What Should You Require from AI Vendors?

Why AI-enabled vendors need AI-specific cybersecurity assurance

AI is quickly becoming part of the enterprise vendor ecosystem.

Organizations are using AI-enabled tools for customer support, clinical workflows, document processing, analytics, fraud detection, productivity, security operations, software development, automation, and decision support. In many cases, AI is no longer a standalone technology. It is embedded inside the products and services organizations already rely on.

That creates a new third-party risk challenge.

When a vendor uses AI, the relying organization may inherit risks that are not always visible in traditional vendor review processes. Sensitive data may flow through AI-enabled systems. AI tools may influence business-critical decisions. Models may interact with users, applications, workflows, and external systems. AI agents may be granted access to information, tools, or actions that expand the risk surface.

For relying parties, this raises a practical question:

What should we require from vendors that develop, deploy, or materially rely on AI?

The answer should go beyond a policy, a questionnaire, or a general security report. Customers need AI-specific cybersecurity assurance.

General assurance may not answer AI-specific questions

Traditional assurance reports can provide useful information about a vendor’s broader control environment. But AI introduces risks that may not be explicitly addressed unless they are specifically defined, assessed, and validated.

A vendor may have a general cybersecurity program. It may have an AI policy. It may even have a broad security report. But that does not necessarily mean its deployed AI systems have been evaluated against AI-specific security and governance expectations.

Relying parties increasingly need answers to questions such as:

• Does the vendor use AI in products, services, workflows, or decision-making that affect our data or operations?

• What sensitive data can the AI system access, process, generate, or expose?

• How are AI models, prompts, outputs, integrations, and supporting systems governed?

• How are AI-specific threats such as prompt injection, data leakage, model misuse, unsafe outputs, or unauthorized tool use addressed?

• How is access to AI systems controlled and monitored?

• How are AI-enabled vendors validating that their controls are operating effectively?

   

These questions are not theoretical. They are becoming central to how organizations evaluate vendor trust.

AI vendor risk is still vendor risk

AI can make vendor relationships more complex, but it does not change the basic responsibility of third-party risk management.

Organizations still need to understand what risk they inherit from vendors. They still need assurance that vendor controls are appropriate to the risk. They still need evidence that those controls have been validated. And they still need a way to apply consistent requirements across vendor populations.

What changes with AI is the type of assurance needed.

When AI systems touch sensitive data, critical workflows, customer-facing services, regulated processes, or business operations, relying parties should not have to infer whether AI risks were addressed inside a broad report. They should be able to ask for evidence that the AI system itself has been evaluated.

That is where AI-specific certification becomes important.

Why HITRUST AI Security Certification matters

HITRUST AI Security Certification is now available as a standalone offering for deployed AI systems and AI-enabled technologies.

That matters because organizations now have a more direct way to demonstrate or require AI-specific cybersecurity assurance. Vendors developing or deploying AI can pursue a focused certification for AI systems. Relying parties can point to a clearer assurance expectation for vendors whose AI solutions may introduce meaningful risk.

This is especially important because AI risk often sits at the intersection of cybersecurity, governance, data protection, model behavior, software security, cloud infrastructure, and third-party dependency. A general statement of AI responsibility may not be enough. A broad security attestation may not provide enough specificity. A questionnaire may not provide enough validation.

HITRUST AI Security Certification helps move the conversation from “Do you use AI responsibly?” to “Can you demonstrate validated assurance over the security of your deployed AI system?”

That is a stronger question. It is also a more useful one.

When should relying parties require AI-specific certification?

Not every use of AI carries the same risk. A low-risk internal productivity tool may not require the same assurance as an AI-enabled platform that processes sensitive customer data or supports business-critical decisions.

But relying parties should consider requiring AI-specific certification when a vendor’s AI system:

• Processes, stores, transmits, analyzes, or generates sensitive data

• Supports regulated, clinical, financial, security, legal, or business-critical workflows

• Is embedded in a product or service delivered to customers

• Uses AI agents or automation that can access systems, data, or tools

• Influences decisions that affect customers, patients, members, employees, or business operations

• Relies on third-party models, platforms, or infrastructure that may affect risk

• Provides AI-enabled functionality that materially changes the vendor’s risk profile

   

In these cases, general assurance may not be enough to support confident vendor reliance.

A practical requirement for AI vendors

Customer organizations can begin by adding clearer AI assurance language to vendor requirements, procurement processes, and third-party risk policies.

A practical requirement could look like this: For vendors that develop, deploy, or materially rely on AI systems that process sensitive data, support critical workflows, or deliver customer-facing functionality, HITRUST AI Security Certification is preferred or required as evidence of AI-specific cybersecurity assurance.

This kind of language gives organizations a stronger and more consistent way to evaluate AI-enabled vendors. It also gives vendors a clear path to demonstrate that they are taking AI security seriously.

The goal is not to slow AI adoption. The goal is to make AI adoption more trustworthy.

What customer organizations should ask now

As AI becomes more deeply embedded across vendor ecosystems, relying parties should revisit their vendor review processes and ask:

Do we know which vendors are using AI in ways that affect our data, workflows, or customers?

• Do our current vendor requirements distinguish between general cybersecurity assurance and AI-specific assurance?

• Are we relying on broad reports that may not explicitly address AI risk?

• Do our procurement, security, compliance, and risk teams have a consistent standard for AI-enabled vendors?

• Have we defined when AI-specific certification should be required?

These questions can help organizations move from ad hoc AI vendor review to a more scalable assurance model.

AI trust needs evidence

AI adoption will continue to accelerate. Vendors will continue embedding AI into products, platforms, services, and workflows. Relying parties will continue inheriting risk from technologies they may not fully control.

That means trust cannot depend on claims alone.

For AI-enabled vendors, validated AI cybersecurity assurance can help demonstrate that deployed AI systems are being secured and governed with the seriousness customers expect.

For customers relying on those vendors, AI-specific certification can help reduce ambiguity, improve consistency, and strengthen confidence in vendor decisions.

AI is changing what organizations build, buy, and rely on.

It should also change what organizations require.

Learn how HITRUST AI Security Certification can help your organization evaluate, require, or demonstrate validated cybersecurity assurance for deployed AI systems.