Do you ever feel you or your team is overwhelmed with vendor assessments? That it is becoming overly resource-intensive for risk, security and privacy teams, or even have your stakeholders complaining that this is the most time-consuming compliance process?
Third-party risk management (TPRM) has become demanding and as organizations increasingly work with offshore vendors and cloud providers, and the volume increases faster than the team can perform assessments, manual reviews and emails just can’t keep up.
The good news is that automation and artificial intelligence/machine learning are opening the door to a smarter, faster and more scalable future for TPRM.
Why Third-Party Risk Is Everyone's Concern
All staff at all levels, including senior leadership as well as security and privacy analysts, share personal responsibility when it comes to third party risk management. A weak link in your vendor onboarding can result in data breaches, regulatory fines or reputational damage. Global regulatory pressure is mounting with mandates like the General Data Protection Regulation (GDPR) in the EU and Federal Trade Commission (FTC) guidelines in the US, requiring persistent monitoring of third-party actions.
Where Does Automation and AI/ML Fit In?
Automation helps streamline repetitive tasks—think vendor onboarding and offboarding, risk rating, sending out questionnaires, performing triage and assessments for similar scope and follow-ups – internally with the business and externally with vendor points of contact who manage escalations.
AI/ML go a step further by identifying patterns and highlighting insights from large internal data sets. This could include Natural Language Processing (NLP) to review contracts or assessments for risk indicators, such as incorrect attestation of no data sharing by the business, in which the actual engagement type can include data shares with the vendor or training a model to score vendors based on historical incident data.
These technologies can also:
- Flag high-risk responses in questionnaires automatically
- Monitor vendors in real time through external threat data feeds
- Prioritize remediation actions based on impact and likelihood
There are many tools in the market like OneTrust, Prevalent and ServiceNow VRM offering intelligence at scale. But the key isn't just buying a tool; it's knowing how to use it strategically.
Getting Started with Integrations
Here are a few quick tips for integrating automation and AI/ML into your TPRM workflow:
- Identify low-hanging fruit. Automate processes such as sending reminders or use pre-built risk assessment logic.
- Leverage your data. If you have a historical dataset, you can train basic models to predict future risks, flag anomalies or even close out assessments faster based on recent assessments already performed with that same vendor.
- Integrate across other compliance teams. TPRM doesn't have to be a siloed process. Integrate other risk reviews within the third-party assessment process or connect them with GRC procurement and legal tools to create a seamless process for the user.
- Analytics. Start with capturing analytics around measuring and monitoring risky vendors and assessment cycle times.
What to Look Out For
As they say, with power comes responsibility. Similarly, AI can introduce bias, produce false positives or fail to account for context. Consider:
- Understanding how the model works before using it to make critical decisions
- Ensure there is human oversight in key decision points
- Document your processes for audit and governance reviews
The Road Ahead
Automation and AI/ML are not silver bullets but they are powerful allies in scaling your TPRM program, driving process efficiencies and reducing manual overhead.
If your third-party ecosystem is growing, your risk assessment process should become scalable and smarter.
Author’s note: The views and opinions expressed in this article are entirely those of the author and do not reflect the views, policies, or positions of the author’s employer or any other organization with which the author is affiliated.
