AI in TPRM: The Challenges
As mentioned above, now that AI has become ubiquitous in the workplace, risk analysts should be ready to receive AI-assisted questionnaire responses, which could be acceptable if the technology is used responsibly, and the output is attested by the third party. After all, one concern that has been raised repeatedly regarding generative AI is its capacity to misreport information. Unlike a human respondent, a generative model does not store knowledge so muchas it processes data, meaning its probabilistic outputs can contain logical and factual errors. It’s up to your analysts to determine if the answers submitted are indeed true.
Another concern is the potential for hackers and other bad actors to exploit AI to run more ambitious operations on a leaner budget. Using generative AI to assist in software development, hackers, once limited to smaller-scale attacks, can launch more significant, more damaging attacks on larger vendors. The potential to “democratize” hacking by opening smaller, less skilled teams to more significant operations will catalyze the rapid acceleration of cybersecurity breaches and exacerbate the importance of adequate cybersecurity controls, both internally and in the vendor ecosystem.
Still, the solution isn’t to retreat from AI but to embrace it. Suppose hackers will have an easier time launching attacks and questionnaire respondents will send more significant volumes of low-quality data. In that case, it is incumbent on TPRM professionals to take any advantage they can get and stay on top of the developing risk ecosystem. Today, that means organizations need to incorporate AI into their TPRM programs as a means to elevate human performance.
AI in Inherent Risk Scoring
Inherent risk scoring is a process of estimation and synthesis: analysts review potential third-party partners according to their firmographic information, data access and digital footprint to geta general idea of how much risk a provider would bring to the organization before controls are brought into the equation. This data is then used to prioritize vendor assessments based on how strong the potential for adverse impact is in a given relationship: vendors that could potentially disrupt operations or breach sensitive data must be assessed more urgently and at a greater level of scrutiny, while vendors with less potential to introduce risk can be evaluated on a less stringent basis.
Luckily, probabilistic reasoning and data sorting are two areas where AI performs strongly. By feeding basic firmographic and access data into an AI-powered TPRM platform, you can rapidly assess the inherent risk associated with each vendor and produce an accurate heat map of which vendors are more likely to introduce risk to the organization and who should be assessed at what level of scrutiny.
AI in the Nth-Party Ecosystem
Another challenge TPRM teams face is the impossibility of assessing the complete vendor ecosystem. It’s hard enough for an organization to distribute and gather questionnaires for most of its third parties, and even that degree of thoroughness doesn’t consider the fourth-party ecosystem, or the broader network of vendors used by an organization’s third parties. Often, this means that critical operations are completed by providers into whom the organization lacks visibility—and that can lead to risk managers being blind-sided by a breach or risk event.
Still, even without distributing questionnaires to the farther reaches of the vendor ecosystem, an organization can use AI to make intelligent predictions about where risk is likely to be concentrated in their third and fourth parties. When vendor risk assessments are stored in a centralized repository, like a risk exchange, AI can leverage the vast stores of vendor data to accurately predict which vendors are likely to exhibit which forms of risk. While this level of insight will never replace vendor assessments for the most critical third parties, it does allow teams to illuminate the significant portions of relevant vendors that would otherwise be completely opaque.
AI in Policy Review
Policy analysts must operate nimbly in a business environment defined by expansive vendor ecosystems. Often, this leads to painful but necessary shortcuts, like skimming questionnaires or skipping them altogether. Significantly, as the threat of cybersecurity breaches mounts, skimming questionnaires or skipping them altogether. Especially as the threat of cybersecurity breaches mounts further, this is an untenable situation—teams need to be able to review more third-party policies faster, and they cannot depend on ballooning resources to make it happen.
Here is one area in which AI is beneficial: by intelligently sorting through vendor questionnaires and calling out areas that demand human attention, an AI-powered TPRM platform can drastically reduce the time risk analysts need to spend on a given assessment while improving consistency throughout. By utilizing AI to power their TPRM processes, analysts can get ahead of their overwhelming assessment backlogs and ensure their organizations are safe.
Conclusion
The emergence of artificial Intelligence will fundamentally change the landscape of Third-Party Risk Management. AI introduces many powerful tools that can significantly enhance the effectiveness and efficiency of TPRM teams. AI-driven analytics tools can sift through vast amounts of data, identifying patterns and correlations that would be impossible for humans to detect manually. This analysis can help identify potential risks in real time, allowing organizations to mitigate them before they materialize into significant threats. Change the landscape of Third-Party Risk Management. AI introduces a host of powerful tools that can significantly enhance the effectiveness and efficiency of TPRM teams. AI-driven analytics tools can sift through vast amounts of data, identifying patterns and correlations that would be impossible for humans to detect manually. This analysis can help identify potential risks in real time, allowing organizations to mitigate them before they materialize into significant threats.
However, it’s crucial to remember that AI is a tool, not a solution. Successful implementation of AI in TPRM requires careful planning, management and oversight to ensure it’s used appropriately and effectively. With the right approach, AI can be a powerful ally in navigating the complex world of third-party risk management. For instance, one study showed that using AI technology made it easier and faster for new employees to achieve the same proficiency as more seasoned employees, increasing productivity and employee retention.
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