A Detailed AI Security Questionnaire for The Voice AI Partner Litmus Test

The integration of Voice AI is a strategic imperative. From revolutionizing customer service with conversational IVRs to enhancing clinical documentation in healthcare, Voice AI partners promise unprecedented efficiency and insight. But as this technology becomes more deeply embedded in our critical workflows, it opens a new and complex frontier of security risks that traditional vendor due diligence processes are woefully ill-equipped to handle.

Your standard vendor questionnaire, built for the predictable world of SaaS platforms, simply doesn’t ask the right questions. It fails to probe the unique vulnerabilities of machine learning models, the ethical complexities of training data, and the potential for sophisticated, AI-specific attacks. To truly trust an AI vendor, you need more than a checklist; you need a litmus test.

That is where a purpose-built AI security questionnaire becomes your single most critical tool. This guide provides a detailed framework and questions designed to expose the security posture, ethical grounding, and operational resilience of any potential Voice AI partner. It’s not just a list of questions; it’s a methodology for understanding and mitigating a new class of risk.

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Why Your Standard Vendor Questionnaire Fails for AI

Traditional software security focuses on a well-understood set of vulnerabilities, including code injection, insecure APIs, and improper access control. While these are still relevant, an AI vendor introduces three new, interconnected layers of risk: the data, the model, and the AI-specific infrastructure. Your old questions don’t even scratch the surface.

Consider the differences:

Risk Category	Traditional Software (e.g., a CRM)	Voice AI Platform
Primary Asset	Application Code & Structured Data	The Model & The Training Data
Key Vulnerability	Code bugs, configuration errors.	Data poisoning, model evasion, privacy leaks via inference.
Attack Vector	SQL Injection, Cross-Site Scripting.	Adversarial audio inputs, model inversion attacks, membership inference.
Source of “Truth”	Deterministic code logic.	Probabilistic, often “black box” model predictions.
Compliance Focus	Data storage and access (GDPR, HIPAA).	Data provenance, bias, explainability, and data handling during training.

Your procurement process needs an upgrade. Using a generic vendor questionnaire for an AI system is like using a car inspection checklist for a rocket ship. The core principles of safety are present, but you’re missing the most critical and high-risk components. This specialized AI vendor questionnaire is designed to inspect the rocket ship.

The Core Pillars of a Robust AI Security Questionnaire

To be effective, your evaluation must be structured. We’ve organized our questionnaire into six core pillars, each representing a critical facet of a trustworthy AI system.

1. Data Governance & Privacy. AI is fueled by data. This pillar scrutinizes how a vendor collects, manages, protects, and ensures the privacy of the data used to train and operate their models, especially sensitive voice data.
2. Model Development & Lifecycle Security (MLSecOps). This pillar examines the security practices embedded within the model’s creation, from data sourcing and feature engineering to training, validation, and deployment.
3. Infrastructure & Operational Security. This encompasses the foundational yet crucial aspects of where AI models and data reside. It assesses the vendor’s cloud or on-premise security, access controls, and network architecture.
4. Adversarial Resilience & Model Robustness. This is the AI-specific stress test. It examines how the model withstands attacks designed to deceive, manipulate, or extract information from it.
5. Ethical AI, Fairness & Explainability. A model can be secure but biased or untrustworthy. This pillar investigates how the AI vendor addresses potential biases, ensures fairness, and provides transparency into the model’s decision-making process.
6. Governance, Compliance & Incident Response. This pillar assesses the vendor’s overarching security program, their adherence to regulatory standards (such as GDPR, HIPAA, or the EU AI Act), and their readiness to respond to security incidents.

The Detailed AI Security Questionnaire: Key Questions to Ask

Here is the detailed list of questions, broken down by pillar. For each question, we’ve included a “What to Look For” section to help you interpret the answers and identify red flags. This is one of the most important parts of using an AI security questionnaire.

Pillar 1. Data Governance & Privacy

That is ground zero. Voice data is often deeply personal and may contain Personally Identifiable Information (PII) or Protected Health Information (PHI). A breach here is catastrophic.

Question 1: Describe the complete lineage and provenance of the data used to train your core voice models. Was this data ethically sourced and with proper consent?

• What to Look For: A clear, auditable trail. Vague answers, such as “publicly available data,” are a red flag. Look for specifics, like the datasets used, licensing agreements, and consent mechanisms. A trustworthy AI vendor will have meticulous records.

Question 2: How do you segregate our data from that of other customers, both during model training (if applicable) and during inference/operation?

• What to Look For: Strong multi-tenancy architecture. Look for terms like “logical and physical segregation,” “data sharding by customer ID,” and “separate cryptographic keys.” A flat data lake for all clients is a major risk.

Question 3: What specific techniques are used to de-identify or anonymize PII/PHI in voice data before it is used for training or analytics?

• What to Look For: Details on techniques like PII redaction, data masking, or tokenization. Ask if these processes are automated and what their accuracy rate is. A manual process is prone to error.

Question 4: Describe your data retention and destruction policies for our specific data, including raw audio, transcripts, and any derived metadata.

• What to Look For: Clear, configurable timelines. The policy should be contractually enforceable. They should be able to prove data destruction (e.g., cryptographic erasure).

Question 5: How do you comply with data residency requirements (e.g., GDPR, LGPD)? Can we specify the geographic region for data storage and processing?

• What to Look For: Affirmative “yes” and details on their cloud infrastructure (e.g., “We leverage AWS regions in Frankfurt and Dublin for EU clients”). Inability to guarantee residency is a non-starter for many regulated industries.

Question 6: Who has access to our raw and processed data? Detail the roles and the access control policies (e.g., RBAC) in place.

• What to Look For: The principle of least privilege. Access should be restricted to a small number of vetted engineers for specific, logged, and approved purposes. Generic admin access is a red flag.

Question 7: Do you use our data to train or fine-tune your general models for other customers? If so, is this an opt-in or opt-out process?

• What to Look For: This should always be an explicit, opt-in choice with clear contractual language defining the terms. Automatic opt-in is a significant risk to data privacy and competitive advantage.

Pillar 2. Model Development & Lifecycle Security (MLSecOps)

This section of the AI vendor questionnaire examines how security is integrated into the model itself, rather than being added as an afterthought.

Question 8: Describe your Secure Development Lifecycle (SDL) for machine learning models. How does it differ from a traditional software SDL?

• What to Look For: A mature answer will mention stages such as threat modeling for AI-specific attacks, data validation, secure data pipelines, model versioning, and continuous monitoring for drift and degradation.

Question 9: What tools and processes do you use to scan and manage vulnerabilities in open-source libraries and dependencies (e.g., TensorFlow, PyTorch, Hugging Face transformers) used in your AI stack?

• What to Look For: Specific tool names (Snyk, Dependabot, etc.) and processes for patching. The open-source AI ecosystem moves fast, and unpatched libraries are a major entry point for attackers.

Question 10: How do you version control your models, datasets, and feature extraction code to ensure reproducibility and rollback capabilities?

• What to Look For: They should describe using tools like DVC (Data Version Control) or MLflow alongside Git. That is crucial for auditing and for recovering from a compromised model update.

Question 11: What quality assurance and testing processes are in place to detect data or concept drift in your models post-deployment?

• What to Look For: A proactive monitoring strategy. Look for terms like “automated performance monitoring,” “statistical process control,” and “retraining triggers.” A model’s accuracy can degrade silently, creating operational and security risks.

Question 12: How do you protect the integrity of your data pipelines against data poisoning attacks, where an attacker intentionally corrupts training data?

• What to Look For: Data validation checks, anomaly detection, and outlier analysis within the data ingestion pipeline. They should be able to identify and quarantine suspicious data before it corrupts the model.

Question 13: Are your data scientists and ML engineers trained in secure coding practices and AI-specific security threats?

• What to Look For: Evidence of a formal training program. That shows a culture of security. Ask about topics covered, like adversarial ML and privacy-preserving techniques.

Pillar 3. Infrastructure & Operational Security

This is where the rubber meets the road—the environment where the AI runs. These questions to ask when evaluating procurement software are foundational.

Question 14: Provide a high-level network diagram of your production environment where our data and your models are hosted.

• What to Look For: A well-architected design showing VPCs/VNETs, public/private subnets, firewalls, load balancers, and IDS/IPS. A flat, simple network is a sign of immaturity.

Question 15: Describe your encryption standards for data at rest and in transit. Specify the algorithms and key management protocols used.

• What to Look For: Strong, modern standards. At rest: AES-256. In transit: TLS 1.2 or higher. Key management should involve a reputable Key Management Service (KMS), such as AWS KMS or Azure Key Vault, with key rotation policies.

Question 16: What are your logging and monitoring capabilities? What events are logged, how long are logs retained, and how are they protected from tampering?

• What to Look For: Comprehensive logging (API calls, access attempts, configuration changes) fed into a SIEM system. Logs should be retained for a contractually agreed period (e.g., 1 year) in immutable storage.

Question 17: Have you completed a third-party security audit or penetration test in the last 12 months? Could you please share the summary report or the attestation letter?

• What to Look For: A clean report (or one with mitigated findings) from a reputable firm. Certifications like SOC 2 Type II or ISO 27001 are strong positive signals. Hesitation to share is a major red flag.

Question 18: How do you manage secrets (API keys, credentials, certificates) within your environment?

• What to Look For: Use of a dedicated secrets management tool (e.g., HashiCorp Vault, AWS Secrets Manager). Hard-coding secrets in code or config files is a critical vulnerability.

Question 19: Describe your change management process for deploying new model versions or infrastructure updates.

• What to Look For: A formal process involving peer review, automated testing, staging environments, and an approval workflow. Uncontrolled changes are a primary source of outages and vulnerabilities.

Question 20: What Disaster Recovery (DR) and Business Continuity (BC) plans are in place for the AI service? What are your RTO (Recovery Time Objective) and RPO (Recovery Point Objective)?

• What to Look For: Clear, tested DR plans with specified RTO/RPO values that meet your business requirements. Look for multi-region or multi-AZ deployments.

Pillar 4. Adversarial Resilience & Model Robustness

This is the core of AI-specific security. It assesses the model’s ability to withstand deliberate attacks. This part of the AI security questionnaire requires knowledgeable staff to evaluate.

Question 21: What measures have you taken to protect your models against evasion attacks, where an attacker makes small perturbations to an input (e.g., audio noise) to cause a misclassification?

• What to Look For: They should describe techniques like adversarial training (training the model on attacked samples), input sanitization, and defensive distillation.

Question 22: How do you defend against model inversion or model stealing attacks, where an attacker attempts to reconstruct the training data or the model itself via API queries?

• What to Look For: API rate limiting, query monitoring for unusual patterns, and techniques that return less precise information (e.g., returning only the top prediction instead of a full probability distribution).

Question 23: Have you performed specific red-teaming exercises against your AI models? If so, what types of attacks were simulated?

• What to Look For: A proactive approach to security. They should be able to discuss simulating attacks from frameworks like MITRE ATLAS. That shows a mature understanding of the threat landscape.

Question 24: How do you protect against membership inference attacks, where an attacker tries to determine if a specific individual’s data was used in the model’s training set?

• What to Look For: Knowledge of privacy-preserving ML techniques like Differential Privacy. That is especially critical if the model was trained on sensitive data.

Question 25: For models that can be fine-tuned, how do you prevent prompt injection or manipulation that could jailbreak the model’s safety constraints?

• What to Look For: Input validation, sanitization of user prompts, context-aware firewalls, and strict separation of instructions from user data. That is crucial for LLM-based voice agents.

Question 26: How does the system behave when presented with out-of-distribution or nonsensical inputs? Does it fail gracefully or produce unpredictable results?

• What to Look For: The model should have a high confidence threshold for acting and a robust fallback mechanism in place. It should be able to say “I don’t know” or escalate to a human rather than guessing.

Pillar 5. Ethical AI, Fairness & Explainability

A secure system that is biased or opaque is not trustworthy. This pillar is crucial for adoption, compliance, and maintaining a strong brand reputation.

Question 27: What steps do you take to measure and mitigate demographic bias (e.g., based on accent, dialect, gender, age) in your voice recognition and language models?

• What to Look For: A detailed description of their process. That includes using diverse and representative training datasets, and employing fairness metrics (e.g., equal opportunity, demographic parity) to audit model performance across subgroups.

Question 28: Can your system provide an explanation or rationale for its critical decisions or classifications? What level of explainability (XAI) do you support?

• What to Look For: An understanding of XAI techniques like SHAP or LIME. The answer will depend on the model type, but they should be able to provide some level of insight, even if it’s just feature importance, rather than simply stating “it’s a black box.”

Question 29: Is there a human-in-the-loop process for reviewing or overriding high-stakes AI-driven decisions?

• What to Look For: A well-defined workflow for escalation. For critical applications (like healthcare or finance), a fully autonomous system is often too risky.

Question 30: How do you govern the use of your AI to ensure it is not used for unintended, malicious, or unethical purposes by your customers?

• What to Look For: A clear Acceptable Use Policy (AUP). They should have monitoring in place to detect potential misuse (e.g., use for mass surveillance or fraudulent activities).

Question 31: Do you have an internal AI ethics review board or committee? What is its mandate and authority?

• What to Look For: A formal governance structure. That demonstrates a corporate commitment to responsible AI that goes beyond marketing claims.

Pillar 6. Governance, Compliance & Incident Response

This pillar ties everything together, ensuring formal processes and accountability are in place. This is a critical section of any vendor questionnaire.

Question 32: List the information security and data privacy regulations and standards with which your organization complies (e.g., ISO 27001, SOC 2, HIPAA, GDPR, CCPA).

• What to Look For: A specific list. Ask for copies of certifications or audit reports. Compliance demonstrates external validation of their security program.

Question 33: Do you have a documented Incident Response (IR) plan? Describe the key phases of the plan.

• What to Look For: A plan that follows a standard framework (e.g., NIST’s Preparation, Detection & Analysis, Containment, Eradication & Recovery).

Question 34: What is your defined process and timeline for notifying us in the event of a security breach affecting our data?

• What to Look For: A clear, contractually obligated Service Level Agreement (SLA) for breach notification. This should be a matter of hours, not days or weeks.

Question 35: Do you carry cybersecurity insurance? If so, what is the coverage amount?

• What to Look For: A significant level of coverage. This provides a financial backstop in a worst-case scenario and indicates the vendor takes risk management seriously.

Question 36: How do you manage and track AI-specific risks, in line with frameworks like the NIST AI Risk Management Framework?

• What to Look For: A formal risk register and management process that specifically addresses AI risks like bias, data poisoning, and evasion, not just traditional IT risks.

Question 37: Do you provide customers with audit logs or API access to monitor the security and usage of the services they consume?

• What to Look For: The ability for you to monitor what’s happening with your data and service. Lack of transparency is a red flag.

Conclusion: Trust, but Verify with the Right Questions

Partnering with a Voice AI vendor can unlock transformative value, but it also means entrusting them with your data, your reputation, and your operational stability. The unique nature of AI technology demands a new level of scrutiny — one that goes far beyond the scope of traditional IT vendor assessments.

This AI security questionnaire provides the framework for that scrutiny. It empowers you to move past marketing promises and dig into the real substance of a vendor’s security posture, ethical commitments, and technical resilience. By asking these targeted questions, you transform your procurement process from a leap of faith into a data-driven decision. In the age of AI, you can’t afford to do it any other way.

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