AI Vendor Due Diligence Scorecard (B2B): Security, Data Rights, Delivery Risk

Most B2B teams still evaluate AI vendors as if they were buying another SaaS tool: a demo, a few reference calls, a security questionnaire, and a procurement checklist. That approach fails with GenAI and model-centric platforms because the biggest risks are not “does it work,” but “what happens to our data, our obligations, and our delivery commitments after the contract is signed.”

AI vendor due diligence has become an operating model decision: it determines how quickly you can move from pilot to production, how defensible your customer commitments are, and how much hidden cost you absorb through usage volatility, change control, and integration drag. If you treat vendor selection as a procurement event, you’ll re-live it as an incident, an audit finding, or a stalled rollout.

What makes AI vendor risk different in B2B

Vendor risk in AI concentrates in three places that typical sourcing workflows underweight:

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Data rights and downstream use: what the vendor can do with your inputs, outputs, logs, embeddings, fine-tunes, and telemetry—across their own products and third parties.
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Security posture in a probabilistic system: AI changes the attack surface (prompt injection, data exfiltration via tool calls, insecure plugin chains, model inversion risks) and requires controls beyond standard SOC2 checkboxes.
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Delivery and change risk: model updates, feature deprecations, token pricing shifts, regional availability, and rate limits can break production behavior even when “uptime” looks fine.

This is why external vendor governance must connect to internal AI governance and your scale path. If you’re building approval controls and risk ownership internally, vendor selection must plug into the same decision rights and guardrails—not operate as a separate procurement track. (See related guidance on internal controls in our AI governance blueprint.)

A decision-grade due diligence flow (not a “questionnaire exercise”)

To avoid late-stage surprises, run due diligence as a gated evaluation with explicit owners. The goal is to make a go/no-go decision with quantified residual risk and a mitigation plan—before your team invests in irreversible integration work.

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Gate 0 (fit and scope): confirm the use cases, data classes involved, and required deployment model (SaaS, VPC, on-prem, hybrid). Decide what “production-ready” means upfront.
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Gate 1 (security and data rights): validate controls, data handling, and contract terms before building anything beyond a sandbox.
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Gate 2 (delivery readiness): test integration paths, observability, incident response, rate limits, and change control with a thin vertical slice.
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Gate 3 (commercial and operating model): finalize pricing, SLAs, support model, and the internal runbook (who monitors, who approves changes, who owns model performance).

If you’re already moving from pilots to production, treat vendor evaluation as part of that scaling operating model: controls, telemetry, and ownership are as important as model quality. The “pilot-to-production” gap is where vendor ambiguity turns into operational debt.

The AI vendor due diligence scorecard (what to score, how to decide)

Use a scorecard to keep decisions consistent across vendors and to create an audit-ready decision trail. Score each domain 1–5 (or Red/Amber/Green) and capture: evidence reviewed, owner sign-off, residual risk, and required mitigations. The point is not “perfect scores”; it’s transparent trade-offs.

1) Data rights, usage, and retention (highest leverage, most commonly missed)

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Training and improvement rights: are your inputs/outputs used to train or improve any model (yours, shared, or vendor-owned)? Are there opt-outs and are they default?
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Data classes and restrictions: explicit treatment of PII, customer data, regulated data, and confidential IP (including what cannot be sent, even transiently).
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Retention and deletion: log retention periods, deletion SLAs, and whether deletion includes derived artifacts (embeddings, fine-tuned weights, caches).
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Output ownership and reuse: who owns outputs; whether the vendor can use outputs for benchmarking, evaluation, or other customers’ improvements.
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Subprocessors and onward transfers: visibility into subprocessors, geographic processing locations, and controls for approval/notification on changes.
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Data residency: whether residency is enforceable in practice (not only “best effort”), including backups, telemetry, and support access.

Execution note: involve data governance early so your evaluation reflects how data is actually classified, accessed, and traced in your environment. If your lineage and access controls are weak, even the best contract won’t prevent accidental exposure.

2) Security controls that map to AI-specific threats

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Tenant isolation and secrets handling: how API keys, connectors, and tool credentials are stored and rotated; whether vendors support customer-managed keys where needed.
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Prompt injection and tool-use controls: guardrails for tool calls, allowlists, parameter validation, and prevention of data exfiltration through connected systems.
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Logging and redaction: ability to control what is logged, redact sensitive content, and segregate logs by environment.
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Vulnerability management and pentest scope: whether the vendor tests model-adjacent surfaces (plugins, agent frameworks, connectors), not just the web app.
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Access control and auditability: granular roles, audit logs, and administrative actions traceability—especially for “human-in-the-loop” review features.
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Incident response: breach notification timelines, shared responsibility boundaries, and evidence you can use in customer and regulator communications.

3) Delivery readiness: integration, reliability, and operational support

Many AI rollouts fail not because model quality is poor, but because the vendor’s delivery model doesn’t match your production realities: identity, networking, observability, release management, and on-call expectations. “API available” is not a delivery plan.

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Integration paths: SSO/IAM compatibility, network requirements, private connectivity options, and how data is passed to/from your systems.
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Non-functional requirements: latency, throughput, rate limits, concurrency caps, and whether limits vary by region or tier.
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Observability: metrics and logs available, tracing for tool calls, and the ability to correlate failures to specific model versions/config.
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Support model: escalation paths, response times by severity, and whether support can access your data (and under what approvals).
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Runbooks: documented failure modes and recommended mitigations (fallbacks, retries, circuit breakers, cached responses).

4) Model lifecycle and change control (the hidden “production breaker”)

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Versioning guarantees: ability to pin model versions; deprecation policies; notice periods; and rollback support.
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Evaluation and release notes: transparency of changes that affect behavior, safety, or outputs (not just uptime).
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Customer change windows: whether updates can be deferred, tested, or staged per environment.
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Regression testing support: tooling or guidance to run your own regression suite (prompts, expected outputs, tool chains).
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Safety tuning drift: how safety layers are updated and how that affects your use case (e.g., refusals, policy changes).

5) Commercials and cost governance (prevent the “success tax”)

AI economics are non-linear: usage grows quickly when users trust the system, and costs can spike due to retries, longer contexts, multi-step tool calls, or model upgrades. Due diligence must validate whether the vendor enables cost control at runtime, not only in the contract.

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Pricing clarity: predictable pricing units, clear definitions (tokens, tool calls, storage, fine-tuning), and transparent overage mechanics.
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Budget controls: caps, throttling, per-project cost attribution, and alerting thresholds.
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Efficiency levers: support for caching, context management, smaller models where appropriate, and routing strategies (if relevant).
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Commercial protections: price change notice periods, committed spend flexibility, and exit terms that don’t penalize responsible de-risking.

6) Compliance alignment and customer obligations

For B2B, the most painful “compliance” moments often come from customer security reviews and contractual flow-downs. Your vendor must support your ability to answer these questions consistently and back them with evidence.

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Security documentation availability: policies, audit reports, pen test summaries, and data processing documentation accessible under NDA as needed.
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Contractual flow-downs: ability to meet your customer obligations on breach notification, data handling, and support access.
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Geographic and sector constraints: support for regulated environments, residency requirements, and sector-specific expectations (where applicable).
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Human review and accountability: if human-in-the-loop exists, whether it is controlled, auditable, and consistent with confidentiality obligations.

How to interpret the scorecard: make trade-offs explicit

A useful scorecard drives decisions, not documentation. A simple pattern that works in executive reviews:

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Set non-negotiables: e.g., no training on customer data, enforceable deletion, minimum incident notification, and ability to pin versions for critical workflows.
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Accept bounded risk intentionally: e.g., accept higher latency for better residency controls, or accept limited residency for low-sensitivity use cases with strict redaction.
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Tie mitigations to owners and timelines: technical controls (redaction, routing, isolation), contractual amendments, and operating controls (approval gates, monitoring).
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Document residual risk: what remains, why it’s acceptable, and when it will be revisited (e.g., at renewal, after vendor attestation updates).

Common failure patterns (and how due diligence prevents them)

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Legal negotiates “no training,” but engineering enables telemetry that still retains sensitive prompts longer than intended.
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Security approves a vendor based on generic attestations, but tool-use and connector risks are untested, leading to data leakage paths.
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A pilot succeeds, then production fails under rate limits, concurrency caps, or region-specific availability constraints.
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Finance signs off on unit pricing, but no one budgets for retries, multi-step agents, or long-context prompts—cost overruns become a product decision.
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A vendor changes a model or safety layer, output behavior shifts, and customer-facing workflows degrade with no rollback.

The practical fix is not “more meetings.” It’s an integrated evaluation that ties procurement and legal terms to the technical reality of how data moves and how the system is operated. Vendor selection should be a portfolio decision with clear accountability for value and risk.

What to ask for as evidence (so you’re not grading on promises)

A mature diligence process is evidence-driven. Typical artifacts that reduce ambiguity:

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Data flow diagrams and a written data processing description that matches your intended architecture (including logs, caches, embeddings, and support access).
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Security documentation and audit reports appropriate to your risk level (plus clear scope statements).
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A model/platform change policy: versioning, notice, deprecation, and customer controls.
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SLAs and support terms aligned to your critical workflows (not generic uptime).
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A production “thin slice” test plan: rate-limit tests, failure injection, observability validation, and rollback drills.

Where this fits: from vendor selection to production outcomes

If you’re choosing between LLM platforms, model providers, or integrators, the winning approach is to treat due diligence as a production readiness activity: contract terms, architecture, and operating controls are one system. Done well, it compresses time-to-value while reducing security exposure and commercial surprises.

If you need support running a vendor scorecard, validating architecture, or setting up the evaluation flow across procurement, security, and engineering, our team can help through our AI Solutions practice.

For procurement- or security-led evaluations, we can also facilitate a focused due diligence workshop to align stakeholders, identify non-negotiables, and produce an evidence-backed risk position for executive approval.

AI Vendor Due Diligence for B2B: A Practical Scorecard for Security, Data Rights, and Delivery Risk