AI adoption has put third-party risk management under a microscope. Security and compliance teams are being asked to review tools that are constantly iterating and becoming more capable. As a result, AI is often treated as a special case, something that existing third-party risk best practices were never designed to handle.
What AI has done is surface long-standing gaps in how organizations scope vendors, understand data flow, and account for layered dependencies. The pressure feels new because the tools are novel, but he underlying risk mechanics are not.
This post lays the groundwork for a more grounded approach to AI and third-party risk management, one that builds on what already works.
Why AI has put pressure on third-party risk management
AI tools spread faster than most enterprise software. Business teams can access them with little friction, and the perceived upside is immediate. That speed collides with third-party risk management processes that were designed for deliberate procurement cycles and clearly defined vendors.
Several factors amplify the tension:
- Employees can paste large volumes of sensitive information into AI tools without realizing the implications
- AI products often rely on multiple upstream services, which complicates vendor scoping
- Product tiers and usage patterns materially change risk, but are hard to capture in intake forms
- Leadership wants enablement, while risk teams are expected to provide certainty
These pressures make it feel as though AI has outgrown traditional third-party risk models. In practice, they reveal where those models relied on assumptions that were already fragile.
What third-party risk management is fundamentally built to assess
At its core, third-party risk management exists to answer a small set of questions:
- What data does a third party access or process?
- How that data is protected?
- Where responsibility sits if something goes wrong?
- How dependent the organization is on the service?
These questions apply whether the vendor is a payroll processor, a cloud CRM, or an AI writing assistant. AI changes how frequently these questions need to be revisited and how precisely they need to be scoped, but it does not replace them.
Most third-party risk management programs already account for indirect dependencies, inherited controls, and shared responsibility. Modern SaaS tools have long relied on services outside the primary vendor relationship, including cloud infrastructure, managed services, and embedded platforms. The AICPA’s guidance on SOC reporting formalizes this model by treating these dependencies as subservice organizations, with responsibility addressed through inherited controls and documented carve-outs rather than separate vendor onboarding.
TPRM programs also distinguish between low-risk productivity tools and systems with deep operational access. That distinction remains relevant when evaluating AI tools, where usage and access patterns are more relevant than the underlying technology.
How AI exposes existing weak points in third-party risk management
AI adoption highlights three areas where third-party risk management programs often struggle.
First, vendor scoping tends to rely on simplified definitions. Intake processes assume a single accountable provider, even though modern SaaS tools rarely operate in isolation. AI makes that simplification harder to defend.
Second, data flow is often inferred rather than mapped. Traditional vendor assessments confirm policies, certifications, and stated architectures, but they rarely capture how a tool is actually used inside the organization.
An AI vendor may encrypt data in transit, limit retention, and commit not to train models on customer inputs under an enterprise contract. Those controls matter, but they do not define the full risk picture. Risk can still vary widely based on employee behavior. A team using an AI tool to draft generic marketing copy introduces minimal exposure. A support team pasting live customer tickets into the same tool introduces a very different data profile, even though the vendor and contractual controls are unchanged.
Third, internal governance is often treated as separate from vendor risk. When employees misuse AI tools, the failure is organizational, not contractual, and third-party risk management programs have to be aware of the difference when designing their processes
The risk of treating AI as an exception
Creating separate AI-specific risk categories or review tracks can feel like progress, but it often introduces new problems. Special treatment slows adoption without improving outcomes, it creates inconsistent standards across tools that process similar data, and it signals that third-party risk management is reactive rather than grounded in structured practice.
Global frameworks for AI governance emphasize consistency and a risk-based, systematic approach rather than special case treatment. The EU Artificial Intelligence Act sets out a risk-based framework that classifies AI systems by potential harm and requires ongoing risk management systems for higher-risk applications, including continuous identification, analysis, evaluation and mitigation of risks throughout the AI lifecycle. In the Act’s risk management provisions for high-risk AI systems, organizations must “identify and analyze potential risks to health, safety or fundamental rights” and implement mitigation measures proportionate to those risks.
ISO/IEC 42001:2023, the first international standard for AI management systems, provides a structured framework for establishing, implementing, maintaining and continually improving governance and risk processes for AI systems. It aligns with the EU AI Act’s emphasis on risk management, ethical considerations, transparency and accountability, and applies to organizations that develop or use AI systems, regardless of size or sector.
Together, these standards reinforce a consistent governance foundation: define risks clearly, apply controls based on impact and exposure, and integrate risk management into organizational processes. They do not suggest isolating AI into an exception lane with its own ad hoc standards.
At Carbide, we help organizations apply third-party risk management with the level of precision modern tools require. Our platform and expert-led services are designed to support risk-based evaluations that account for real-world data flow, layered dependencies, and evolving usage patterns, including those introduced by AI tools. We help teams strengthen their existing programs so they remain consistent, defensible, and aligned with how technology is actually used across the business. Talk with our team to learn how we can help.
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