96% of leaders believe that adopting generative AI makes a security breach more likely. With artificial intelligence rapidly evolving, risks associated with AI systems are evolving, too.
Many organizations already deploy AI systems across departments. Employees use it to write, research, code, and analyze data. Some teams use AI to help make large business decisions. Each use case brings its own set of risks. Without clear rules, it's hard to know how it could negatively affect your company.
That's where AI risk management comes in. After all, the goal isn't to stop AI deployment. It's to help encourage responsible AI use.
In this guide, you'll learn what an AI risk management policy is. We'll cover key components, from AI governance frameworks to risk appetite and ongoing monitoring. We'll also look at common AI security risks and how regulations like the EU AI Act fit in. Let's get into it!
Artificial intelligence risk management looks at the risks AI systems can create. This includes development, implementation, deployment, and ongoing use.
As organizations adopt AI systems, they may add new AI tools across departments. An effective approach to AI risk management helps organizations do three things. They can understand where risks exist. They can decide how to address those risks. They can also establish appropriate oversight.
The goal isn't to eliminate every possible risk. Instead, effective AI risk management requires organizations to understand their risk exposure. They also need to establish controls that match the potential impact of each AI use case.
A risk management framework provides a structured approach for identifying and addressing risk. An artificial intelligence risk management framework applies these principles to AI systems. It helps identify the risks they can introduce.
Organizations can use a framework to establish a consistent risk management strategy. Rather than evaluating each individual AI project, a framework creates repeatable processes. These processes cover risk identification, assessment, mitigation, documentation, and monitoring.
Frameworks can also help organizations connect AI governance with existing risk management and regulatory compliance programs.
The NIST AI Risk Management Framework (AI RMF) is a common framework. The National Institute of Standards and Technology released it in 2023. It's design is flexible and can apply across industries and AI technologies.
The NIST AI RMF organizes AI risk management around four core functions:
Other frameworks can also contribute to an organization's approach. ISO 42001, for example, provides an AI management system framework. It includes guidance on AI governance, risk assessment, system performance, and documentation.
The important point is not simply selecting a framework. Organizations still need to translate framework guidance into policies, procedures and controls. As well as responsibilities and ongoing processes. These should fit their specific use of AI.
An AI risk management policy covers how organizations manage risks that come with using AI. It also establishes the organization's expectations for developing, purchasing, deploying, and using AI.
A strong policy should explain four things. It should state what falls within its scope. It should say who handles oversight. It should describe how to properly evaluate risks. And it should explain what happens when risks exceed the organization's defined tolerance.
The policy should also connect to existing risk management processes. Such as security, privacy, compliance, and enterprise. It should not operate as a completely separate program.
AI governance establishes accountability for how businesses develop, buy, deploy, and use AI.
A governance structure should identify who handles AI oversight. It should also show how to make decisions about AI use cases. Depending on the organization, this may involve compliance, legal, IT, or other teams.
Governance should also address AI ethics and the organization's expectations for trustworthy AI. This can include principles around transparency, accountability, fairness, privacy, security, and human oversight.
Clear governance helps ensure you use AI consistently with your broader risk management strategy.
Not every AI use case carries the same level of risk. Establishing a risk appetite helps you decide how much risk you are willing to accept as a business.
For example, an organization may consider an internal productivity tool minimally risky. It may apply stricter requirements to an AI system that processes sensitive information. Or one that influences important decisions.
A risk appetite can establish:
Defining risk appetite gives teams a way to make decisions based on risk levels. It means they don't treat every AI system the same way.
Risk identification should happen before deploying an AI system. Not after something goes wrong.
Organizations should evaluate the intended use of an AI system. They should look at the data it processes and how its outputs will be used. They should also ask what could happen if the system produces inaccurate results.
A risk assessment may examine:
The assessment should also consider the entire AI lifecycle. This runs from development and testing through deployment and ongoing monitoring.
Once an organization identifies risk, they need controls and processes to address them.
Risk mitigation can include:
For example, an organization may need human review before an AI system's output can be used. It may also restrict employees from entering confidential information into public AI tools.
Risk management practices should be documented and repeatable. That way, teams can consistently evaluate new AI use cases. They can also respond when existing systems change.
AI risk management continues after deploying an AI system.
AI systems often change over time. Data changes, vendors modify platforms, and employees discover new ways to use them. AI technologies evolve quickly, which makes ongoing monitoring especially important.
Organizations should track AI systems for changes in performance and accuracy. They should also watch for changes in security, bias, and other risk indicators.
Monitoring ensures that AI systems continue to operate within their approved risk tolerance.
Documentation creates accountability. It also gives organizations evidence of how organizations evaluated and managed AI risks.
An organization may maintain records covering AI system inventories, risk assessments, and incidents. As well as testing results, approval decisions, and control activities. This is not an exhaustive list, but the point is that maintaining an AI system inventory is important.
The inventory can identify what AI systems the organization uses. It can also show who owns them, what they use them for, and their associated risk level. This is helpful to have in case of an audit or legal trouble.
The policy itself should also have a defined review schedule. AI technologies, regulations, and organizational AI practices all evolve. Prioritize keeping up with the changes.
The risks associated with AI technologies vary. It's usually based on how companies develop the AI, deploy it, and use it. Organizations should consider the following areas when building an AI risk management program.
AI systems may process sensitive, confidential, or regulated information. Employees who use an AI system may also share information with an external AI tool by accident.
Security risk assessments should consider how to collect information. As well as how to safely store, send, and protect it.
AI systems can generate inaccurate or unexpected outputs. Organizations should establish appropriate validation and human review requirements. This matters most when AI outputs could affect important business decisions.
AI models can reflect biases present in their training data or development processes. Organizations should consider whether an AI system could produce unfair or discriminatory outcomes. They should also decide whether extra testing or oversight is necessary.
AI ethics should be built into risk assessments. It should not be treated as a separate consideration.
AI systems are increasingly easy to access. This means employees may adopt AI tools without formal approval.
Organizations should establish clear policies for acceptable AI use. They should also maintain visibility into the AI systems across the organization.
Organizations often rely on vendors when implementing AI. Vendor assessments should consider security, data retention, model training practices, and contractual requirements. It also covers how responsibility for AI risks divides between vendor and customer.
The regulatory landscape surrounding AI continues to develop. Organizations should test applicable requirements based on their industry, location, and data. They should also take AI use cases into consideration, too.
Generative AI systems have made implementing AI easier for organizations and employees. At the same time, accessibility creates new governance challenges.
Employees may use generative AI for research, writing, coding, analysis, and other tasks. Without clear requirements, organizations may have limited visibility into which tools employees use. They may also not know what information they are sharing.
An approach to AI risk should establish clear expectations for generative AI use. Expectations may include:
Organizations should also consider how their controls apply in two different cases. One is developing and deploying AI internally. The other is purchasing AI capabilities from third-party vendors.
The goal of AI risk management is not necessarily to prevent AI use. Instead, a clear policy helps organizations manage AI responsibly. It also helps employees understand how they can use AI within established boundaries.
The growing adoption of AI has created a changing regulatory environment. An AI risk management program should be thorough. Addressing both internal governance requirements and applicable external regulations.
Risk management and regulatory compliance should work together. They should not operate as separate processes. Organizations can build regulatory requirements into their risk assessments. They can add them into their controls, documentation, and monitoring practices, too.
The EU AI Act uses a risk-based approach to AI regulation. Different requirements may apply. This depends on the type and level of risk associated with an AI system.
For high-risk AI systems, requirements can be more extensive. Including tasks like risk management, data governance, technical documentation, and human oversight. Organizations should decide whether the EU AI Act applies to their AI activities. They should also understand the specific requirements that may apply.
This is why you should evaluate AI systems by their intended use and potential impact. Applying identical controls to every system won't work.
Organizations do not necessarily need to create an entirely separate program for AI.
An artificial intelligence risk management framework can connect with existing programs. Including enterprise risk management, cybersecurity, privacy, vendor risk, and compliance.
This allows organizations to build on existing risk management processes. All while adding controls specific to AI.
Frameworks such as the NIST AI RMF can also provide a common structure. This structure can help integrate AI governance into an organization's broader strategy.
The landscape of AI risk continues to change. Organizations adopt new technologies, and regulators establish new requirements.
Organizations should periodically review their AI risk management policy, risk assessments, and controls. This helps ensure they remain appropriate as regulations and AI use cases evolve.
An effective AI risk management program turns policy into an ongoing process. Implementing AI risk management requires organizations to identify their AI systems. It pushes them to also assess associated risks and establish controls. They must also watch those systems throughout the entire AI lifecycle.
A comprehensive AI risk management strategy can follow these steps:
AI development is moving fast. New tools, new use cases, and new regulations show up all the time. A clear AI risk management policy helps organizations keep up without slowing down.
With a strong program and framework in place, teams can use AI responsibly. Everyone knows who is accountable, which uses are ok, and when they need extra review. Your team can catch risks early, not after something goes wrong.
AI systems must also stay within your approved use cases and risk appetite. That takes ongoing monitoring, solid documentation, and regular policy reviews. When you build these habits across the AI lifecycle, your controls stay current as AI changes.
You don't have to do it all at once. Start by identifying the AI systems your organization uses. Then define your risk appetite and assign clear owners. From there, you can build a repeatable program that lets your teams use AI with confidence.
