AI Vendor Selection for B2B: Trust, Data Privacy, and the Questions You Must Ask

Trust Is the Decisive Factor in AI Selection

Choosing an AI vendor for your enterprise goes beyond technical specs. It’s a trust decision. You’re handing over sensitive business data — customer information, financial data, intellectual property — to a system that operates as a black box.

Most AI vendor evaluations focus on performance benchmarks: how fast is it? How accurate? These metrics matter, but they’re not the most important questions.

The most important questions are:

• What happens to my data after I send it?
• Does the vendor use my data to train their models?
• What happens if there’s a security breach?
• Can I audit how the AI makes decisions?
• What happens if the vendor changes their policies?

Some vendors already demonstrate this through transparent business models and growing collaboration with companies in regulated industries — a sign that trust is becoming a competitive differentiator, not just a compliance checkbox.

The 12 Critical Questions for Evaluating AI Vendors

Block 1: Data and Privacy

Question 1: “Is my data used to train your models?”

Why it matters: If the vendor uses your data to improve their AI models, there’s a risk that sensitive company information could appear in responses to other users.

Expected answer: “No. Enterprise customer data is not used to train our models in any way.”

Red flags:

• Vague answers like “we continuously improve our models”
• Policies that allow data use with opt-out (should be opt-in)
• Differences between free and enterprise plans in data policies

Question 2: “Where is my data stored and for how long?”

Why it matters: Data residency can be a regulatory requirement (GDPR, local data protection laws) and affects service latency.

Expected answer: Configurable storage region options, clear retention policies, and ability to delete data on demand.

Red flags:

• No data residency options
• Indefinite retention without deletion option
• Lack of clarity about subprocessors and third parties

Question 3: “What security certifications do you have?”

Why it matters: Certifications are external validation of the vendor’s security practices.

Minimum expected certifications:

• SOC 2 Type II: audit of security, availability, and privacy controls
• ISO 27001: information security management system
• Regulated industries: HIPAA (healthcare), PCI DSS (payments), FedRAMP (government)

Question 4: “How do you handle a security breach?”

Why it matters: Every vendor will face a security incident eventually. What matters is how quickly and transparently they respond.

Expected answer: Documented incident response plan with defined notification times (ideally less than 72 hours), containment process, and transparent communication.

Block 2: Model and Transparency

Question 5: “Can I audit how the model makes decisions?”

Why it matters: If the AI model is making decisions that affect customers, employees, or finances, you need to be able to explain the reasoning.

Expected answer: Detailed logs of every decision, ability to “replay” interactions, and explainability mechanisms that document why the model reached a conclusion.

Question 6: “What happens when the model makes a mistake?”

Why it matters: All AI models make mistakes. What matters is how they’re detected, corrected, and prevented.

Expected answer: Error detection mechanisms, escalation processes, feedback capability that improves the system, and clear responsibility for who corrects errors and when.

Question 7: “What model are you using and which version?”

Why it matters: Models are updated frequently, and each update can change behavior. You need to know what you’re using and when it will change.

Expected answer: Specific model versions, update schedule with advance notice, and ability to remain on a version if the new one causes problems.

Block 3: Integration and Operations

Question 8: “What SLAs do you offer for availability and performance?”

Why it matters: If your operation depends on the AI service, an outage can paralyze your business.

Expected minimums:

• Availability: 99.9% uptime (maximum 8.7 hours of downtime per year)
• Latency: P95 under 2 seconds for standard operations
• Throughput: guaranteed capacity for your transaction volume
• Compensation: credits or refunds when SLAs are not met

Question 9: “How does it integrate with our existing systems?”

Why it matters: Integration is where most projects fail. A vendor that facilitates integration significantly reduces risk.

Expected answer: Well-documented APIs, SDKs for the languages and platforms your company uses, support for standards like MCP, and experience with your industry’s specific systems.

Question 10: “What is the pricing model and how does it scale?”

Why it matters: AI costs can scale quickly with volume. You need to understand the complete structure to project costs.

Follow-up questions:

• Price per token, per API call, or per user?
• Are there volume discounts?
• What are the hidden costs (storage, processing, support)?
• How do prices change if I double my usage?

Block 4: Governance and Future

Question 11: “What happens if I want to switch vendors?”

Why it matters: Vendor lock-in is a significant risk in AI. If your vendor changes prices, policies, or quality, you need to be able to migrate.

Expected answer: Standards-based APIs (not proprietary), ability to export data and configurations, and reasonable transition periods.

Red flags:

• Proprietary formats without export capability
• Contracts with excessive lock-in clauses
• Dependency on exclusive features without alternatives

Question 12: “What is your vision and roadmap for the next 2 years?”

Why it matters: AI evolves rapidly. You need a vendor whose direction is compatible with yours.

Evaluation:

• Do they invest in proprietary research or only resell?
• Does their roadmap align with your industry’s needs?
• Do they have financial stability to exist in 2-3 years?
• How do they approach responsible AI and ethics?

Build an AI Governance Framework

Beyond selecting the right vendor, your company needs an internal governance framework that defines how AI is used across the organization.

Component 1: AI Usage Policy

Clearly define:

• What data can be processed with AI and which is prohibited
• What decisions AI can make autonomously vs. with human oversight
• Who is responsible when AI makes a mistake
• How decisions made by AI are audited

Component 2: Data Classification for AI

Not all data has the same sensitivity level:

Level	Data Type	AI Policies
Public	Marketing content, product data	Can be processed with any AI service
Internal	Procedures, technical documentation	Requires vendor with NDA
Confidential	Customer data, financial	Only vendors with SOC 2 and no-training policy
Restricted	PII, regulated data, trade secrets	Only on-premise models or with maximum guarantees

Component 3: AI Committee

A cross-functional group that:

• Evaluates new use cases before implementing them
• Reviews vendors annually
• Monitors incidents and updates policies
• Trains the organization on responsible AI use

Component 4: Continuous Evaluation Process

Vendor evaluation isn’t a one-time event — it’s an ongoing process:

• Quarterly: review performance metrics, costs, and SLA compliance
• Semi-annually: evaluate vendor policy changes and new market options
• Annually: complete security and compliance audit
• Upon changes: re-evaluation when vendor updates terms, pricing, or models

AI Business Models Ranked by Trustworthiness

Model 1: AI as a Service (API)

How it works: You pay per use (tokens, API calls) and the vendor handles all infrastructure.

Pros: Low initial cost, immediate scalability, access to the latest models.

Cons: Vendor dependency, data travels to the cloud, costs can scale.

Ideal for: Companies that need flexibility and don’t have extreme regulatory restrictions.

Model 2: AI Deployed in Your Cloud

How it works: The vendor deploys their models in your cloud infrastructure (AWS, Azure, GCP).

Pros: Data remains in your control, easier regulatory compliance, customization.

Cons: Higher cost, requires operations team, models may be less current.

Ideal for: Companies in regulated industries or with strict data residency requirements.

Model 3: On-Premise AI

How it works: Models run on your own servers.

Pros: Complete data control, no internet dependency, maximum privacy.

Cons: Very high cost, more limited models, requires specialized team.

Ideal for: Organizations with the strictest security requirements (government, defense, healthcare).

Model 4: Hybrid

How it works: Combine cloud models for non-sensitive data and local models for confidential data.

Pros: Balance between capability and control, optimized costs, flexible compliance.

Cons: Greater architectural complexity, requires clear governance.

Ideal for: Most mid-market and large B2B companies.

How Do You Protect Your Company’s Data When Using AI?

Essential Technical Measures

1. End-to-end encryption: data encrypted in transit (TLS 1.3) and at rest (AES-256)
2. Tokenization: replace sensitive data with tokens before sending to the model
3. Data masking: mask PII and confidential data before processing
4. VPN or private endpoints: private connections instead of public internet
5. Audit logging: immutable record of all interactions with the AI service

Essential Contractual Measures

1. Data Processing Agreement (DPA): formal agreement on how your data is processed
2. No-training clause: explicit prohibition on using your data to train models
3. Breach notification: obligation to notify within a specific timeframe
4. Right to audit: right to audit the vendor’s security practices
5. Data deletion: ability to request complete deletion of all data

What Red Flags Indicate an Untrustworthy Vendor?

1. Ambiguous data policies: if they can’t clearly explain what they do with your data, walk away
2. No security certifications: SOC 2 is the minimum for enterprise use
3. Frequent changes to terms of service: indicates instability in their business model
4. No written SLAs: if guarantees are verbal, they don’t exist
5. No data deletion option: your data should be deletable on demand
6. Breach history without transparency: incidents happen; lack of transparency is unacceptable
7. Opaque pricing: if you can’t predict your costs, you’ll find surprises
8. No support for your industry: a vendor that doesn’t understand your regulations is a risk

Putting This Into Practice

The vendor you choose will handle sensitive business data for years. Apply the 12 questions above to any vendor you’re evaluating, and establish an internal AI committee — even a small one with representatives from IT, legal, and operations — to own the governance process.

If you’re evaluating vendors right now, use the 12 questions as a scorecard: send the questionnaire to each candidate and compare responses side by side. In our experience, serious vendors respond in detail within a week; those who stall or dodge are already giving you an answer.

Need help with the evaluation? Reach out — we’ve guided B2B companies through AI vendor selection with governance and privacy criteria from day one.