From headaches to hassle-free: Transforming TPRM with AI
- November 28, 2023
Managing third-party relationships can be a major headache for leaders. Consistently tracking these parties is tedious and time-consuming, along with a hefty price tag. Traditional third-party risk management (TPRM) requires extensive human capital, with employees constantly tracking, updating and uploading details associated with current and new third parties. A single mistake in your TPRM program can lead to damaging headlines for your organization. Moreover, not all third parties receive the necessary level of scrutiny they should, with low- or medium-risk parties often only being reviewed once a year, leaving room for potential issues to arise.
Furthermore, outdated TPRM programs often lack a centralized repository for all the data your organization has interacted with. This results in unstructured data being stored in Excel or locally on desktops, making integration of a new tool a nightmare. Additionally, data feeds from multiple sources may contradict or contain inaccuracies, further complicating the process.
However, new technologies bring forth a modern TPRM framework and tangible steps that leaders can take to protect their reputation, people and business. By leveraging the power of AI, organizations can streamline and enhance their TPRM processes, providing comprehensive oversight and mitigating risks effectively.
The medicine for monitoring third parties: Artificial intelligence (AI)
“As organizations build larger third-party ecosystems, the already difficult job of policy review can become overwhelming. Teams trying to work through an assessment backlog will do what it takes to meet their deadlines: Sometimes, this leads to skimming assessments, sometimes it means skipping some of them altogether. With AI-enabled technology, you can detect the policies that demand the most rigorous human review, enabling analysts to spend less time conducting individual policy reviews and giving them more time to answer requests at scale.” — David Klein, senior director of Product Strategy at ProcessUnity.
Implementing a proven AI-enhanced platform into your TPRM program can significantly reduce risk, save time, effort and most importantly, money. One compelling use case of AI in third-party risk management software is demonstrated by ProcessUnity. Their software, called ProcessUnity Policy Evaluator, uses Natural Language Processing (NLP), a machine learning technology that enables computers to interpret, comprehend and manipulate human language. This technology simplifies the analysis of unstructured data by instantly comparing a third-party's security policy to established security frameworks. As a result, analysts can quickly assess how well a third-party's policies align with industry best practices. This streamlined process saves time and provides valuable insights for effective risk management.
In a TPRM setting, this technology allows a third-party policy to be uploaded into the platform, and the proprietary NLP, combined with an extensive database of information, can compare the policy to security standards to determine its relevancy to the risk needs of your organization. This is achieved through word association training and deep learning-based inferences. The benefits of applying NLP include reducing labor hours, continuous improvement of the NLP and the establishment of a central repository for these policies. However, it's important to note, human oversight is still crucial to making sure subjective aspects are properly addressed.
AI-powered solutions mitigate risks and minimize human error
AI-empowered programs can also help mitigate human error and reduce risks. By leveraging predictive analytics, AI-integrated systems can create models based on historical data and patterns to identify potential risks while tracking third parties. AI can minimize mistakes and allow employees to focus on business growth. Additionally, AI can detect unusual patterns that may indicate fraud, security breaches or other risks. This enables the TPRM program to work across multiple fields simultaneously.
Transforming TPRM with structured and complete data
Conducting TPRM through spreadsheets and desktops often leads to unstructured and incomplete data, making it challenging to accurately track and check certain data fields that aren't stored in a central repository. AI could address these gaps. For instance, if a third-party vendor is difficult to reach and lacks comprehensive documentation, AI in your TPRM platform can use predictive analytics to build a risk profile based on similar firms of comparable size and industry. Simultaneously, AI aggregates the data into a centralized database. With the tools AI leverages, it can consolidate hard-to-record fields and provide tangible information for your third-party risk management program.
Risks associated with AI: What you should be worried about
It’s important to acknowledge the risks of AI, including data breaches and leaks of confidential and sensitive information. Without proper training and monitoring, individuals may inadvertently upload data into an AI bot, or an improperly programmed AI program may access restricted internal resources. When combined with your TPRM program, these AI risks can result in reputational damage and regulatory liabilities.
Solving the risks associated with AI: Why you don’t need to worry
These are actionable steps you can take to address the risks associated with implementing AI into your TPRM program.
1. Establish priorities within your organization regarding the protection of sensitive information. Employees can be better trained to handle and safeguard this data, and there can be stricter controls on the flow of information to and from sensitive sources.
2. Protect sensitive data: Classify, anonymize and encrypt any data that is considered sensitive or delicate. This ensures that even if there is a breach or unauthorized access, the information remains protected and can't be easily exploited.
3. Know the best practices: Staying up to date with modern cybersecurity frameworks, such as the NIST Cybersecurity Framework, is also essential. These frameworks provide guidelines and best practices for maintaining the security of your data and systems. Regular audits and assessments should be conducted to identify and address any vulnerabilities or biases in the AI algorithms. By following these standards, you can make sure that your TPRM program is aligned with industry-recognized security measures.
4. Uphold data hygiene: Lastly, maintaining proper data hygiene is crucial in mitigating risks associated with using AI. This involves regularly reviewing and updating your data, removing any unnecessary or outdated information, and making sure that data is stored and accessed securely. By practicing good data hygiene, you can minimize the potential for errors, breaches or misuse of data within your TPRM program. “Dirty Data,” or incorrect data, can adversely affect a vendor/clients’ performance and reputation.
By taking these proactive steps, you can effectively address the risks associated with implementing AI into your TPRM program and protect your company's reputation and sensitive information from potential harm.
