What should an AI vendor security questionnaire include for companies using customer data?

Companies adopting artificial intelligence tools must evaluate more than model performance and pricing. When an AI vendor receives customer information, support records, behavioral data, intellectual property, or regulated personal data, the vendor becomes part of the company’s risk environment. A well-designed security questionnaire should therefore examine how data is collected, used, stored, shared, protected, and deleted, while also testing whether the vendor’s stated practices are supported by contracts, technical controls, and independent evidence.

Assess Customer Data Handling and Governance

The questionnaire should begin by asking the vendor to identify every category of customer data it may receive or generate. This includes direct inputs, uploaded files, prompts, conversation histories, metadata, system logs, model outputs, embeddings, and inferred information. The vendor should classify whether any of these categories may contain personal data, confidential business information, payment data, health information, authentication credentials, or other regulated content.

Companies should ask the vendor to explain the specific purposes for which customer data is processed. The response should distinguish processing required to provide the service from optional uses such as analytics, product development, benchmarking, advertising, or model improvement. Vague terms such as “service enhancement” should trigger follow-up questions because they may authorize broader uses than the customer intends.

A complete questionnaire should require a data-flow description covering collection, transmission, processing, storage, backup, and deletion. The vendor should identify the systems, cloud environments, geographic regions, application interfaces, and third parties involved at each stage. Data-flow diagrams are especially useful because they can reveal processing activities or copies of data that are not obvious from privacy policies and contractual summaries.

Retention practices should be examined separately for production data, logs, backups, cached content, embeddings, and disaster-recovery systems. The questionnaire should ask how retention periods are determined, whether customers can configure them, and how quickly information is deleted after contract termination or a customer request. Vendors should also explain whether deletion is immediate, cryptographic, or delayed until backup media rotate out of service.

One of the most important questions is whether customer data is used to train, fine-tune, evaluate, or otherwise improve AI models. The vendor should state whether such use is enabled by default, whether customers can opt out, and whether opting out changes functionality or pricing. Companies should also ask whether data incorporated into a model can later be isolated or removed, since deleting a source record does not necessarily reverse its influence on model parameters.

The questionnaire should identify all subprocessors and other parties with access to customer data, including model providers, cloud hosting companies, annotation services, support contractors, and monitoring platforms. For each party, the vendor should disclose its role, processing location, and access level. Companies should also ask how new subprocessors are approved, how customers are notified of changes, and whether equivalent security and privacy obligations flow down contractually.

Data residency and international transfers require specific scrutiny when customer data may cross national borders. The vendor should identify where data is stored, where remote personnel may access it, and which legal mechanisms support cross-border transfers. The questionnaire should also address whether government access requests are logged, reviewed, challenged when appropriate, and disclosed to customers where legally permitted.

Ownership and control provisions should be tested through direct questions rather than inferred from general terms of service. The vendor should confirm that the customer retains ownership of its inputs and clarify who owns outputs, derivative data, evaluation results, and usage metadata. It should also disclose any licenses it claims over customer content and whether those licenses survive termination of the agreement.

The questionnaire should assess how the vendor supports privacy rights and regulatory obligations. Relevant questions include whether the vendor can locate, export, correct, restrict, or delete data associated with a particular individual and how identity requests are authenticated. The vendor should also explain how it assists with privacy impact assessments, records of processing, consent requirements, breach notifications, and obligations under laws such as the GDPR, CCPA, or sector-specific regulations.

Finally, companies should examine the vendor’s governance structure for approving and monitoring data use. The questionnaire should identify accountable executives, privacy personnel, security leaders, data protection officers, and internal review committees. Strong responses should reference documented policies, recurring risk assessments, employee training, disciplinary processes, and evidence that governance findings lead to corrective action rather than remaining advisory.

Verify Security Controls, Access, and Oversight

The security portion of the questionnaire should start with the vendor’s overall security program and supporting evidence. Companies should ask which frameworks the program follows, such as ISO 27001, SOC 2, NIST Cybersecurity Framework, or NIST AI Risk Management Framework. Certifications and audit reports are useful, but their scope, dates, exceptions, and covered systems must be reviewed to confirm that they apply to the actual AI service handling customer data.

Identity and access management questions should determine who can access customer environments and under what conditions. The vendor should describe role-based access, least-privilege enforcement, multifactor authentication, privileged access management, periodic access reviews, and prompt deprovisioning. The questionnaire should also ask whether engineers or support personnel can view prompts and outputs, whether access requires customer approval, and whether every privileged session is logged.

Encryption controls should cover data in transit, at rest, and within backups or intermediate processing systems. The vendor should identify the protocols and encryption standards used, how keys are generated and rotated, and whether keys are separated from encrypted data. Where risk warrants it, companies should ask about customer-managed keys, hardware security modules, field-level encryption, and controls protecting data while it is actively processed.

For multitenant AI services, the questionnaire should test how the vendor prevents one customer’s data from being exposed to another. Relevant controls include tenant-specific authorization, logical or physical segregation, isolated storage namespaces, API-level checks, and testing for cross-tenant access. Vendors should also explain whether shared model components, vector databases, caches, or retrieval systems could inadvertently return information originating from another customer.

Secure development practices should be evaluated across the entire software and model lifecycle. The questionnaire should address code review, dependency management, software bills of materials, secrets scanning, security testing, change control, and separation between development and production. It should also ask how vulnerabilities are prioritized, how quickly critical issues are remediated, and whether independent penetration tests cover both conventional application risks and AI-specific attack paths.

AI systems introduce threats that traditional questionnaires may overlook. Companies should ask how the vendor mitigates prompt injection, insecure tool use, data poisoning, model extraction, membership inference, model inversion, malicious file uploads, and retrieval-augmented generation leakage. The vendor should describe input validation, output filtering, tool permission boundaries, model evaluations, red-team exercises, and safeguards that prevent generated instructions from overriding system-level controls.

Logging and monitoring questions should establish whether suspicious behavior can be detected and reconstructed. The vendor should specify which user, administrator, model, API, and data-access events are logged; how long logs are retained; and how their integrity is protected. Companies should also ask whether customers can access relevant logs, integrate alerts with their own security systems, and distinguish between normal AI usage and possible exfiltration or automated abuse.

Incident response requirements should address both conventional breaches and AI-specific failures. The vendor should explain how incidents are classified, escalated, contained, investigated, and reported, including scenarios involving unintended model disclosure or exposure through generated outputs. The questionnaire should request defined notification timelines, named communication channels, forensic support commitments, preservation of evidence, and a process for providing root-cause analyses and corrective action reports.

Resilience and continuity controls should confirm that security does not collapse during outages or recovery operations. The vendor should disclose redundancy arrangements, backup frequency, restoration testing, recovery time objectives, recovery point objectives, and dependencies on critical third parties. It should also explain how customer data remains protected during failover, whether recovered backups preserve deletion requests, and how service continuity plans are tested.

Oversight questions should connect technical claims to enforceable accountability. Companies should request audit rights, current assessment reports, remediation status for significant findings, cyber insurance information, and contractual commitments covering confidentiality, breach notification, deletion, and subprocessor control. The final risk decision should consider not only whether controls exist, but also whether the vendor can demonstrate that they operate consistently and whether the customer has meaningful remedies when they fail.

An effective AI vendor security questionnaire is not a generic compliance checklist. It should trace customer data through its full lifecycle, identify uses that may extend beyond service delivery, and verify that governance promises are reinforced by technical controls and contractual obligations. The strongest assessments combine detailed vendor responses with architecture reviews, audit evidence, testing results, and negotiated safeguards, allowing companies to adopt AI capabilities without surrendering visibility or control over customer data. Start Building Agents With the Best Data – Bright Data Click Here