Implementing third-party risk management (TPRM) is crucial for organizations that rely heavily on external partners or vendors to run its essential services and operations. The traditional approach of conducting manual, slow, static, and tedious assessments may help achieve compliance, but does not prioritize prevention in modern times. In a recent study, it was discovered that 59% of data breaches involve external vendors . Hence, it is crucial to incorporate continuous monitoring of vendors as a key element of TPRM to gain a better understanding of the associated risk.
The importance of TPRM
TPRM aims to detect, evaluate, and manage risks associated with using services or goods from third parties. It is essential to choose vendors and partners who will adequately mitigate the risks and protect your organization’s interests. TPRM has developed into a critical component of an organization's risk management strategy due to the growing number of vendors/suppliers and the complexity of their services.
Leveraging AI for continuous monitoring
Implementing ongoing continuous monitoring in TPRM, with the help of external datasets, can be highly beneficial. When supported by Natural Language Processing (NLP) and Optical Character Recognition (OCR) capabilities powered by Artificial Intelligence (AI) and Machine Learning (ML), it can strengthen and fast-track the TPRM assessment process. This allows vendors to respond quickly, and organizations can detect risks as soon as they occur and take appropriate action. Furthermore, it facilitates efficient engagement with vendors to partner in addressing identified risks and achieve favorable risk remediation outcomes.
The growth of AI and ML in TPRM
AI and ML technologies are increasingly being adopted in every field, and cybersecurity is no exception. The application of AI and ML technologies to third-party risk management is growing. According to a Gartner report, “By 2025, AI and ML will be used to reduce the likelihood of significant third-party incidents by 60% and reduce time to detection and response by 50%." As AI and ML technologies continue to evolve and improve, they can be leveraged to enhance the TPRM process, allowing organizations to quickly identify and mitigate risks associated with third-party relationships. This will lead to more efficient risk management and better protection of sensitive data and intellectual property.
Streamlining assessments with AI and ML
Assessing third-party risk can be a complex and time-consuming process, requiring a thorough understanding of the vendor's business practices, financial stability, legal compliance, and more. However, by leveraging AI and ML-driven NLP OCR capabilities, organizations can simplify the assessment process and make more informed decisions. Using automation and advanced technologies such as Wipro's TPRM, organizations can drive faster auto response to assessments and make more accurate decisions that align with the speed of business.
The future of TPRM
To keep up with the evolving threat landscape, organizations must take a proactive approach to third-party risk management. This means leveraging advanced technologies such as AI and automation to quickly identify and respond to potential risks.
Wipro's third-party risk management (TPRM) has evolved and matured over time by leveraging Natural Language Processing (NLP) and Optical Character Recognition (OCR), AI and ML technologies. By extracting valuable information from structured and unstructured data sources, such as compliance audit reports, assessment reports, and vendor contracts, organizations can automate assessing vendor risk and identify potential risks more quickly. These technologies can also provide insights into vendor performance and help organizations make better-informed decisions. This highly engaging and automated process highlights the areas that require attention, making it faster and more efficient.
The time has come to transform third-party risk management by leveraging the power of AI and ML for automated and efficient assessment cycles. TPRM is a critical aspect of an organization's risk management strategy, and continuous monitoring and external datasets are essential for detecting and responding to real-time risks. With AI and ML-driven NLP OCR capabilities, organizations can simplify and automate some extractable questions in the assessment process and gain valuable insights into vendor risk. By leveraging these technologies, organizations can make more informed decisions and reduce the time it takes to identify and respond to risks and threats.