AI Vendor Risk Management: What to Check Before Adopting a Third-Party AI Provider

Organizations are using external LLMs, AI SaaS platforms, AI copilots, AI APIs, AI development tools, AI agents, RAG services, vector databases, and specialized AI platforms to accelerate business operations.

This creates a new cybersecurity challenge.

Can your organization trust an AI provider with its data, systems, and business processes?

Traditional Third-Party Risk Management already evaluates areas such as cybersecurity, privacy, compliance, availability, business continuity, and financial stability.

Those controls remain important.

But they are no longer enough for AI vendors.

An AI provider may process sensitive prompts, uploaded documents, source code, customer information, conversations, or data retrieved from enterprise systems.

It may also use third-party models, subprocessors, cloud infrastructure, AI agents, connectors, APIs, and external tools.

The result is a much more complex risk relationship.

What Is AI Vendor Risk Management?

AI Vendor Risk Management is the process of identifying, assessing, monitoring, and managing the cybersecurity, privacy, compliance, operational, and AI-specific risks associated with third-party AI providers.

It extends traditional vendor risk management into the AI environment.

For example, evaluating an AI SaaS provider should involve more than checking whether the company has a SOC 2 report or ISO certification.

Security teams should also ask:

• What happens to customer prompts?
• Are conversations retained?
• Is customer data used for model training?
• Where is the data processed?
• Which subprocessors receive it?
• Which models power the service?
• Can the provider change the underlying model?
• Can AI agents access enterprise systems?
• What integrations are available?
• What logs can customers access?
• How quickly will the provider report a security incident?

These questions can reveal risks that traditional vendor assessments may miss.

Start by Understanding the Vendor's Role

Not every AI vendor creates the same level of risk.

A provider may offer:

• An LLM API
• An AI SaaS application
• An AI agent platform
• A RAG service
• A vector database
• An AI gateway
• An AI development platform
• AI infrastructure
• AI security services

The risk depends heavily on what role the provider plays.

An AI model API may primarily process prompts and generate responses.

An AI agent platform could potentially access corporate email, CRM systems, cloud storage, databases, source-code repositories, and internal applications.

The second scenario requires significantly deeper due diligence.

Identify What Data the Vendor Will Receive

Data classification should be one of the first steps in an AI vendor assessment.

Before approving a provider, organizations should identify exactly what information will be processed.

This could include:

• Customer information
• Employee information
• Financial records
• Intellectual property
• Source code
• Business strategies
• Confidential documents
• Credentials
• Personal information
• Regulated information

A vendor suitable for public marketing content may not be suitable for confidential financial information.

Security teams should compare the vendor's capabilities against the organization's data-classification policy.

If a provider cannot meet the required controls for a specific data category, that category should be restricted.

Understand Prompt and Response Retention

AI platforms process prompts and generated responses as part of their normal operation.

But organizations need to understand what happens to those interactions afterward.

Important questions include:

Are prompts retained?

How long are they stored?

Can retention be disabled?

Can administrators configure retention periods?

Can vendor employees access customer conversations?

Are generated responses retained?

Is conversation history available to the provider?

These details can materially affect the security and privacy risk of an AI platform.

Can the Vendor Use Your Data for Model Training?

This is one of the most important questions in AI vendor due diligence.

Organizations should determine whether customer prompts, uploaded documents, responses, feedback, or other information can be used for:

• Model training
• Fine-tuning
• Evaluation
• Service improvement
• Product development

If customer data can be used for these purposes, organizations should understand whether they can opt out and whether that opt-out is technically and contractually enforceable.

For sensitive enterprise applications, informal assurances should not be enough.

The requirements should be clearly documented in the vendor's contractual and technical documentation.

Where Is Your AI Data Processed?

Data location matters.

AI providers may process information across multiple countries or regions.

They may also rely on different cloud providers and infrastructure partners.

Organizations should establish:

• Where data is stored
• Where model inference occurs
• Whether data moves between regions
• Which countries can process the information
• Which subprocessors receive the data
• What data-residency controls are available

This is especially important for organizations subject to regulatory, contractual, or data-residency requirements.

Understand the AI Vendor's Subprocessors

An AI provider may not operate the complete service itself.

It may depend on:

• Cloud infrastructure providers
• Foundation model providers
• Embedding services
• Observability platforms
• Security providers
• Content moderation services
• Data-processing providers
• External APIs

This creates a chain of dependencies.

If sensitive enterprise data passes through multiple organizations, assessing only the primary AI vendor provides an incomplete picture.

Security teams should identify material subprocessors and understand what role each one plays.

Evaluate the AI Supply Chain

AI supply chains can be significantly more complex than traditional software supply chains.

An AI application may depend on:

Foundation Model → API → AI Application → RAG → Embedding Model → Vector Database → Connector → Enterprise Application

A weakness in one component can potentially affect the larger environment.

Vendor assessments should therefore consider:

• Dependency management
• Software supply-chain security
• Model supply-chain practices
• Vulnerability management
• Third-party risk management
• Open-source components
• External APIs
• Plugins and connectors

The question isn't simply:

"Is the AI vendor secure?"

It is:

"How secure is the complete ecosystem that delivers this AI service?"

What Foundation Model Does the Vendor Use?

Some AI vendors don't develop their own underlying models.

They may rely on models provided by another company.

That introduces another dependency.

Organizations should determine:

• Which models power the service
• Who owns those models
• Whether the model provider can change
• Whether customer data is shared with the model provider
• How model changes are communicated
• Whether customers can control model selection

This becomes especially important when the vendor offers multiple models from different providers.

Model Changes Can Create New Risks

AI models evolve quickly.

A provider may change model versions, safety behavior, system instructions, inference architecture, or available capabilities.

These changes can affect:

• Security
• Privacy
• Performance
• Compliance
• Data processing
• AI behavior

For high-risk AI applications, organizations should understand how material model changes are communicated and whether security review or customer approval is possible.

Evaluate Traditional Security Controls

AI vendors still need strong conventional cybersecurity controls.

Organizations should assess:

• Identity and access management
• MFA
• SSO
• Privileged access management
• Encryption
• Vulnerability management
• Secure development
• Network security
• Endpoint security
• Security monitoring
• Incident response
• Business continuity

However, traditional controls should be assessed alongside AI-specific security capabilities.

A vendor can have excellent infrastructure security and still create significant AI risk if it retains customer prompts unnecessarily or provides an AI agent with excessive privileges.

Assess AI-Specific Security Testing

AI vendors should be evaluated on whether they actively test AI-specific attack paths.

Security teams should ask about testing for:

• Prompt Injection
• Indirect Prompt Injection
• Data leakage
• RAG vulnerabilities
• AI agent abuse
• Excessive agent permissions
• API abuse
• Connector vulnerabilities
• Model abuse
• Adversarial inputs

Traditional penetration testing remains important.

But it may not identify all AI-specific vulnerabilities.

For high-risk AI services, organizations should seek evidence of dedicated AI security testing or AI Red Teaming.

AI Agents Require Additional Scrutiny

AI agents significantly change the vendor-risk equation.

An agent may be capable of accessing email, databases, cloud storage, CRM platforms, source-code repositories, and business applications.

Organizations should determine:

What can the agent access?

What actions can it perform?

Can permissions be restricted?

Can high-risk actions require human approval?

Are agent actions logged?

Can the organization immediately disable the agent?

Least privilege should apply to AI agents just as it applies to human and machine identities.

Evaluate Connectors, APIs, and OAuth

Third-party AI platforms often integrate with enterprise applications through APIs, connectors, plugins, OAuth applications, or emerging tool-integration mechanisms.

Each integration creates another attack surface.

The vendor assessment should determine:

• Which integrations are supported
• What permissions they require
• How authentication works
• Whether permissions can be restricted
• Whether access can be limited by user or application
• Whether connector activity is monitored
• How quickly access can be revoked

An AI application requesting access to an entire corporate cloud-storage environment should receive significantly more scrutiny than one restricted to a specific repository.

Can You Investigate an AI Incident?

One of the most overlooked vendor-risk questions is logging.

Ask the provider:

If an AI security incident happens tomorrow, what evidence will we have?

The vendor should provide appropriate audit visibility into relevant activities such as:

• Authentication
• User activity
• Administrative changes
• Model usage
• Data access
• Agent actions
• Connector activity
• API requests
• Security events

Without sufficient logging, even a well-secured AI platform can become difficult to investigate.

Vendor Incident Response Matters

Organizations should understand how an AI provider handles security incidents before signing the contract.

Important questions include:

• How are incidents detected?
• How quickly are customers notified?
• What information is included?
• How is evidence preserved?
• How is containment performed?
• Who is responsible for investigation?
• What happens if a subprocessor is involved?

For critical AI providers, incident notification timelines and responsibilities should be addressed contractually.

The AI Vendor Relationship Does Not End at Procurement

One of the biggest mistakes organizations can make is treating vendor assessment as a one-time activity.

AI providers continuously change.

Models change.

Subprocessors change.

Features change.

Integrations expand.

AI agents become more capable.

Security policies evolve.

The vendor's risk profile can therefore change after onboarding.

AI Vendor Risk Management needs to become a continuous process.

A Practical AI Vendor Assessment Should Answer One Question

Before adopting a third-party AI provider, organizations should be able to explain:

What data does this vendor receive, where does it go, who can access it, what can the AI do, which third parties are involved, and what happens if something goes wrong?

If the organization cannot answer those questions, the vendor assessment isn't complete.

AI adoption should not begin with:

"Does the vendor have AI?"

It should begin with:

"Can we securely govern the AI vendor throughout its entire lifecycle?"

As enterprises increasingly depend on third-party AI providers, AI Vendor Risk Management will become a critical component of enterprise AI governance and security.

Read the complete guide:

https://digitaldefense.co.in/blogs/ai-vendor-risk-management-assessing-third-party-ai-providers-before-adoption