Your organization may have strict controls for SaaS applications, cloud platforms, and technology vendors. But what happens when an employee connects a third-party generative AI tool to your business data?
That question is becoming increasingly important.
Generative AI has changed the traditional third-party risk equation. A vendor may no longer simply store or process your data. Its AI models may analyze prompts, retain inputs, generate outputs, interact with APIs, retrieve information from connected systems, or use data to improve its services.
For organizations adopting AI at scale, vendor risk now extends far beyond conventional questionnaires and compliance certificates. You need to understand how AI is built, what data it touches, where that data travels, and what the vendor can do with it.
Why Third-Party AI Tools Create a Different Risk
Traditional third-party risk management typically examines areas such as infrastructure security, access controls, encryption, incident response, privacy, and regulatory compliance.
These controls remain important, but AI introduces additional variables.
Consider an employee using an AI-powered coding assistant. The tool may process source code, configuration details, credentials accidentally included in prompts, or proprietary business logic. A marketing platform using generative AI may process customer information. An AI-powered support application could access internal knowledge bases and customer conversations.
The vendor is no longer simply providing software. It may be operating an intelligent processing layer between your employees, your data, and other business systems.
That makes Generative AI Risk a third-party risk issue as much as an AI security concern.
The Vendor Risk Questions Have Changed
When evaluating an AI vendor, asking whether the provider is ISO certified or SOC 2 compliant is useful, but it is not enough.
You also need to ask:
- Does the vendor use customer data to train or improve its AI models?
- Where are prompts, uploaded files, and generated responses stored?
- How long is customer data retained?
- Can administrators or support personnel access customer prompts?
- Does the AI service send data to other sub processors or model providers?
- What happens to your data when the contract ends?
- Can the vendor change its underlying AI model without notifying customers?
- How does the provider protect against prompt injection and model manipulation?
- What controls exist around AI agents that can perform actions?
- Can the vendor provide sufficient logging for investigations and audits?
These questions help uncover risks that conventional vendor assessments can easily overlook.
Data Exposure Is No Longer Limited to Databases
One of the biggest changes introduced by generative AI is the way employees interact with business information.
A user does not need to upload an entire database to create a security problem. A single prompt can contain customer details, source code, internal reports, credentials, intellectual property, or confidential strategy information.
Once that information reaches an external AI service, your organization needs visibility into what happens next.
This is particularly concerning with unsanctioned or “shadow AI” applications. Employees may adopt free AI tools because they are convenient, while security teams have limited visibility into what information is being submitted.
For this reason, modern AI security controls should complement traditional data protection, identity, endpoint, and cloud security measures.
AI Supply Chains Are Becoming More Complex
Another important consideration is the AI supply chain itself.
An AI application may rely on a foundation model from one provider, cloud infrastructure from another, vector databases from a third party, and multiple APIs or plugins from additional vendors.
Your contract may be with one company, but your data could pass through several technology layers.
This creates a fourth-party risk problem.
A weakness in a model provider, API, plugin, data processor, or infrastructure component could potentially affect your organization even though you never directly selected that provider.
AI vendor assessments therefore need to examine the broader technology ecosystem rather than focusing exclusively on the primary supplier.
Model Behavior Is Now Part of Vendor Risk
With conventional software, organizations generally evaluate whether the application performs its intended function securely.
Generative AI introduces another variable: model behavior.
AI systems can produce inaccurate information, reveal sensitive context, follow malicious instructions, or behave unpredictably when connected to external data and tools.
For example, an AI assistant connected to internal documents could encounter malicious instructions hidden inside a document. If the application is not properly designed, the model could interpret those instructions as legitimate commands.
This is why AI-specific threats such as prompt injection, data leakage, excessive permissions, and insecure agent workflows should become part of third-party assessments.
What a Strong AI Vendor Assessment Should Cover
A practical AI vendor review should examine several layers.
Data governance: Determine what information enters the AI service, how it is classified, where it is stored, and how long it remains there.
Model governance: Understand the model provider, training practices, update processes, testing procedures, and limitations.
Security architecture: Review encryption, authentication, access controls, API security, isolation, logging, and monitoring.
AI-specific controls: Evaluate protections against prompt injection, data leakage, malicious inputs, model abuse, and unauthorized AI actions.
Sub processors: Identify the vendors involved in model hosting, infrastructure, analytics, support, and data processing.
Incident response: Establish what happens if the AI provider suffers a breach, model compromise, data exposure, or service disruption.
Contractual protections: Define data ownership, retention, deletion, breach notification, audit rights, and requirements for material changes to AI services.
Moving From Vendor Compliance to Continuous AI Risk Management
A one-time vendor questionnaire cannot capture how quickly AI services evolve.
Models are updated. New capabilities are introduced. Vendors add sub processors. AI agents gain additional functionality. APIs change. Data processing practices can also evolve.
Your third-party AI risk program should therefore become continuous.
Monitor changes to vendor architecture, model functionality, data-processing terms, security incidents, sub processors, and regulatory requirements. High-risk AI applications should receive more frequent reviews than low-risk productivity tools.
You should also classify AI vendors according to the sensitivity of the information they process and the level of autonomy they possess.
An AI tool that only generates generic marketing ideas presents a different risk profile from an AI agent that can access customer records and execute business actions.
AI Vendor Risk Requires a Broader Security Strategy
Generative AI is not replacing conventional third-party risk management. It is expanding it.
Organizations now need to evaluate vendors based on both traditional cybersecurity controls and AI-specific risks. The most effective approach combines vendor due diligence with identity controls, data protection, application security, monitoring, access governance, and continuous risk assessment.
At Know All Edge, we help organizations evaluate and implement modern cybersecurity technologies based on their infrastructure, data environment, and operational requirements. As AI adoption accelerates, the objective should not be to block every third-party AI tool. It should be to understand where AI creates exposure and establish practical controls that allow innovation without losing visibility or control.
The question is no longer simply, “Is this vendor secure?”
It is “What happens to our data, access, and security posture when this vendor’s AI becomes part of our environment?”
That is the question modern third-party risk management needs to answer.
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