fair, accurate, and transparent. Governance is the part that makes those words hold up, translating them into rules people follow, owners who answer for them, and monitoring that surfaces problems while they're still small. Without it, those principles live on a slide and nowhere else.
It has also stopped being optional. AI is spreading through most companies faster than the oversight around it, often through tools that showed up without a review, and binding rules are now catching up to hold organizations accountable for how their AI behaves. More AI in more places, and more scrutiny on all of it, is why governance has moved from an afterthought to a question your buyers, regulators, and board expect you to answer.
Why is AI governance important?
AI governance matters now because organizations are adopting AI faster than they can secure or oversee it, and that gap is already expensive.
In its 2025 Cost of a Data Breach Report, IBM found that 13% of organizations had a breach involving an AI model or application, and 97% of those lacked proper AI access controls. Another 63% of breached organizations either don't have an AI governance policy or are still developing a policy. Shadow AI on its own added around $670,000 to the average breach cost.
Those failures share a root cause. AI behaves in ways traditional IT controls were not built to catch. Models produce biased or discriminatory outputs, invent facts that read as authoritative, drift as the underlying data shifts, and shape decisions with little human review. Layer on data leaking into outside models and fresh duties under regulation, and the exposure turns financial, legal, and reputational at once.
Governance is how you move fast without absorbing that risk. It gives every use of AI an owner, a policy, and a check, so you adopt AI deliberately instead of discovering months later that you have lost track of what your own tools are doing. AI adoption is outpacing the security and governance meant to keep it in check.
The key components of an AI governance program
A working AI governance program is built from a recognizable set of parts. Name them, and you have a checklist for what you are missing.
AI inventory
Start with a living record of every AI tool and model in use, whether your team built it or bought it, including the ones nobody formally approved. You cannot govern what you cannot see, and most companies underestimate how much AI has already crept into their workflows through everyday tools. The inventory is the foundation every other component builds on.
Clear ownership
Name accountability that runs from an executive sponsor down to the person responsible for each AI use case, so every decision has someone behind it. Ownership is what separates a policy that gets enforced from one that gets ignored. When something goes wrong, you want to know who answers for it before the incident, not during.
Policies and standards
Write the rules for acceptable use, data handling, model quality, and disclosure that tell teams what good looks like before they build. Clear standards let people move quickly without guessing where the lines are, which is what keeps governance from turning into a bottleneck. Keep them short enough that teams read them and specific enough that they change behavior.
Risk assessment
Score each AI use case by its potential impact and the likelihood of harm, so your attention goes where it matters most. A model recommending a playlist and a model screening loan applications carry very different stakes, and your controls should reflect that. A repeatable scoring method keeps these calls consistent as your AI use grows.
Controls and guardrails
Put the approval gates, access limits, and bias and quality testing in place that keep AI inside the lines your policies draw. Controls are where intent turns into enforcement, and they are the difference between a rule and a habit. Match the strength of each control to the risk it covers, so low stakes uses do not drown in process.
Transparency and documentation
Keep model documentation, decision logs, and clear disclosure to the people affected when AI makes or shapes a decision about them. Good records let you explain what a model did and why, which matters the moment a customer, an auditor, or a regulator asks. They also make it far easier to spot and fix a problem after the fact.
Human oversight
Keep a person in the loop for the calls that carry real consequences, with the authority to pause or override the model. Automation earns its keep, but a human should still hold the final say on decisions that affect someone's job, money, or health. Oversight only works if that person has the context and the power to act, not just a rubber stamp.
Continuous monitoring
Run ongoing checks on how models behave and whether your controls still hold, rather than a review that happens once a year. Models drift, data shifts, and new AI shows up between audits, so a point in time check goes stale fast. Continuous monitoring is what keeps a program honest the other 364 days of the year.
AI governance frameworks and standards
Most programs anchor on a small number of frameworks. Some are voluntary standards you adopt to put good practice to work. One is binding law. Knowing which is which, and what each gives you, saves you from running them on parallel tracks.
ISO 42001
ISO 42001 is the one standard on this list you can certify against. It sets out the requirements for an AI management program that a third party auditor can assess, which is what makes it useful for proving responsible AI rather than just claiming it. Vanta was among the first companies to earn ISO 42001 certification, and the standard anchors a growing number of programs because a certificate travels well in enterprise deals and diligence.
NIST AI Risk Management Framework
The NIST AI Risk Management Framework is a voluntary, risk first structure from the US National Institute of Standards and Technology. Its value is a usable operating model built on four functions, govern, map, measure, and manage, that carry a team from naming its AI risks through to treating them. It pairs well with ISO 42001, since NIST gives you the how and ISO gives you the certifiable what.
EU AI Act
The EU AI Act is binding law, and it sorts AI into tiers by risk, placing the heaviest duties on the uses most likely to cause harm. It took effect in 2024 and is phasing in its obligations across 2025 to 2027. One point catches many teams off guard. You do not have to build AI to fall under it. Companies that only use AI tools from other vendors can still count as deployers with duties of their own. If any of your AI touches the EU market, the Act's requirements are worth mapping early.
Levels of AI governance maturity
AI governance is not all or nothing. Programs tend to fall into one of three levels, and locating yourself on the scale tells you what to build next.
Informal
Some good practices exist in pockets, but there's no framework, owner, or shared strategy behind them. Governance rides on individual judgment, which works right up until the person who cared about it moves on. Most companies sit here without realizing it.
Ad hoc
Policies and processes exist, but they went in as one off reactions to a specific incident or request rather than as part of a plan. Coverage is patchy, so some AI uses are tightly controlled while others sit untouched. It can feel like progress, but the gaps are usually where the next problem surfaces.
Formal
A deliberate, unified program runs across the organization, with named owners, an adopted framework, and monitoring that keeps it current. New AI use cases enter a known process instead of slipping through, and you can show an auditor or a customer exactly how the program works. This is the level that holds up as your AI footprint grows.
Moving up a level comes down to three moves. Formalize ownership, adopt a framework to anchor the work, and put monitoring in place so the program stays live between audits instead of resetting each time.
{{cta_withimage6="/cta-blocks"}}
Who is responsible for AI governance?
AI governance is a shared responsibility with a single point of accountability at the top. Executives own how the business uses AI, and a group drawn from across the company does the daily work. What sets it apart from ordinary IT governance is who sits at the table, because the people building and training the models belong there too.
You do not need a large team to start. A small standing group with a clear owner and the right functions represented will carry a program much further than a long document nobody maintains.
How to implement an AI governance program
A first program comes together in a repeatable order. These six steps take you from finding your AI to keeping it in line, and they map loosely onto the frameworks above so the pieces connect.
Step 1. Inventory where AI is used
Start by finding every place AI shows up, whether your team built it or bought it, and include the unofficial tools people picked up on their own. You cannot write a policy for AI you do not know exists, and shadow AI is where much of the risk hides.
Step 2. Choose a framework to anchor the program
Pick one framework as your spine so you are not assembling controls from scratch. ISO 42001 suits teams that want something certifiable, the NIST AI Risk Management Framework offers a flexible operating model, and the EU AI Act sets the floor if you touch the EU market. You can layer others on later.
Step 3. Assign ownership and accountability
Stand up your governance group and name an owner for each AI use case. Ownership is what turns a policy from a document into something that gets enforced, reviewed, and answered for.
Step 4. Assess and map AI risks
Score each use case by how much harm it could do and how likely that harm is, then map controls to the risks that rank highest. This is where the NIST functions to map and measure earn their place, and it keeps effort focused instead of spread evenly across things that do not need it.
Step 5. Set policies and controls
Write the rules that govern acceptable use, data handling, and model quality, then put the guardrails behind them, the approval gates, access limits, and testing for bias and accuracy. Policies without controls are suggestions.
Step 6. Monitor continuously and improve
Track how models behave and whether your controls still hold over time, and feed what you learn back into the program. AI changes, data shifts, and regulation moves, so governance has to be a loop, not a one time project. This is also where a platform that monitors controls continuously does the heavy lifting a spreadsheet cannot.
Done in this order, governance stops being a scramble before each audit and becomes something that runs quietly in the background.
Make AI governance stick
AI is not slowing down, and the rules around it are only getting firmer. The teams that stay ahead treat governance as a living program, not a document they revisit once a year. They inventory their AI, anchor on a framework, give every use an owner, and keep watch as models and regulations change.
That last part, keeping a program current, is where tooling matters most. Vanta brings an AI governance program onto one platform and monitors the controls behind it continuously, with ISO 42001 as the certifiable anchor that proves your practices to the people asking. If you are ready to turn AI governance from a scramble into something that runs on its own, start with ISO 42001 or see how Vanta handles it in a demo.
{{cta_testimonial6="/cta-blocks"}}
Frequently asked questions
What is an AI governance framework?
An AI governance framework is a structured set of principles, processes, and controls for managing how AI gets built and used. Frameworks fall into two camps, voluntary standards like ISO 42001, the NIST AI Risk Management Framework, and the OECD AI Principles, and binding law like the EU AI Act. Most mature programs use a voluntary standard to put a legal requirement into practice.
What is the difference between AI governance and AI ethics?
AI ethics is the set of values that defines what your organization considers fair and acceptable. AI governance is the structure of policies, owners, and controls that puts those values into practice. Ethics says the AI should be fair, and governance builds the testing and monitoring that make it so.
Is AI governance the same as AI risk management?
No. AI risk management is one function inside AI governance, focused on finding and treating risks like bias, inaccuracy, and regulatory exposure. Governance is broader, adding the ownership, policies, and accountability that turn risk decisions into lasting practice.
Which AI governance framework should we use?
It depends on what you are trying to prove. Choose ISO 42001 if you want a certificate that reassures buyers and investors, the NIST AI Risk Management Framework if you want a flexible operating model to start with, and the EU AI Act as your baseline if any of your AI touches the EU market. Many teams combine a voluntary standard with the legal one.
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). |
Explore more GRC articles
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Implementing a GRC program
Optimizing a GRC program
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