How to Leverage AI for Enhanced Third Party Risk Management

Conducting Comprehensive Due Diligence with AI

The first step in managing third party risk is conducting a thorough third party due diligence on potential vendors and partners. Gathering information on a third party’s financial health, security posture, regulatory compliance, reputation and more allows organizations to make informed decisions when entering relationships. This process has traditionally been manual and labor-intensive. AI can automate the information-gathering process through:

• Natural language processing to extract insights from contracts, audits, policies, and other documents provided by the third party. This helps surface any red flags or areas of concern.
• Web scraping and analysis of public information sources to quickly compile details on leadership, litigation, regulatory actions, and cyber incidents associated with the third party.
• Automated questionnaires and assessments to gather security, privacy, and compliance data from third parties in a standardized format. AI can identify areas that require further follow-up by human analysts.

Ongoing Monitoring Powered by AI

The risks associated with third parties evolve rapidly, so periodic due diligence is no longer sufficient. AI enables continuous monitoring of third party and fourth party risks through:

• Real-time analysis of news, social media, and dark web sources to identify emerging threats, litigation, outages, or other incidents involving third parties. Alerts can be triggered to re-evaluate the relationship when risks emerge.
• Automated review of audit reports, security assessments, and policy documents provided by third parties to analyze changes in status or new areas of concern.
• Analytics to detect abnormal activity patterns that may indicate a third party system compromise, such as unexpected spikes in data transfers or logins from suspicious IP addresses.

AI-Driven Risk Ratings and Predictions

AI can synthesize information from all sources into dynamic risk profiles for each third party to support ongoing relationship management. Risk models powered by machine learning can:

• Continuously calculate and update risk scores for every partner based on their unique profile, relationship type, and risk factors.
• Identify the strongest predictors of third-party risk materializing based on past performance data and near-miss indicators.
• Predict which vendors may have the greatest cybersecurity, compliance or operational risk going forward based on analysis of current internal controls and external threat levels.
• Project where and how strongly third party risks could manifest, enabling proactive mitigation steps before damages occur.

Orchestrating Intelligent Risk Assessments

While AI excels at gathering and analyzing data at scale, human expertise is still essential for judgment-oriented tasks like interpreting the impact of assessment results. AI can optimize how human analysts conduct assessments by:

• Automated identification of the most salient potential risks to prioritize for human review based on hazard likelihood and potential impact estimates.
• Natural language generation to provide analysts with draft risk assessment narratives to edit and build upon, saving time spent writing reports.
• Risk forecasting models that estimate future areas of concern to guide assessment efforts toward potential vulnerabilities before they materialize.
• AI is unmatched in its ability to process and derive insights from large, complex sets of risk data. But human oversight ensures assessments don’t overlook nuances and don’t over or under emphasize risks based on faulty algorithms.

Driving Mitigation via AI Assisted Audits

• The culmination of third party risk management is driving concrete steps to mitigate unacceptable risks through corrective actions and controls. AI lends efficiency and consistency to governance processes like audits by:
• Automating audit scheduling and planning, freeing up staff to focus on strategic initiatives versus administrative tasks.
• Generating preliminary audit reports for review by pulling relevant information from continuous monitoring activities and past assessments.
• Ensuring consistent audit scope and rigor across all vendors and partners through standardized protocols and questionnaires.
• Providing predictive analytics on which corrective actions would most effectively reduce a given partner’s risk exposure based on comparative data.