Businesses across the United States are rushing to adopt artificial intelligence tools, but many are overlooking a critical piece of the puzzle: third‑party risk management. According to Vanta’s State of Trust Report, 65% of organizations surveyed in July 2025 said their use of agentic AI outpaces their understanding of it, creating gaps in security and compliance.
Why Traditional TPRM Falls Short
Traditional third‑party risk management (TPRM) relies on point‑in‑time artifacts such as questionnaires, periodic reviews, and risk registers. Those methods assume a vendor’s risk profile remains stable between assessments. In reality, AI vendors can change foundation models, add new subprocessors, or modify data‑handling terms at any time. As Vanta’s governance risk compliance expert Evan Rowse explains, “A security questionnaire gives you a snapshot of something that doesn’t hold still. By the time your AI vendor has finished responding, their model has changed, a new subprocessor has been added, and last quarter’s data handling terms are already out of date.”
Four Steps to Strengthen AI Vendor Oversight
1. Implement continuous monitoring. Use modern GRC platforms to track changes in AI models, infrastructure, data residency, and certifications such as SOC 2 Type II, ISO 27001, ISO 42001, and HIPAA. Ongoing visibility helps organizations spot material changes as they happen.
2. Leverage automation. AI‑driven tools can filter the flood of signals from continuous monitoring, surfacing only those that require human review. Automated analysis of vendor questionnaires and evidence packages saves time while maintaining rigor.
3. Integrate vendor risk into broader GRC. When TPRM operates in isolation, departments end up with fragmented views of AI risk, leading to inconsistent policies and weak accountability. A unified GRC approach creates a single risk register that feeds all control programs.
4. Expand visibility beyond direct vendors. Many AI services rely on downstream providers—model hosts, cloud platforms, and embedded tools. Mapping these fourth‑ and Nth‑party dependencies uncovers hidden exposure and ensures contracts include notification requirements for material changes.
Contract Language Matters
Rowse stresses that “material AI change” should be defined in vendor agreements with clear notification clauses. This moves the expectation from an informal practice to a contractual obligation, giving organizations the right to request a new assessment whenever a vendor swaps a foundation model or adds a subprocessor.
What This Means for Companies
Organizations that adopt these practices are not abandoning existing TPRM processes; they are simply acknowledging that a static questionnaire cannot govern a vendor that evolves weekly. By treating continuous monitoring and AI vendor discovery as core requirements—not optional features—companies can protect sensitive data, maintain regulatory compliance, and reduce the risk of unexpected breaches.
About the Source
This guidance was produced by Vanta, a platform that helps businesses manage third‑party risk, and was distributed by Stacker. The original article appeared on KVIA on September 15, 2026.
Original reporting: El Paso News (HLL/CB) — read the source article.
