Why “SOC 2” Isn’t Enough — How Compliance Teams Should Assess Third-Party AI Vendors
- jomargacoscos
- Dec 9, 2025
- 4 min read
Introduction
Many organizations rely on third-party vendors to supply AI systems — from machine-learning pipelines to generative-AI services. Traditionally, compliance and security teams look for a SOC 2 report as a sign of due diligence. That approach may work for conventional SaaS or data-hosting services. But AI systems introduce a different class of risk — related not just to data storage or infrastructure, but to model behaviour, data provenance, fairness, robustness, and ongoing governance. In that context, SOC 2 alone is no longer sufficient.
Why Traditional Compliance Falls Short for AI
SOC 2 (and similar audits) provide assurances around standard IT-service controls: security, access control, confidentiality, availability, privacy, and processing integrity. These remain valuable when vendors host data or run cloud services. However, SOC 2 was not designed to deal with AI-specific risks — such as how training data was sourced; whether models are fair or biased; how models behave under unexpected or adversarial inputs; how updates or retraining are governed; or whether outputs are explainable or auditable. As a result, a vendor might be SOC 2-compliant — and still deliver a risky, opaque, or untrustworthy AI system.
What to Review Instead When Evaluating AI Vendors
When assessing third-party AI vendors, compliance and risk teams should require clarity around data lineage and privacy; documentation of model development, versioning, and update/retraining processes; evidence of robust testing (including adversarial and edge-case input handling); assessments of fairness and bias; logging or auditability of model outputs; and governance frameworks for monitoring, incident response, and human oversight. In effect, treat the AI system as a living, evolving asset, not a static deliverable.
Using NIST AI RMF (and Similar Frameworks) as a Forward-Looking Benchmark
As third-party AI vendor risk becomes more material, compliance and security teams should begin using AI-risk and governance frameworks as reference points for what “good” vendor assurance could look like. A leading example is NIST AI RMF — a flexible, voluntary framework designed to help organizations assess, govern, and manage risks associated with AI systems, guiding companies to embed trustworthiness, security, fairness, transparency, privacy, and robustness into the AI lifecycle. NIST AI Resource Center
For firms seeking a more formal, certifiable approach, the international standard ISO/IEC 42001 provides an AI-management–system standard, facilitating structured governance, accountability, and continuous improvement of AI operations across the organization. Scrut.io. Beyond these, various ethical-oriented and risk-management frameworks emphasize human-centric principles — fairness, transparency, accountability — complementing technical and governance requirements. Secureframe.com
Because different frameworks serve complementary roles — risk management, formal governance, ethical guardrails, regulatory readiness — many organizations adopt a hybrid approach. For example: apply NIST AI RMF for flexible, context-specific risk assessment; layer ISO 42001 for formal governance and auditability; and reference ethical frameworks to guide vendor-selection policies and long-term AI ethics commitments. This blended approach helps compliance teams demand meaningful vendor assurance today, while staying adaptable as frameworks evolve — making the vendor-assessment process both forward-looking and defensible. RSI Security
What Compliance Teams Should Do Now
Begin by expanding your vendor-due-diligence process to explicitly include AI-related risk questions: ask where training data comes from (data provenance); require documentation of model development, updates, and governance; request evidence of testing (robustness, bias/fairness, security); demand logging, auditability, and incident-response plans; and embed contractual commitments for periodic re-evaluation — especially post-update or retraining.
When sourcing new AI vendors, prefer those who demonstrate alignment or willingness to align with AI-risk frameworks such as NIST AI RMF (or equivalent). Finally, treat vendor-AI risk as a continuous lifecycle issue, with ongoing monitoring and governance instead of a one-time checklist.
Conclusion
AI isn’t just another software tool — it’s a different kind of system: dynamic, complex, and capable of producing unexpected outcomes. While SOC 2 compliance remains useful for baseline infrastructure and data-service assurance, it doesn’t meaningfully address the unique risks posed by AI.
By grounding vendor evaluations in frameworks like NIST AI RMF, embedding AI-specific assessment criteria into procurement and vendor-management processes, and staying alert to evolving best practices and regulations, organizations can better manage AI vendor risk — and reap the benefits of AI more responsibly.
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References
NIST AI Risk Management Framework (AI RMF) — National Institute of Standards and Technology. NIST
NIST AI RMF Core Functions: Govern, Map, Measure, Manage. NIST AI Resource Center
Comparison of AI risk-management frameworks and best practices for combining NIST AI RMF and ISO 42001. RSI Security
Commentary on how NIST AI RMF helps build trustworthy, transparent, and ethical AI systems across sectors. Databrackets.com
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