AI Risk Assessment 101: Identifying and Mitigating Risks in AI Systems
Content

Our Newsletter

Get Our Resources Delivered Straight To Your Inbox

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
We respect your privacy. Learn more here.

Introduction

What Is AI Risk Assessment?

Before companies can conduct an AI risk assessment, they need to have a clear picture of what AI risk is. In simplest terms, AI risk can be expressed with the following simple formula:

AI risk = (likelihood of an AI model error or exploit) x (its potential effect).

This formula articulates AI risk as a product of both the likelihood of an AI error occurring and the damage that would be done in that case, but it vastly oversimplifies the many different ways in which AI risk can arise. 

For example, model errors could arise in the form of data poisoning, hallucinations, prompt injection, data exfiltration and a wide number of other forms. The severity of their impact will also vary based on where in the data pipeline the error takes place. In addition, the full legal, operational, financial, and reputational damage is often difficult to quantify completely. 

Understanding AI Risk Assessment

From the EU's AI Act and General Data Protection Regulation (GDPR) to Canada's Artificial Intelligence and Data Act (AIDA), several governing bodies have adopted legislation to govern how organisations conduct their AI operations. The US has yet to implement an authoritative legal framework to govern its AI processes, the proposed AI Bill of Rights and the National Institute of Science and Technology's (NIST) AI Risk Management Framework (AI RMF) can give organisations a reference point for how to assess and reduce their AI risk. 

Definition and Scope

The AI RMF defines AI risk as "the composite measure of an event’s probability of occurring and the magnitude or degree of the consequences of the corresponding event. The impacts, or consequences, of AI systems can be positive, negative, or both and can result in opportunities or threats." 

The result of this definition is that the scope of AI risk assessment and management should go beyond minimizing the probability of negative threats, and should include inquiries into how AI processes can be better leveraged for the greater good. With that definition in mind, the AI RMF describes AI risk assessment and management as consisting of four phases: GOVERN, MAP, MEASURE, and MANAGE. An overview of each phase is as follows:

Govern

The first step refers to establishing the administrative and policy infrastructure needed to carry out the technical AI risk management phase. It includes but is not limited to:

  • Implementing the structures needed to carry out the MAP, MEASURE, and MANAGE phases of the risk management process
  • Ensuring that accountability structures are in place at each level, so that all policies and procedures are followed
  • Executing Diversity, Equity, and Inclusiveness (DEI) efforts at each level, to eliminate bias

 

Map

The MAP phase seeks to identify all of the internal and external interdependencies that the AI model has on broader social and business processes. This allows risk management teams to better understand which phases their operations will be impacted by each form of a model error or exploit. It includes but is not limited to:

  • Establishing and understanding the context of the AI model and error
  • Categorizing the AI system 
  • Comparing the desired benefits, capabilities, and outcomes of the AI model with current benchmarks

Measure

The MEASURE phase uses quantitative, qualitative, and mixed methods to oversee the tool's performance and assess the impact that a failure would have. The MEASURE phase includes but is not limited to:

  • Identifying and applying the appropriate methods and metrics 
  • Evaluating current AI systems for trustworthy characteristics 
  • Establishing mechanisms that track AI risk over time 

Manage

The MANAGE phase applies the procedures and protocols created within the GOVERN phase when an AI risk management incident occurs. It includes but is not limited to:

  • Triaging the AI risks that were found during the MAP and MEASURE phase
  • Receiving input from relevant AI actors during the planning, preparation, implementation, and documentation of all response processes
  • Managing AI risks from third-party actors 

By following the many subcategories listed within the GOVERN, MAP, MEASURE, and MANAGE phases put forth within the AI RMF, organisations may better assess the various AI risks that they face, and respond to them accordingly. 

Types of Risks

There are many different types of risk within AI systems. An organisation's specific use of AI will also play a role in determining which type of risk it faces. The most prevalent types of AI risk include:

  • Ethical risk: Ethical risk violates important norms, standards, and governance policies, and often perpetuates social inequality. Both algorithmic and data bias can result in flawed AI models that produce exclusionary or prejudiced outputs, often at the expense of underrepresented groups.
  • Operational risk: Data drift, hallucinations, corrupt data, and other AI errors can damage a company's day-to-day operations. The results can be increased downtime, communication disruptions, and inaccurate predictions, causing businesses to make wrong decisions.
  • Compliance risk: When AI models go wrong, they may cause businesses to violate important regulatory standards. Examples include unfair rejection of loan applications for people of color or overlooked resumes from applicants in a single-gender-dominated field — both of which violate consumer protection and labor laws. 
  • Reputational risk: When ethical and compliance violations take place, businesses risk tarnishing their reputation as well. This can damage brand trust, and prompt some values-based consumers to take their business elsewhere.

Another critical AI risk is security, as some AI vulnerabilities can be exploited for nefarious purposes. Threat actors may use prompt injection or other attack methods to exfiltrate data or generate incorrect outputs, which can lead to further risks down the road. 

Importance of AI Risk Assessment

AI risk assessment is critical for preventing mishaps before they occur, and also for minimizing the damage when they do. The most important reasons for conducting an AI risk assessment are to:

  • Prevent Harm: AI mishaps can affect real human lives. AI risk assessments aim to minimize the likelihood of a model error so that users and stakeholders will remain safe from harm.
  • Ensure Compliance: AI frameworks such as AIDA and the AI Act require thorough documentation on every part of the development cycle. Risk assessments can help demonstrate the developers' intentions, and are often mandatory for meeting regulatory and legal requirements.
  • Enhance Trust: When teams conduct thorough AI risk assessments, they prove to users and stakeholders that they're committed to using the technology for good. This builds brand trust and can improve corporate image. 

From remaining compliant with regulatory bodies to ensuring equity for their consumers, organisations that are diligent with their AI risk assessment policies benefit not only their own operations but also society as a whole. 

Steps in Conducting AI Risk Assessment

The NIST risk management framework contains a playbook for how organisations should conduct their risk assessments.  The steps are neither comprehensive nor mandatory, but are meant to be used as a reference point as each AI team develops a risk assessment infrastructure that works for them. Consult the AI RMF for the exact details as you build out your risk assessment strategy, but here's a general layout of the phases you'll likely encounter. 

Identifying Risks

The first step in mitigating risk in AI systems is to identify where it exists. A scenario analysis considers the possible events that could happen should an AI error occur, and allows organisations to identify the most impactful risks and prepare for the worst. Businesses should also consult with other stakeholders to see how an error or exploit would affect them. 

Analysing Risks

Multiple techniques exist that enable AI teams to analyze the risks their models face. A few of the most common AI risk analysis methods are: 

  • Bow-tie analysis — Dividing each individual risk into contributing factors and their consequences, then listing those to mitigate the core risk
  • Delphi — Brainstorming in conjunction with experts to create a comprehensive list of all risks
  • SWIFT analysis — Gathering in team meetings to pose "what-if" questions (short for Structured What-IF Technique)
  • Decision-tree analysis — Plotting out all possible outcomes as a risk scenario evolves.

Evaluating Risks

Once organisations analyze the severity of each risk, they must then decide how willing they are to expose themselves to that risk. This is known as their risk tolerance, and companies should use it to triage the most urgent needs and allocate their resources accordingly. 

Mitigating Risks

Some risk is inevitable as companies integrate AI into their operations — the real question is how they can manage it. There are many risk mitigation strategies that teams can resort to, including but not limited to:

  • Risk avoidance — Refraining from taking the risk altogether
  • Risk acceptance — Receiving the full brunt of the risk and proceeding with operations
  • Risk transfer — Shifting some of the risk to a third party
  • Risk buffering — Adding extra resources to the vulnerable part of operations to absorb the risk with greater ease
  • Risk strategizing — Creating a contingency plan for specific risks
  • Risk quantification — Accurately determining the full financial costs of the risk, to assess how it should be handled 
  • Risk diversification — Distributing the risk across multiple operations and processes, so that each will be minimally affected

The diverse nature of AI risk means that your team will have to decide which risk assessment and management tactics work best for them. Whichever configuration you choose, you'll still need to adhere to the AI governance framework(s) that applies to your industry. 

Best Practices for AI Risk Assessment

Creating an AI risk assessment infrastructure can be a daunting task, so you'll need to implement multiple best practices to achieve it. Some key risk assessment best practices include:

  • Comprehensive Risk Identification: Just as datasets and algorithmic outputs change over time, the risks that your AI system poses are dynamic as well. Be sure to return to your risk identification process early and often.  
  • Stakeholder Involvement: Because AI models contain so many interdependencies, include stakeholders from every party in the risk assessment process. That may include technical personnel such as data scientists, analysts, and engineers, but also non-technical personnel such as legal, sales, management, and even end-users.  
  • Regular Reviews and Updates: In addition to ever-changing datasets and social trends, the AI threat landscape is continuously evolving as well. Consistently review and update your AI risk assessment, so that it includes the most recent external threats and risks.

From identification all the way through mitigation, it's especially critical to think of your AI risk assessment policies as living sources of truth. Generative AI risks have gotten much greater over the last year, and new threats are sure to arise. Be flexible as you implement your risk management processes, and make every effort to keep up with the risks that new innovations may bring. 

Challenges in AI Risk Assessment: Obstacles and Solutions

Even when implementing best practices, there are still plenty of hurdles that AI development teams must clear. Some of the greatest AI risk assessment challenges and their solutions are:

Evolving AI: Despite tremendous recent advancements, AI technology is still in its nascent stages. Its capabilities are only just beginning to be discovered, so teams must invest considerable time and manpower into understanding upcoming features and the risks that they present.

  • Solution: Consult an expert. Even the leading AI giants must collaborate to glean from each other. Consult an AI risk management expert to help you identify risks that you may not have considered, and to keep you abreast of upcoming trends. 

Resource constraints: AI development and integration is a resource-intensive process, and so is managing all the risks. Even large enterprises have a hard time investing the necessary time, money, and manpower into all of their AI operations, so deciding which risks to prioritize can be a challenge. 

  • Solution: Allocate your assets. Risk mitigation strategies can help you decide how best to spend your resources, so use them to strategically allocate your assets. For example, risk diversification can show you how to split up risk across multiple operations, making the cost of mitigation easier to absorb for each. 

Integration: Each organisation's risk profile will vary, so it can be difficult to implement a single uniform solution that adheres to all regulatory requirements.

  • Solution: Follow a framework. AI governance frameworks such as the AI RMF exist to help companies create a standardized, comprehensive risk management infrastructure. They also contain supplemental tools and reference materials, so take advantage of them to build out your risk management pipeline.

By consulting an expert, leveraging risk mitigation tactics to strategically allocate your assets, and frequently referencing the frameworks that apply to you, you can overcome the most pressing AI risk management hurdles.

Final Thoughts

Whether companies are prepared for it or not, the AI revolution is fully underway. The pace of AI innovation will only accelerate in the near future, so those using AI tools must act now to identify the risk already present in their systems before it results in harm. 

Zendata integrates privacy by design across the entire data lifecycle with an emphasis on the context and risks associated with how data is used, helping mitigate AI risk downstream. If you'd like to see how our data quality and privacy practices can reduce your AI risk, check out our services today. 

Our Newsletter

Get Our Resources Delivered Straight To Your Inbox

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
We respect your privacy. Learn more here.

Related Blogs

AI Governance Maturity Models 101: Assessing Your Governance Frameworks
  • AI
  • July 5, 2024
Learn How To Asses The Maturity Of Your AI Governance Model
AI Governance Audits 101: Conducting Internal and External Assessments
  • AI
  • July 5, 2024
Learn How To Audit Your AI Governance Policies
AI Ethics Training 101: Educating Teams on Responsible AI Practices
  • AI
  • July 5, 2024
Learn How To Teach Your Teams About AI Ethics.
AI Interpretability 101: Making AI Models More Understandable to Humans
  • AI
  • July 4, 2024
Learn How To Understand AI Models
Threat Modelling, Risk Analysis and AI Governance For LLM Security
  • AI
  • July 3, 2024
Explore The Privacy, Governance and Security Challenges Posed By LLMs
AI Incident Response 101: Handling AI Failures and Unintended Consequences
  • AI
  • June 28, 2024
Discover Best Practices For AI Incident Response
Addressing Shadow AI Risks with Zendata AI Governance
  • AI
  • June 12, 2024
Learn How Zendata Helps To Address Shadow AI Risks
AI Risk Assessment 101: Identifying and Mitigating Risks in AI Systems
  • AI
  • June 6, 2024
Learn How To Identify And Assess AI Risks
From RAG to Agent Systems: The Transition to GenAI 2.0
  • AI
  • June 6, 2024
Learn More About GenAI 2.0 And What It Means For Businesses
More Blogs

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.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.





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 Risk Assessment 101: Identifying and Mitigating Risks in AI Systems

June 6, 2024

Introduction

What Is AI Risk Assessment?

Before companies can conduct an AI risk assessment, they need to have a clear picture of what AI risk is. In simplest terms, AI risk can be expressed with the following simple formula:

AI risk = (likelihood of an AI model error or exploit) x (its potential effect).

This formula articulates AI risk as a product of both the likelihood of an AI error occurring and the damage that would be done in that case, but it vastly oversimplifies the many different ways in which AI risk can arise. 

For example, model errors could arise in the form of data poisoning, hallucinations, prompt injection, data exfiltration and a wide number of other forms. The severity of their impact will also vary based on where in the data pipeline the error takes place. In addition, the full legal, operational, financial, and reputational damage is often difficult to quantify completely. 

Understanding AI Risk Assessment

From the EU's AI Act and General Data Protection Regulation (GDPR) to Canada's Artificial Intelligence and Data Act (AIDA), several governing bodies have adopted legislation to govern how organisations conduct their AI operations. The US has yet to implement an authoritative legal framework to govern its AI processes, the proposed AI Bill of Rights and the National Institute of Science and Technology's (NIST) AI Risk Management Framework (AI RMF) can give organisations a reference point for how to assess and reduce their AI risk. 

Definition and Scope

The AI RMF defines AI risk as "the composite measure of an event’s probability of occurring and the magnitude or degree of the consequences of the corresponding event. The impacts, or consequences, of AI systems can be positive, negative, or both and can result in opportunities or threats." 

The result of this definition is that the scope of AI risk assessment and management should go beyond minimizing the probability of negative threats, and should include inquiries into how AI processes can be better leveraged for the greater good. With that definition in mind, the AI RMF describes AI risk assessment and management as consisting of four phases: GOVERN, MAP, MEASURE, and MANAGE. An overview of each phase is as follows:

Govern

The first step refers to establishing the administrative and policy infrastructure needed to carry out the technical AI risk management phase. It includes but is not limited to:

  • Implementing the structures needed to carry out the MAP, MEASURE, and MANAGE phases of the risk management process
  • Ensuring that accountability structures are in place at each level, so that all policies and procedures are followed
  • Executing Diversity, Equity, and Inclusiveness (DEI) efforts at each level, to eliminate bias

 

Map

The MAP phase seeks to identify all of the internal and external interdependencies that the AI model has on broader social and business processes. This allows risk management teams to better understand which phases their operations will be impacted by each form of a model error or exploit. It includes but is not limited to:

  • Establishing and understanding the context of the AI model and error
  • Categorizing the AI system 
  • Comparing the desired benefits, capabilities, and outcomes of the AI model with current benchmarks

Measure

The MEASURE phase uses quantitative, qualitative, and mixed methods to oversee the tool's performance and assess the impact that a failure would have. The MEASURE phase includes but is not limited to:

  • Identifying and applying the appropriate methods and metrics 
  • Evaluating current AI systems for trustworthy characteristics 
  • Establishing mechanisms that track AI risk over time 

Manage

The MANAGE phase applies the procedures and protocols created within the GOVERN phase when an AI risk management incident occurs. It includes but is not limited to:

  • Triaging the AI risks that were found during the MAP and MEASURE phase
  • Receiving input from relevant AI actors during the planning, preparation, implementation, and documentation of all response processes
  • Managing AI risks from third-party actors 

By following the many subcategories listed within the GOVERN, MAP, MEASURE, and MANAGE phases put forth within the AI RMF, organisations may better assess the various AI risks that they face, and respond to them accordingly. 

Types of Risks

There are many different types of risk within AI systems. An organisation's specific use of AI will also play a role in determining which type of risk it faces. The most prevalent types of AI risk include:

  • Ethical risk: Ethical risk violates important norms, standards, and governance policies, and often perpetuates social inequality. Both algorithmic and data bias can result in flawed AI models that produce exclusionary or prejudiced outputs, often at the expense of underrepresented groups.
  • Operational risk: Data drift, hallucinations, corrupt data, and other AI errors can damage a company's day-to-day operations. The results can be increased downtime, communication disruptions, and inaccurate predictions, causing businesses to make wrong decisions.
  • Compliance risk: When AI models go wrong, they may cause businesses to violate important regulatory standards. Examples include unfair rejection of loan applications for people of color or overlooked resumes from applicants in a single-gender-dominated field — both of which violate consumer protection and labor laws. 
  • Reputational risk: When ethical and compliance violations take place, businesses risk tarnishing their reputation as well. This can damage brand trust, and prompt some values-based consumers to take their business elsewhere.

Another critical AI risk is security, as some AI vulnerabilities can be exploited for nefarious purposes. Threat actors may use prompt injection or other attack methods to exfiltrate data or generate incorrect outputs, which can lead to further risks down the road. 

Importance of AI Risk Assessment

AI risk assessment is critical for preventing mishaps before they occur, and also for minimizing the damage when they do. The most important reasons for conducting an AI risk assessment are to:

  • Prevent Harm: AI mishaps can affect real human lives. AI risk assessments aim to minimize the likelihood of a model error so that users and stakeholders will remain safe from harm.
  • Ensure Compliance: AI frameworks such as AIDA and the AI Act require thorough documentation on every part of the development cycle. Risk assessments can help demonstrate the developers' intentions, and are often mandatory for meeting regulatory and legal requirements.
  • Enhance Trust: When teams conduct thorough AI risk assessments, they prove to users and stakeholders that they're committed to using the technology for good. This builds brand trust and can improve corporate image. 

From remaining compliant with regulatory bodies to ensuring equity for their consumers, organisations that are diligent with their AI risk assessment policies benefit not only their own operations but also society as a whole. 

Steps in Conducting AI Risk Assessment

The NIST risk management framework contains a playbook for how organisations should conduct their risk assessments.  The steps are neither comprehensive nor mandatory, but are meant to be used as a reference point as each AI team develops a risk assessment infrastructure that works for them. Consult the AI RMF for the exact details as you build out your risk assessment strategy, but here's a general layout of the phases you'll likely encounter. 

Identifying Risks

The first step in mitigating risk in AI systems is to identify where it exists. A scenario analysis considers the possible events that could happen should an AI error occur, and allows organisations to identify the most impactful risks and prepare for the worst. Businesses should also consult with other stakeholders to see how an error or exploit would affect them. 

Analysing Risks

Multiple techniques exist that enable AI teams to analyze the risks their models face. A few of the most common AI risk analysis methods are: 

  • Bow-tie analysis — Dividing each individual risk into contributing factors and their consequences, then listing those to mitigate the core risk
  • Delphi — Brainstorming in conjunction with experts to create a comprehensive list of all risks
  • SWIFT analysis — Gathering in team meetings to pose "what-if" questions (short for Structured What-IF Technique)
  • Decision-tree analysis — Plotting out all possible outcomes as a risk scenario evolves.

Evaluating Risks

Once organisations analyze the severity of each risk, they must then decide how willing they are to expose themselves to that risk. This is known as their risk tolerance, and companies should use it to triage the most urgent needs and allocate their resources accordingly. 

Mitigating Risks

Some risk is inevitable as companies integrate AI into their operations — the real question is how they can manage it. There are many risk mitigation strategies that teams can resort to, including but not limited to:

  • Risk avoidance — Refraining from taking the risk altogether
  • Risk acceptance — Receiving the full brunt of the risk and proceeding with operations
  • Risk transfer — Shifting some of the risk to a third party
  • Risk buffering — Adding extra resources to the vulnerable part of operations to absorb the risk with greater ease
  • Risk strategizing — Creating a contingency plan for specific risks
  • Risk quantification — Accurately determining the full financial costs of the risk, to assess how it should be handled 
  • Risk diversification — Distributing the risk across multiple operations and processes, so that each will be minimally affected

The diverse nature of AI risk means that your team will have to decide which risk assessment and management tactics work best for them. Whichever configuration you choose, you'll still need to adhere to the AI governance framework(s) that applies to your industry. 

Best Practices for AI Risk Assessment

Creating an AI risk assessment infrastructure can be a daunting task, so you'll need to implement multiple best practices to achieve it. Some key risk assessment best practices include:

  • Comprehensive Risk Identification: Just as datasets and algorithmic outputs change over time, the risks that your AI system poses are dynamic as well. Be sure to return to your risk identification process early and often.  
  • Stakeholder Involvement: Because AI models contain so many interdependencies, include stakeholders from every party in the risk assessment process. That may include technical personnel such as data scientists, analysts, and engineers, but also non-technical personnel such as legal, sales, management, and even end-users.  
  • Regular Reviews and Updates: In addition to ever-changing datasets and social trends, the AI threat landscape is continuously evolving as well. Consistently review and update your AI risk assessment, so that it includes the most recent external threats and risks.

From identification all the way through mitigation, it's especially critical to think of your AI risk assessment policies as living sources of truth. Generative AI risks have gotten much greater over the last year, and new threats are sure to arise. Be flexible as you implement your risk management processes, and make every effort to keep up with the risks that new innovations may bring. 

Challenges in AI Risk Assessment: Obstacles and Solutions

Even when implementing best practices, there are still plenty of hurdles that AI development teams must clear. Some of the greatest AI risk assessment challenges and their solutions are:

Evolving AI: Despite tremendous recent advancements, AI technology is still in its nascent stages. Its capabilities are only just beginning to be discovered, so teams must invest considerable time and manpower into understanding upcoming features and the risks that they present.

  • Solution: Consult an expert. Even the leading AI giants must collaborate to glean from each other. Consult an AI risk management expert to help you identify risks that you may not have considered, and to keep you abreast of upcoming trends. 

Resource constraints: AI development and integration is a resource-intensive process, and so is managing all the risks. Even large enterprises have a hard time investing the necessary time, money, and manpower into all of their AI operations, so deciding which risks to prioritize can be a challenge. 

  • Solution: Allocate your assets. Risk mitigation strategies can help you decide how best to spend your resources, so use them to strategically allocate your assets. For example, risk diversification can show you how to split up risk across multiple operations, making the cost of mitigation easier to absorb for each. 

Integration: Each organisation's risk profile will vary, so it can be difficult to implement a single uniform solution that adheres to all regulatory requirements.

  • Solution: Follow a framework. AI governance frameworks such as the AI RMF exist to help companies create a standardized, comprehensive risk management infrastructure. They also contain supplemental tools and reference materials, so take advantage of them to build out your risk management pipeline.

By consulting an expert, leveraging risk mitigation tactics to strategically allocate your assets, and frequently referencing the frameworks that apply to you, you can overcome the most pressing AI risk management hurdles.

Final Thoughts

Whether companies are prepared for it or not, the AI revolution is fully underway. The pace of AI innovation will only accelerate in the near future, so those using AI tools must act now to identify the risk already present in their systems before it results in harm. 

Zendata integrates privacy by design across the entire data lifecycle with an emphasis on the context and risks associated with how data is used, helping mitigate AI risk downstream. If you'd like to see how our data quality and privacy practices can reduce your AI risk, check out our services today.