Would you use AI to support your third-party risk decisions?
We’ve recently witnessed AI morphing from novelty tech to a ubiquitous force for decision-makers across many industries, including third-party risk management (TPRM). And the numbers agree: According to a survey by PwC, by 2025, the AI-driven hardware market will reach $234.6 billion. Fast-forward another five years, and the number climbs to $15.7 trillion.
Despite the growth of AI as a business intelligence tool, many are still hesitant to use it when making third-party risk decisions. Can AI make these critical choices easier or faster? It’s a fair question, especially considering the complexity of the third-party risk lifecycle.
In a recent webinar, we polled our audience about their use of AI. The adoption rate has been slow, with only 7% indicating they currently use AI, while 62% say they plan to incorporate AI in the near future.
Whether you’re currently using AI, plan to, or are on the fence, in this article, we look at AI considerations, how AI can be used in TPRM, and how AI can play a pivotal role in assessing your third parties.
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Understanding the Third-Party Risk Lifecycle
Understanding the third-party risk lifecycle is the first step, as it provides the structural foundation for any AI-driven solution. The lifecycle consists of four critical stages:
- Identify and classify. This stage involves calculating your risk exposure based on the type of service your intended supplier provides and the criticality of the digital assets you plan to entrust to their custody. For example, your CRM software solution provider is likely processing or controlling customer master data, making this third party a high internet risk.
- Scope and assess. Determining the scope of the risk involves understanding which systems it may affect and how prevalent the risk is. In the scope and assess stage, you want to understand the potential reach and severity of the risk.
- Analyze and report. During this phase, an in-depth examination is performed to ascertain the importance of each risk factor to your organization. Sharing the results with pertinent stakeholders is crucial, ensuring they are well-informed and prepared to take the required measures.
- Mitigate and monitor. The fourth and final phase of the risk lifecycle focuses on reducing or eliminating the risk, followed by monitoring how the threats that create the risk evolve, increase, or decrease over time.
The Challenges in Third-Party Risk Management
Each TPRM phase introduces unique challenges, which also become the target for any AI-powered solution. The challenges by stage include:
Identify & Classify
You’ve heard the phrase, “Garbage in, garbage out,” and building an effective AI model is no exception. As you identify risks, you often find dirty data unfit for assessment. More specifically, you may have duplicate records and poor data management, a byproduct of a decentralized procurement process.
Additionally, acquisitions, personnel, and systemic adjustments can create black holes limiting your visibility into the full range of risks. And, of course, classifying vendors is time-consuming and tedious, resulting in excessive investments in people hours.
Scope & Assess
Managing risks in a large or multifaceted organization can present significant challenges, especially when assessing the full extent of the risks that need addressing. One strategy is distributing self-assessments, which can lead to delays and slow-downs, particularly when recipients are not prompt in returning them.
While you could potentially decrease the time required to complete an assessment by offering tailored questionnaires, individual customization is often time-consuming and inefficient. Additionally, you often end up with unstructured evidence, which is difficult to process because it lacks a consistent pattern. Alternatively, if you opt for a standardized Self-Assessment Questionnaire (SAQ), you may find it lacks relevance for many of your third parties.
Analyze & Report
During the analysis and reporting phase, there’s often too much room for misinterpreting findings and reporting risks without providing mitigation steps. In addition, during this phase, the process may get labeled as a “check-the-box” solution, which doesn’t consider the uniqueness of each risk factor as it pertains to the organization and individual vendors.
Mitigate & Monitor
The mitigate and monitor stage is often debilitating for mitigation teams, as it’s not uncommon for risks to be identified but not accompanied by a solution.
Adding to the challenges, you may also end up with a large number of risk alerts. Trying to comb through them to surface the most relevant risks can feel like picking a needle out of a haystack. The bottom line is decision-makers need actionable insights rather than just informative reports about third-party risks.
We polled our webinar attendees on their biggest TPRM challenge. The bottlenecks and time consumption associated with assessments was the top response, an area AI can help solve.
How AI Can Solve Some of the Most Important TPRM Challenges
AI can tackle some of the most substantial challenges in third-party risk management (TPRM), paving the way for a quicker and more efficient system.
Automating Vendor Classification
Artificial intelligence (AI) and language learning models (LLMs) can be employed to gather vendor data. These technologies have the capability to scrutinize intake forms and extract crucial data necessary for making sound decisions. Additionally, AI can categorize vendors and assess the risks they present based on firmographic information, including company size, geographical location, financial status, and customer demographics.
AI-powered LLMs also have the ability to interpret documents and can be used to collect threat intelligence and subsequently determine the risk level posed by vendors.
Rather than personally reviewing and updating vendor scores, AI can automatically refresh this information using the latest threat intelligence, eliminating the need for tedious manual intervention, thus streamlining the entire process.
Get the free downloadable resource: Vendor Risk Management Guide
Breaking Through Bottlenecks
Asking a vendor to complete a comprehensive and detailed questionnaire can be quite demanding in terms of time and effort. However, artificial intelligence can facilitate this process for both parties. For instance, AI and language learning models can be used in conjunction with self-assessments, effectively filling out most of the form on the vendor’s behalf. The vendor only needs to verify the information and make any necessary amendments.
Moreover, AI’s ability to monitor threat intelligence in real-time allows for swift updates to vendor assessments concerning risks stemming from this intelligence. This not only saves time for both parties but also maintains the assessment’s accuracy.
AI can further evaluate a vendor’s readiness against the latest threats. For example, in the event of an application vulnerability, AI can identify which vendors might be potential targets for threats that seek to exploit that weakness.
Processing Unstructured Evidence
Processing unstructured evidence is another strength of AI and LLMs, and it’s particularly useful when dealing with vendor certification data. Instead of combing through thousands of profiles, AI can slice and dice them according to their firmographics. This way, you use AI to pinpoint the most relevant risks automatically.
Further, with this methodology, you can predict how vendors should respond to questionnaires with a high degree of accuracy. Attested assessments are subject to human error and interpretation of whether the behavior is occasional or baked into a vendor’s processes. For example, a vendor may respond positively to an SAQ question about data encryption at rest if they’ve done it before, even though it’s not a regular practice. Predictive Intelligence predicts how a vendor should respond to self-attested questionnaires, so you have a good idea of the risk a vendor poses even before they begin.
Finally, manually reviewing certifications to verify their validity can be arduous and time-consuming. However, AI can handle this automatically, alerting the company if a previously deemed safe vendor now exhibits an elevated risk level.
Once AI supplies risk-related information, it can be used to revise your trust metrics, amend your contract, or initiate a new risk assessment.
The Risks Involved with Using Traditional Generative AI Models
Conventional generative AI models function much like a fisherman casting a wide net: he catches many fish but also hauls in unwanted creatures and random trash. The scenario is quite similar for these traditional AI models. While they may gather vast amounts of data, a substantial portion may be irrelevant for risk assessment. This can lead to misclassification, undermining your TPRM program’s reliability.
Data privacy is another concern with traditional AI models, as they may inadvertently collect information protected by compliance regulations.
Moreover, conventional generative AI models may yield inaccurate information, often due to a lack of a proper system to validate the data sources. As a result, security teams need to manually review all the gathered data, potentially causing further bottlenecks.
Finally, the indiscriminate data collection methods of traditional generative AI models may allow false or outdated certifications to seep into your system, thus compromising the credibility of your TPRM.
The Solution: Responsible AI That Uses Selective Training
By employing machine learning, you can strategically train your AI models to gather the information you need exclusively and are legally permitted to collect.
For instance, you can program the system to solely collect vendor metrics to assess the potential risk they might pose. Your AI-enabled system can also generate a checklist of the cybersecurity measures a company has implemented. It can provide insights into whether they have access control policies, employ encryption for data in transit, or utilize web application firewalls.
AI can also enhance data privacy by anonymizing the collected data and safeguarding sensitive information from unauthorized exposure. For example, the AI can exclude details like the company name and address from the generated profile and any other data you choose not to include.
To round off the process, you can leverage AI to augment your data by appending additional information after the primary collection phase. Data enrichment could encompass publicly available information and other valuable data you wish to add.
AI Adds Confidence and Convenience to Your TPRM
Leveraging AI-driven TPRM data, you can shift your focus from rudimentary tasks like researching firmographics and assembling spreadsheets to work with more significant impact, like analyzing the risk results and outliers and taking the necessary remedial actions.
AI’s advantages are many, including the capability to gather high-quality data, remain compliant with regulations, and append additional data as necessary to create comprehensive vendor profiles. With the seamless integration of an AI-powered system into your existing TPRM framework, you can efficiently utilize information from various sources and eliminate data silos.
CyberGRX is already capitalizing on AI to anticipate risk across thousands of vendors. To discover more about what CyberGRX can offer, schedule a call with our team.
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Welcome to the ProcessUnity Blog
On behalf of the ProcessUnity team, I am excited to welcome you to the new..
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ProcessUnity is a leading provider of cloud-based applications for risk and compliance management. The company’s software as a service (SaaS) platform gives organizations the control to assess, measure, and mitigate risk and to ensure the optimal performance of key business processes. ProcessUnity’s flagship solution, ProcessUnity Vendor Risk Management, protects companies and their brands by reducing risks from third-party vendors and suppliers. ProcessUnity helps customers effectively and efficiently assess and monitor both new and existing vendors – from initial due diligence and onboarding through termination. Headquartered outside of Boston, Massachusetts, ProcessUnity is used by the world’s leading financial service firms and commercial enterprises. For more information, visit www.processunity.com.