Webinars
Third-party risk is entering a new operating model
AI agents are showing up everywhere - in your own workflows and inside your vendors’. That means vendor dependencies change faster, evidence goes stale sooner, and “point-in-time” assessments become harder to defend.
In this live conversation, risk practitioners from Cursor, Uber, and Verily share how they’re adapting: what they’re automating, what they’re keeping human-led, and what “defensible oversight” looks like when systems move at agent speed.
You’ll learn how to:
Scale TPRM without scaling headcount
Move from periodic reviews to continuous, evidence-based oversight
Keep auditability and human control as workflows become agent-driven
Key takeaways from the discussion
How do AI agents change third-party risk management?
AI agents can help teams review vendor documents and build risk profiles from multiple sources. In the discussion, Anirban from Uber described the goal as faster onboarding and a risk view that updates over time, rather than relying only on a point-in-time assessment.
What should continuous vendor monitoring look for?
Kenneth from Cursor highlighted changes in vendor features, API documentation, SOC 2 reports and shared responsibilities. Comparing new evidence with earlier evidence can help teams identify where a vendor needs further review after onboarding.
Where should humans stay involved?
Humans set the objectives, risk tolerances and escalation triggers. Anirban described allowing agents to operate within approved boundaries and escalating when those boundaries are exceeded. The panel also emphasized that people remain accountable for risk decisions and their defensibility.
Does an approved AI tool inventory prevent shadow AI?
An approved inventory is a starting point. Anirban described tracking third-party AI tools, internally built agents and externally built agents, alongside monitoring actual use and data flows. An inventory alone does not capture every unapproved tool employees may use.
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