Automating Third-Party Risk Assessments with AI

Artificial intelligence (AI) has an impact on third-party risk management (TPRM) processes, regardless of whether your TPRM team utilizes the technology. Service providers use AI to help them fill out surveys, hackers use AI to help them create malware, and AI services provide risk managers with another outsourced service that stores and analyzes potentially sensitive data. While the rise of AI services brings numerous new issues for risk analysts, avoiding this new technology is not the solution. You may tackle these new difficulties by implementing an AI-powered third-party risk management program that allows for faster reviews, broader insights, and more uniform vendor assessments.

AI technology has the potential to transform the risk management environment by speeding up and improving the efficacy of formerly manual and time-consuming tasks.

For example, AI can analyze vendor SOC II reports, PEN test results, and various compliance documents against an organization’s criteria in a fraction of the time. Furthermore, AI’s ability to keep up with regulatory changes allows for 10 to 30 times faster modifications to assessment controls than conventional techniques.

According to an HSB survey, nearly half of all data breaches in 2017 were caused by a third-party vendor or contractor, and IBM and the Ponemon Institute’s annual Cost of a Data Breach report consistently finds that breaches involving third-party suppliers result in higher damage costs.

These concerning trends are prompting businesses to boost their investments in Third-Party Risk Management (TPRM) and Vendor Risk Management (VRM). However, as our reliance on VRM technology grows, so does the need for scalability. In this article, we’ll look at why it’s important to take a complete approach to vendor assessments, as well as how businesses may use AI for third-party risk assessments.

Third-party risk assessment automation refers to the use of modern technology, specifically artificial intelligence (AI), to automate and improve the process of assessing possible dangers associated with an organization’s vendors, suppliers, and partners. These AI-powered systems can analyze vast amounts of data, detect potential hazard factors, and provide actionable insights with minimal human intervention.

Evaluating AI systems and components

Understanding the technological intricacies of AI systems applied by vendors is the foundation for a complete review. Examining the underlying technology reveals potential risks related to AI components in third-party solutions, such as:

• Dataset attributes: AI systems require a great amount of data, so it’s critical to understand the attributes of each dataset. Your assessments should help you understand data quality, training data sources, data ownership, data versioning and traceability, and so on.
• Model’s qualities: Once you have transparency in the datasets you’ll be using, you must achieve equal clarity in the model itself. Is your model a foundational one? What learning strategy does it use? What biases might be present, and what is the demographic parity ratio? What is the model’s level of autonomy, and how much human control is required?

Though you are not building or delivering the system yourself, as a deployer of a third-party AI system, you will still have obligations and responsibilities for the data and models you use; therefore, it is vital to have the answers to these questions well documented.

When implemented securely, AI solutions may greatly enhance an organization’s third-party risk management. However, risk managers that utilize AI for TPRM must follow a method to ensure that it can efficiently manage supply chain risks, identify threats, comply with regulations, and give actionable insights, all at scale. Organizations can utilize AI in third-party risk management to improve visibility, efficacy, and resilience in managing third-party relationships by following these recommendations and best practices. The approach consists of:

Step 1: Specify AI objectives and risk criteria.

Set precise risk standards that complement the goals of the company and its regulatory requirements. Determine the objectives of utilizing AI in third-party risk management, including automated risk assessments, predictive analytics, and real-time monitoring. Clearly state your goals for using AI-powered risk evaluations.

• Make sure AI goals align with industry-specific risk variables and the organization’s operational risk tolerance.
• Establish Key Risk Indicators (KRIs) based on industry standards to improve AI-based risk assessments.
• Engage cross-functional teams (IT, legal, and compliance) to match AI applications with overarching business objectives.

Step 2: Choose data sources

Select AI-powered instruments for risk analysis and data gathering. To guarantee thorough risk assessments, choose trustworthy internal and external data sources, such as financial data, regulatory documents, and news sources. Choose an AI solution that fits the needs of your company and works well with the technologies you already have. To improve the accuracy of AI-driven insights, always use current and correct data.

• Choose AI solutions that can grow with the company’s third-party network.
• Incorporate real-time data streams to enable prompt interventions and maintain current risk profiles.

Step 3: Establish risk levels and configure AI models.

Create risk thresholds that cause alerts and modify AI models to satisfy the specified risk criteria. Set up machine learning algorithms to rank risks according to their seriousness, occurrence, or possible influence on the company. Make sure the data you supply the AI system is of high quality. The quality of the incoming data has a significant impact on how accurate AI-driven insights are.

• AI models should be calibrated or adjusted on a regular basis to enhance their capacity to identify new threats and lower false positives.
• As threats change, use adaptive thresholds based on real-time insights.
• Put feedback systems in place to gradually modify risk sensitivity and increase the AI’s accuracy.

Step 4: Automate monitoring and risk assessment

Use automation powered by AI to continuously monitor third-party threats. Employ AI to do recurring evaluations, send out notifications, and offer insights on matters pertaining to third parties.

• Automate high-frequency processes, such as repetitive assessments, to free up resources for strategic risk management initiatives.
• To quickly inform stakeholders of noteworthy changes in the third-party risk status, set up real-time alerts.
• To establish a cohesive TPRM process, make sure AI-based monitoring technologies work with the existing risk management and compliance systems.

Step 5: Evaluate information

Utilize AI-generated insights to spot patterns, make informed choices, and put risk-reduction plans into action. Regularly review risk and incident reports to identify trends and make necessary adjustments to third-party partnerships or oversight. Make sure your AI-powered risk assessment procedure is consistently in line with your changing business requirements by reviewing and improving it on a regular basis.

• Teams should be trained to understand AI-generated information and use risk analytics to make well-informed decisions.
• To test possible reactions to hazards that have been discovered, use AI-generated risk scenarios.
• To organize a cohesive response to recognized third-party risks, share insights with the appropriate teams.

The potential for generative AI to misreport information is one issue that has been brought up time and time again. Since a generative model processes data rather than storing knowledge like a human respondent does, its probabilistic outputs may contain factual and logical errors. Your analysts are responsible for confirming that the responses you have provided are accurate. The following are the main challenges and practical ways to overcome them:

Data Quality

Assuring the accuracy, comprehensiveness, and accessibility of data is one of the main obstacles to applying AI to third-party risk management. For AI models to produce trustworthy insights, complete and correct data from outside sources is necessary. Outdated or incomplete data might result in missing dangers, false positives, and less-than-ideal decisions. Implement stringent data governance procedures that prioritize the completeness, timeliness, and correctness of data. To keep an eye on and preserve high-quality datasets for AI models, work with reliable data providers and make use of automated data validation technologies.

Regulatory Compliance and Ethical Considerations

AI-powered TPRM systems have to abide by laws pertaining to cybersecurity, data protection, and moral AI application. This can be difficult since privacy regulations, like the General Data Protection Regulation (GDPR), limit the kinds of data that can be collected and shared, which may limit AI’s potential. To make sure AI models satisfy legal requirements, collaborate closely with the legal and compliance departments. Use ethical AI techniques to protect privacy and make sure AI algorithms stay within moral and legal bounds, such as anonymizing sensitive data and carrying out frequent compliance assessments.

Integration with Existing Systems

It can be difficult and resource-intensive to integrate AI-powered TPRM solutions with current risk management, compliance, and operational systems. A lack of unified risk insights might arise from system incompatibility, which can impede the smooth implementation of AI. Only choose AI solutions that offer API-based integration and are compatible with the infrastructure already in place at your company. To improve interoperability with existing systems, think about utilizing scalable AI platforms that enable customization and progressive adoption.

High Resource Requirements and Implementation Costs

It may be necessary to make large investments in infrastructure, software, and expert personnel in order to implement AI in third-party risk management. Businesses may encounter financial and resource limitations, particularly when developing AI capabilities from the ground up.

Begin by implementing AI on a limited scale to target particular high-priority TPRM areas. Pay attention to affordable, cloud-based AI solutions that provide scalable choices so the company may increase AI capabilities as funds and resources increase.

Complexity in Interpreting AI Insights

For effective interpretation, AI-generated insights might be complicated and necessitate specific understanding. Teams that are not experienced with AI analytics may find this difficult, which could result in incorrect interpretations of risk data. To assist risk management teams in comprehending AI data and efficiently interpreting information, offer training courses. Encourage interdepartmental cooperation so that AI experts can collaborate closely with risk management groups and advance precise data interpretation.

Businesses need to take a proactive approach to third-party risk management in order to stay ahead of the evolving threat landscape. This means utilizing cutting-edge technology like automation and artificial intelligence to promptly recognize and address possible threats. By pulling pertinent data from both structured and unstructured data sources, including vendor contracts, compliance audit reports, and assessment reports, organizations can automate vendor risk assessment. Additionally, by offering insights into vendor performance, these technologies can help businesses make better decisions. This automatic and highly entertaining approach is quicker and more effective since it concentrates on the areas that require attention.