Most security frameworks weren’t built for AI. SOC 2 doesn’t tell you if an AI solution hallucinates. ISO 27001 doesn’t tell you if a model update quietly changes how your system responds to customers.

This checklist gives security, compliance, and privacy leaders a structured, vendor-agnostic framework to evaluate any AI tool in your CX stack before it touches your customers or your data.

The eight domains that follow cover what traditional frameworks miss: from hallucination risk and auditability to model drift and long-term governance. Use it before the next AI tool makes it onto your shortlist.

Vendor self-assessment

Score your AI vendor

Work through each domain and check every question the vendor can prove with evidence. You’ll get a scorecard showing exactly where the gaps are.

8domains

23questions

5high priority

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At the end, download Inbenta’s AI Compliance & Security overview.

Security Certifications & Audit Posture

Can the vendor prove their security claims with independent validation?

Check all that apply.

Does the vendor hold appropriate certifications for your industry (such as ISO 27001, ISO 27017, SOC 2, or equivalent)?Are independent audit reports available, performed at least annually?Can the vendor report incidents in a timely way?

Data Privacy & Regulatory Compliance

Does the vendor handle personal data lawfully and keep it where you need it?

Check all that apply.

Does the solution comply with applicable data protection regulations (such as GDPR, CCPA/CPRA, NIS2 & DORA, EU AI Act or sector-specific requirements)?Is customer data processed solely on your behalf? Does the vendor ensure it is not used to train or fine-tune underlying LLMs?Can the platform enforce data residency requirements — ensuring data stays within a specified geographic region? Can the vendor host in the region you require (when applicable)?

Governance, Transparency & Ongoing Oversight

Does the vendor give you the tools to maintain oversight long after go-live?

Check all that apply.

Does the platform provide real-time analytics, exportable audit logs, and a published security policy or trust center?Are AI system updates communicated, with advance notification and an option to review before deployment?High priorityDoes the platform provide a governed knowledge layer where every deployed intent is linked to its source content, giving your compliance team a system of record for why the AI said what it said?

AI-Specific Risk: Accuracy & Output Control

Is the AI explainable and auditable — or a black box?

Check all that apply.

Does the AI use proprietary, structured reasoning (e.g., knowledge graphs, semantic search) to constrain outputs — or is it a pure LLM generating responses at runtime?Does the platform offer guardrails that prevent off-topic, non-compliant, or inappropriate responses?High priorityIs every AI response traceable to a specific, pre-approved source and intent, so a compliance officer can examine why the AI said what it said?High priorityDoes the system separate LLM intent recognition from response delivery, returning only pre-approved, governed content with no runtime content generation?

Vendor Risk & Third-Party Data Handling

You’re accountable for your vendor’s data practices. Can you verify them?

Check all that apply.

Does the vendor have a documented sub-processor evaluation process with NDAs and confidentiality commitments?Upon contract termination, is there a documented return and deletion process?

Access Control, Authentication & Encryption

Has the vendor secured every access point their platform introduces?

Check all that apply.

Does the platform support SSO, MFA, and role-based access control (RBAC)?Is data encrypted at rest and in transit using industry-standard protocols (such as AES-256 and TLS 1.2+)?Are API endpoints secured with token-based authentication and rate limiting?

Prompt Injection & Adversarial AI Threats

Has the vendor prepared for attacks specific to AI systems?

Check all that apply.

Has the vendor assessed and documented prompt injection risks for their platform?High priorityDoes the architecture inherently limit prompt injection by separating intent recognition from response delivery so the system can only return pre-approved responses, even if the conversational layer is manipulated?

Model Drift, Durability & Long-Term Risk

Is the vendor protecting you from risks most evaluations never consider?

Check all that apply.

Is the platform model-agnostic, capable of transitioning between LLMs without downtime or re-deployment?What is the vendor’s documented strategy for protecting against model deprecation, vendor lock-in, and LLM obsolescence?High priorityDoes the architecture decouple response accuracy from any single model’s behavior — insulating you from model drift when providers update or replace models?

Check each question the vendor can answer with evidence. Leave it unchecked if they can’t, or if you don’t know yet.

Criteria met

High-priority gaps

Score by domain

Questions to raise with the vendor

How Inbenta measures up

Independently audited and anchored by ISO/IEC 27001, ISO/IEC 27017 and ISO/IEC 27701, with all three ISO certifications audited annually. Hosted on AWS (SOC 2 Type II certified — attestation available on request).
Replies only with pre-approved, governed answers, each traceable to the exact intent it came from, which also protects against prompt injection.
Model-agnostic, so accuracy is anchored to governed intents rather than to any one model’s behavior.
Customer data is not used to train LLMs, with hosting in North America, Europe, and Asia.
Full interaction-level audit trail, a real-time unified dashboard, and documentation at trust.inbenta.com.

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*This is a self-assessment based on the CX AI Security & Compliance Checklist and your answers. It is not a certification or audit of any vendor. Verify each answer against the vendor’s documentation.

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