AI Risk Management

What is AI Risk Management?

AI risk management identifies and addresses the risks of using artificial intelligence systems. It aims to minimize any negative impacts that AI may potentially have while maximizing the benefits. 

It involves applying practices and principles, focusing on formal risk management frameworks specifically designed for AI.

AI is generally applied in risk management through the following avenues:

• Identifying situations or conditions that might pose risks
• Understanding the potential impact if those risks materialize
• Evaluating the likelihood of those risks happening based on the current context

AI is also used to establish controls to handle emerging risks. These systems are not static; they continuously monitor for changes and adapt to evolving threats.

How Does AI in Risk Management Work?

AI in risk management uses AI technologies like machine learning to identify, assess, and mitigate risks. One of its key strengths is processing unstructured data and here’s how it works.

• AI handles diverse data types like text and images, which don’t fit traditional formats.
• AI extracts insights from complex data, enhancing understanding and decision-making
• AI analyzes data quickly, enabling faster responses and immediate reports.
• AI manages large volumes of data and adapts to rising data demands

This wasn’t the case before, as computers struggled with areas requiring more human-like judgment and reasoning. However, they are becoming more capable of handling these “gray areas.”

Moreover, AI’s role in risk management became even more defined with the release of AI RMF 1.0 by the National Institute of Standards and Technology (NIST) on January 26, 2023. This framework introduced a formal definition of risk: the combination of an event’s probability and the magnitude of its consequences. 

NIST highlighted AI-related risks, ranging from biased hiring algorithms to trading algorithms triggering market instability. These risks often emerge from the data used to train AI, the system’s design, application, and human interaction.

Does it seem overwhelming?

Keep your AI systems compliant with the latest NIST AI RMF standards. At IS Partners, our expert auditors are here to make the process smooth and straightforward for you.

But there’s more—we don’t just help with certification. Our team provides a personalized set of recommendations to boost your security. From a thorough risk assessment to ongoing support, we’re here to help you navigate and strengthen your defenses.

Real-World Applications of AI in Risk Management

Gartner forecasts that 34% of organizations will start using generative AI within the next year. Yet, if you dig into the topic, you might find that many discussions about AI are light on real-world examples of AI in cybersecurity. 

To bridge this gap, we’ll examine six concrete cases of organizations that have effectively harnessed AI to enhance their cybersecurity practices. These use cases will show how powerful AI can be in action and help with your AI compliance.

Fraud Detection

Fraud detection is an essential part of any loss prevention strategy. It aims to stop individuals from illegally obtaining money or property through schemes like identity theft, false insurance claims, or embezzlement. 

The process starts by analyzing suspicious behaviors. It monitors transaction data, and fraud detection systems can spot inconsistencies that might suggest something’s off. 

Risk Assessment

AI has become an invaluable tool in risk assessment thanks to its ability to quickly identify, analyze, and respond to potential threats. 

One of the standout features of AI-powered tools, like user and event behavior analytics, is their ability to detect and respond to anomalies that might signal a security breach. AI-powered tools can reduce the false positives that often come with traditional vulnerability detection methods.

Also, AI makes risk scoring much more accurate. For example, older systems that might pose a risk but are often overlooked in traditional assessments are thoroughly evaluated with AI. Unlike standard risk rating systems, AI can assess security vulnerabilities and countermeasures on their own merits, offering a clearer picture of the risks.

Threat Intelligence Analysis

AI analyzes threat intelligence analysis works by examining threat data to assess the severity of an incident. It prioritizes the most critical threats and sometimes can even suggest or automatically take actions, such as isolating an infected system. 

This process speeds up response times and relieves security teams of much pressure.

Threat intelligence data uncovers critical insights, revealing where attacks originate and exposing indicators of compromise. It also sheds light on emerging trends, particularly in how cloud accounts and services become prime targets.

AI tools can pull in and analyze this data at scale, using machine learning to calculate the likelihood of future threats and build risk prediction models. 

Unstructured Data for Predictive Risk Analysis

AI can analyze unstructured data to find patterns from past incidents and turn them into risk predictors. It helps create scenarios that project potential risks, giving businesses a clearer picture of future threats. 

For example, AI allows auditors to analyze entire datasets in auditing, flagging even small transactions that might have been missed before. This makes audits more thorough and helps catch anomalies early on.

Data Classification and Monitoring

AI can quickly scan and classify data in a cloud environment based on set rules and patterns, automatically tagging sensitive or critical information. This makes managing and securing data easier because everything is categorized and organized. 

Risk monitoring ties closely into this process. AI doesn’t just stop at classifying data—it also keeps an eye on potential risks. It continuously checks for threats and reviews risk contingency plans to ensure they stay relevant and effective.

Notable AI Risk Management Frameworks and Laws

Many companies are now embracing AI risk management to stay ahead of upcoming legislation and regulations. As we consider the complexities of artificial intelligence, Gray Scott’s thought-provoking question looms large: 

“The real question is, when will we draft an artificial intelligence bill of rights? What will that consist of? And who will get to decide that?”

However, the regulatory oversight for AI is finally here. Several notable examples highlight how different governments and organizations are taking action, and they are:

NIST AI Risk Management Framework

Recognizing the growing importance of AI risk management, the U.S. Congress tasked the NIST with developing a framework to guide organizations. In January 2023, NIST released its first AI Risk Management Framework version. This framework focuses on several key areas:

• Advancing trustworthy AI through research.
• Applying AI innovations across NIST’s programs.
• Establishing benchmarks, data, and metrics to evaluate AI technologies.
• Leading the development of technical AI standards.
• Providing technical expertise to shape AI policies.

Voluntary Commitments From Big Tech

The Biden-Harris Administration secured voluntary commitments from 15 major AI companies to adopt specific AI risk management measures in the United States. 

This agreement aims to boost public trust in AI services and set a standard for enterprises to follow when evaluating their AI lifecycle, including those from third-party vendors.

EU AI Act

In Europe, the EU AI Act is breaking new ground as a legislative effort to regulate AI based on risk. It takes a prescriptive approach, especially for high-risk AI providers, covering areas like risk management, data governance, and documentation. The act’s main goals include:

• Addressing risks specific to AI applications.
• Prohibiting AI practices that pose unacceptable risks.
• Defining high-risk AI applications and setting clear requirements for their use.
• Establishing obligations for deployers and providers of high-risk AI systems.
• Requiring conformity assessments before trustworthy AI systems are marketed.
• Implementing enforcement measures and establishing governance structures at both European Union and national levels in line with human rights.

HITRUST AI Risk Management Program

The HITRUST AI Risk Management Assessment takes a deeper dive into managing AI risks, going beyond surface-level regulatory compliance. 

What’s fascinating is its all-encompassing approach which offers a 360-degree framework that addresses security. 

Even more compelling is its alignment with the NIST and ISO/IEC standards—two industry pillars known for setting the bar in AI governance. This makes the HITRUST program a strategic asset for companies looking to strengthen their AI risk management.

AI Risk Assessment Template

The Responsible AI Risk Assessment Template provides a clear-cut approach for evaluating the potential impacts and use of AI systems on individuals, organizations, and society.

Below is an example of an AI Risk Assessment Template:

Steps to Implement AI in Risk Management

Implementing AI in risk management can significantly enhance your organization’s ability to identify, assess, and mitigate new risks more precisely. 

Here’s a step-by-step guide to help you integrate AI into your risk management processes.

Assemble a Cross-Functional Team

AI projects benefit from a variety of expertise. Gather a team including IT specialists, legal advisors, compliance officers, and business leaders. 

This diverse group will contribute different perspectives and ensure that the AI risk management framework addresses technical, legal, and business aspects.

Define the Context and Objective

Clearly outline the environment in which the AI system will operate. Consider the following aspects:

• Function. What specific tasks will the AI perform?
• Purpose. Why is AI being implemented, and how does it align with organizational goals?
• Context. Understand the AI system’s operation’s social, legal, and financial backdrop.
• Impact. Identify who will be affected by the AI system, such as regulators, customers, or employees.

Establish Clear Policies and Procedures

Develop detailed policies and procedures to guide the AI risk management framework. Note that the policies can be based on the existing framework’s standards and guidelines as well. 

For example, if you already have a Business Continuity and Disaster Recovery policy, you need to integrate AI systems into your existing risk assessment processes. 

A practical adjustment would be to mandate an annual Business Impact Analysis (BIA) specifically focused on AI systems.

Here’s how you can do this: 

• Roles and Responsibilities: Define who is responsible for different aspects of AI risk management.
• Development Lifecycle: Outline the stages of AI development and deployment.
• Risk Assessment Methods: Specify how risks will be identified and evaluated.
• Incident Response: Create protocols for addressing issues or incidents involving the AI system.

Conduct Regular Audits and Assessments

AI technology and its applications evolve rapidly. To keep up with these changes, schedule frequent audits and assessments. Contact industry leaders such as IS Partners to conduct regular audits of your AI systems. 

Identify and Monitor AI Risks Regularly

After identifying risks, implement a robust monitoring system. Regularly audit data inputs, results, and algorithms to detect and address emerging risks. Compare the AI system’s outputs with sensitive data to identify potential biases or discriminatory patterns.

Invest in Training and Awareness

Help your team become active contributors to responsible AI by investing in their training. Offer programs that cover AI risks, best practices, and ethical issues. 

When knowledgeable, your employees can identify problems early and support a culture of responsible AI use throughout your organization.

Establish AI Risk Management Framework With IS Partners 

Managing AI risks and ensuring compliance is essential in today’s regulatory environment. Frameworks like NIST AI RMF set global standards that businesses must follow to avoid penalties and maintain trust. With 89% of U.S. Foreign Corrupt Practices Act enforcement actions involving third-party intermediaries, proactive AI governance is critical.

As AI technologies transform operations, companies must align with compliance requirements to mitigate risks effectively.

IS Partners play a vital role by providing secure infrastructure, monitoring AI activities, and facilitating compliance through real-time data oversight. Our support ensures businesses can confidently manage AI risks within cloud-based environments.

Stay ahead of evolving threats with expert guidance and cutting-edge solutions. Contact IS Partners today to establish a robust AI risk management framework and safeguard your business for the future.

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About The Author

Ian Terry

Ian Terry is a highly respected thought leader and subject matter expert in the field of cybersecurity.

As the Director of Cybersecurity Services for IS Partners, Ian leverages his extensive knowledge and experience to provide valuable insights and guidance to clients across various industries.

Ian has performed numerous risk assessments and audits related to NIST, HIPAA, HITRUST, FISMA, PCI, and CMSR. He is also an expert in third-party risk management having built a SaaS security platform for streamlining third-party risk assessments. Ian's cybersecurity writings have been published in Hackernoon, Security Boulevard and CISO Mag.

He holds a B.S. in Cybersecurity from a national center of academic excellence in cybersecurity as recognized by the NSA and DHS and has CSM, HCISPP, and SSCP certifications.