procurementgovernancesecurity

Data Governance Checklist for Enterprises Buying AI Platforms (What to Ask Vendors)

ddatawizard

2026-02-10

12 min read

Vendor due-diligence checklist for AI platforms: ask the right questions on data residency, model provenance, FedRAMP, SLAs, and costs.

Hook: Why procurement teams are waking up to AI platform risk

If your procurement team is evaluating AI platforms in 2026, you’re buying more than software — you’re buying a data supply chain, an operational model, and an ongoing liability. Teams we work with list the same pain points: unpredictable cloud spend, unclear data residency, opaque model provenance, and SLAs that avoid real operational guarantees. This checklist arms procurement, security, and legal teams with the questions and contract language you need to reduce risk and control costs.

Why rigorous vendor due diligence matters in 2026

The last 18 months reshaped vendor risk calculus. Late 2025 saw several acquisitions of FedRAMP-approved AI platforms and accelerated public-sector demand for compliant AI (for example, BigBear.ai’s purchase of a FedRAMP-authorized platform), and regulators in the EU and globally began enforcing the EU AI Act and updated guidance from NIST. Buyers must now validate not only cybersecurity posture but also model lineage, dataset provenance, and contractual controls over inference and trained artifacts. Without that, enterprises inherit compliance gaps and uncontrolled operating costs.

How to use this article

This is a practical, procurement-ready vendor due-diligence checklist that focuses on five high-risk domains: data residency, model provenance, compliance (FedRAMP and peers), SLAs, and economic considerations. Each section contains concrete questions to ask, example contractual language, and scoring guidance to help you compare vendors objectively.

1. Data residency & sovereignty: ask where data lives and who can access it

Data residency is no longer theoretical. Governments and industry regulators have tightened rules about where and how personal and regulated data can be processed. Procurement must verify physical and logical location, cross-border transfer mechanisms, and deletion guarantees.

Questions to ask the vendor

Where are production and backup data centers located (regions, physical sites, cloud providers)?
Can you guarantee that customer data and derived models for our tenant remain in a specific country/region? If so, how is that enforced?
Which sub-processors and third-party services have access to our data? Provide a current list and contractually required notification period for changes.
What mechanisms exist for cross-border transfers (standard contractual clauses, adequacy, SCCs)?
How do you implement data segregation for multi-tenant deployments (dedicated VPC, separate projects/accounts, logical partitions)?

Example contractual language

"Vendor shall store and process Customer Data only in the following regions: [list]. Vendor will not transfer Customer Data outside these regions without Customer’s prior written consent. Vendor shall provide a deletion certificate within 10 business days after deletion requests."

2. Model provenance: traceability, lineage, and explainability

Vendors often treat models as products, but procurement must treat them as assets derived from data with regulatory weight. In 2026, auditors and regulators are asking for model lineage and documentation (model cards, datasheets). You need to know the model’s ancestry, training data sources, and update cadence.

Key checks

Request a model card and training-data datasheet for any pre-trained models you will use — include provenance, known biases, and limitations.
Ask whether the vendor uses third-party or open-source models and how licenses and data-use terms are handled downstream.
Confirm the vendor’s capability to provide lineage and versioning for model artifacts and training datasets (audit-ready logs for model creation, retraining, and deployment).
Verify mechanisms for watermarking or fingerprinting model outputs to prove provenance and detect model theft or leakage.

Operational questions

How are model updates governed and communicated (change control process, TTL for deployed models)?
Do you support reproducible training (saved environment, random seeds, container images) so that models can be re-created for audits?
What metrics do you provide for concept drift and data drift, and what are your automated remediation options?

3. Compliance & certifications: FedRAMP, ISO, SOC, EU AI Act

Compliance is a spectrum. Public-sector customers require FedRAMP Moderate or High; healthcare customers need HIPAA assurances; finance needs PCI/FFIEC alignment; and the EU AI Act creates new obligations for high-risk systems. Don’t assume parity — validate.

What to validate

FedRAMP status and authorization boundary: ask for the Authorization to Operate (ATO) letter, package, and if FedRAMP High/Moderate/Low.
Current SOC 2 Type II and ISO 27001 certificates, and the auditor reports. Ask for recent SOC/ISO remediation plans if any findings exist.
Specific regulatory fit: HIPAA, PCI-DSS, GDPR articles invoked, local data-protection laws for your jurisdictions.
Evidence of alignment to NIST AI RMF (ask for mappings to the vendor’s controls). NIST updates in 2024–2025 pushed AI-specific guidance that auditors expect to see in vendor control documents.

Contractual asks

Include audit rights (on-site or remote) and a commitment to provide penetration-test results and remediation timelines.
Require notification windows for security incidents (e.g., 72 hours for data breaches), and specific forensic support levels.

4. SLAs & operational guarantees: beyond uptime

Standard uptime SLAs (99.9% etc.) are table stakes. For AI platforms, you need measurable guarantees for inference latency, model availability, data durability, and operational response times — and clarity on remedies.

Ask for SLAs on

Uptime for control plane and data plane, and measurement methodology.
Inference latency percentiles (p50, p95, p99) for typical workloads and for burst loads.
Model availability (percentage of time a deployed model responds within SLA bounds).
Backup & restore RTO/RPO targets for models and training data.
Security incident response times and escalation matrices.

Penalties, credits, and exit triggers

Define service credits for SLA violations, and include exit triggers for prolonged non-compliance (e.g., 3 consecutive months of degraded availability below agreed thresholds). Ensure credits are scalable and meaningful compared to subscription revenue.

5. Economic & procurement considerations: pricing model, TCO, and hidden costs

Vendors offer wide-ranging pricing models in 2026: per-inference, per-GB stored, per-GPU-hour, per-seat, and flat platform fees. Don’t focus solely on headline per-call prices — build a 3-year TCO model that includes egress, monitoring, logging, backups, retraining costs, and integration effort.

Key cost questions

What are the pricing dimensions (inference, training, storage, egress, monitoring)? Ask for example invoices from comparably sized customers.
Do you charge for logs, metrics, or model explainability reports? What are the typical data-retention charges?
Are there committed-use discounts or reserved capacity with predictable pricing (useful for steady inference workloads)?
How are overage charges applied and reported? Insist on daily/weekly cost dashboards during PoC.
Ask for a modeled cost runbook for 3 scenarios: pilot (low), scale-up (sustained), and burst/peak (e.g., Black Friday type peaks).

Negotiation levers

Bundled professional services vs. time-and-materials for integration and data migration.
Cap on data egress costs for the first 12 months or a defined egress pricing schedule.
Include price-review clauses that allow renegotiation if underlying cloud cost drivers change materially.

6. Security, PII protection & encryption

Buyers must verify end-to-end protections for PII and regulated data. That starts with encryption, but also includes key management, access controls, logging, and secure software supply chain practices.

Must-ask security details

Is data encrypted in transit and at rest? Is customer-managed key (BYOK) supported? (Ask for technical proof.)
How is access to customer data controlled and logged (role-based access control, IAM, just-in-time access)?
Do you produce an SBOM (software bill of materials) for your runtime and dependencies? How do you manage supply-chain vulnerabilities?
Do you run regular third-party pen tests and red-team exercises? Will you share results or a summary of findings and mitigations?

7. Legal, IP, and exit planning

The trickiest long-term vendor risk is lock-in. Define what happens to your data and models at contract termination, who owns derivative models, and whether you can run the platform on-prem or in a self-hosted setup.

Contractual points to insist on

Data ownership: Customer retains all rights to raw input data and derived artifacts.
Model weights and artifacts: Vendor delivers trained model artifacts (weights, metadata, container images) in a standard, documented format upon termination or on-demand for escrow.
Escrow: Include source-code or model-weights escrow for critical components with release triggers (bankruptcy, failure to meet SLAs).
Transition assistance: Vendor provides 90 days of transition support to export data and models and a runbook for redeployment.
IP indemnities: Warranty on non-infringement for vendor-provided models and datasets; clear carve-outs for customer-supplied data.

8. Risk-assessment scoring template (practical, quick to use)

Use a simple weighted scoring model to compare vendors across the checklist. Here’s a practical rubric you can copy into an RFP evaluation sheet.

Data Residency (weight 20): 0–5 score (0 = cannot enforce, 5 = region-specific guarantees + contractual penalty).
Model Provenance (weight 20): 0–5 score (0 = opaque, 5 = full lineage, model cards, reproducibility).
Compliance & Certifications (weight 15): 0–5 score (0 = none, 5 = FedRAMP High + SOC2 + ISO or equivalent).
SLAs (weight 15): 0–5 score (0 = vague, 5 = measurable SLAs + meaningful credits + exit triggers).
Economics & TCO (weight 15): 0–5 score (0 = opaque pricing, 5 = clear TCO examples + capped egress).
Security Practices (weight 10): 0–5 score (encryption, BYOK, SBOM, pen tests).
Exit & IP (weight 5): 0–5 score (escrow, ownership, transition assistance).

Multiply each score by the weight and sum to get a 0–500 scale. Use this to rank vendors and to justify selection decisions to stakeholders.

Practical procurement steps & negotiation playbook

Follow these steps to move from shortlist to contract quickly while keeping risk low.

Run a scoping workshop with stakeholders (security, legal, data engineering, cloud FinOps) to finalize non-negotiables.
Issue an RFP that embeds these checklist questions and asks for evidence (ATO letters, SOC reports, model cards).
Run a time-boxed PoC with defined success metrics (cost per 1M inferences, latency P95, model drift alerting accuracy).
Request a red-team or pen-test summary and a security attestation during the PoC phase.
Negotiate firm contractual clauses: data residency, SLA credits, audit rights, escrow, and price protection for egress and compute costs.
Include a 90–180 day ramp-and-exit clause where you can leave with minimal penalty if critical controls are unmet.

Short case study — a concise, anonymized example

An enterprise financial services buyer in late 2025 shortlisted three AI vendors. One vendor claimed FedRAMP alignment but had no ATO; another had an ATO limited to non-sensitive workloads. The procurement team required either FedRAMP Moderate ATO or a hybrid deployment (private cloud for regulated workloads). The selected vendor provided a contractual commitment to a FedRAMP-ready roadmap, an escrow agreement for model artifacts, explicit egress caps for the first year, and a 120‑day exit trigger tied to SLA breaches. The result: predictable cost profile, provable compliance posture, and a defined escape path.

"Don’t buy the shiny features — buy the guarantees. In 2026, the difference between a successful AI deployment and a regulatory failure is in the contract and the audit trail."

30 essential vendor questions (quick checklist you can paste into an RFP)

Where are production and backup regions located? Provide a list of data centers and cloud providers.
Do you support region-restricted deployments? If yes, how is it enforced?
List all sub-processors and provide a notification period for changes.
Provide copies of FedRAMP ATO (if any), SOC 2 Type II, ISO 27001 certificates.
Do you handle regulated data (HIPAA, PCI)? Provide attestation and scope.
Provide a model card and dataset datasheet for each pre-built model proposed.
Describe your model lineage and versioning system — how can we audit model provenance?
Do you provide reproducible training artifacts (containers, seeds, environment specs)?
How do you detect and notify about model/data drift? What automated mitigations exist?
List supported encryption standards and whether BYOK is available.
Do you provide an SBOM and supply-chain security attestations?
Share recent pen-test summaries and remediation timelines.
Provide SLA metrics for uptime, inference latency (p95/p99), model availability, RTO/RPO.
What are the service credits and exit triggers for SLA breaches?
Detail pricing dimensions: inference, training, storage, logs, egress, monitoring.
Provide 12-month egress and storage pricing examples for a 10TB dataset and 1M monthly inferences.
Do you offer committed-use discounts or reserved capacity?
What is the change-control process for model updates to production?
How are software updates and patches managed for customer environments?
What audit rights do customers have? Provide scope and frequency constraints.
What incident notification windows do you commit to (detection, notification, remediation)?
Who owns trained models and derivative works? Provide sample IP clauses.
What export formats are supported for trained models and data (ONNX, torch, TF, CSV)?
Do you provide model-weights escrow? Define triggers and delivery timelines.
Describe transition assistance on contract termination (export, redeployment support).
Provide references for customers with similar compliance needs (public sector, finance, healthcare).
Do you support self-hosted or hybrid deployments? What components are required on-prem?
How do you bill for support and professional services? Provide SOW template examples.
Do you have a customer success SLA tied to onboarding timelines and performance metrics?
Provide an operational runbook for security incidents and a sample forensics report.

Final takeaways — what to do in the next 30 days

Run the weighted risk-scoring template across your shortlist and share results with legal and security.
Make data-residency and model provenance non-negotiable contract items for regulated workloads.
Build a 3-year TCO model (pilot, scale, peak) and negotiate caps on egress and unpredictable costs.
Require FedRAMP evidence where public-sector rules apply, and map vendor controls to NIST AI RMF requirements.
Include escrow and exit triggers to avoid vendor lock-in and to ensure continuity if the vendor fails to deliver.

Call to action

Use this checklist in your next RFP or procurement playbook. If you want a ready-made RFP addendum and scoring spreadsheet pre-populated with these questions, download our vendor-due-diligence template or contact our team for a short advisory session — we’ll run a vendor gap analysis and a 90-day remediation roadmap tailored to your compliance needs.

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