Data as of Sep 18, 2026 · Based on 50 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
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Source | Citation share |
|---|---|
The AI Vendor Security Questionnaire: 38 Questions ...https://www.deepinspect.ai/blog/ai-vendor-security-questionnaire | 2.5% |
 How do you actually assess AI vendor risk? : r/procurementhttps://www.reddit.com/r/procurement/comments/1r3kbj9/how_do_you_actually_assess_ai_vendor_risk/ | 2.5% |
 Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profilehttps://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf | 1.8% |
 Enterprise AI Vendor RFP: 40 Questions to Ask (2026) - worqlohttps://worqlo.com/blog/enterprise-ai-vendor-rfp-questions/ | 1.8% |
AI Procurement Regulations Every Government Contractor ...https://gsconsultingllc.com/insights/ai-procurement-regulations-government-contractors | 1.5% |
How to Answer the AI Governance Section of a Security Questionnairehttps://www.aetos-data.com/answers-insights/enterprise-security-ai-questionnaires | 1.5% |
 How to Evaluate AI Vendors: 5 Key categories for AI Adoptionhttps://www.darktrace.com/blog/how-to-evaluate-ai-vendors-5-key-categories-for-ai-adoption | 1.3% |
The AI Vendor Security Questionnaire: 38 Questions Procurement Should Actually Ask | DeepInspecthttps://www.deepinspect.ai/blog/ai-vendor-security-questionnaire | 1.3% |
 Your AI Vendor Security Questionnaire Is Asking the Wrong Questionshttps://www.swept.ai/post/security-questionnaires-ai-vendors-what-to-ask | 1.3% |
 Evaluating AI Vendors: The Red Flags, Questions, and ...https://www.youtube.com/watch | 1.3% |
To successfully clear InfoSec, legal, and procurement , enterprise AI evaluations must break away from generic feature comparisons and focus on architectural control categories. Look for vendors structured around these core evaluation…
To successfully clear InfoSec, legal, and procurement , enterprise AI evaluations must break away from generic feature comparisons and focus on architectural control categories. Look for vendors structured around these core evaluation buckets:
If you'd like to narrow this down, let me know:
For a procurement-ready AI vendor evaluation, I’d organize the market into **capability categories** first, then apply a common **enterprise security and governance gate** across all of them. Enterprise reviews commonly focus on evidence such as SOC 2/ISO attestations, SSO/SCIM, RBAC, audit logs, encryption, data…
For a procurement-ready AI vendor evaluation, I’d organize the market into capability categories first, then apply a common enterprise security and governance gate across all of them. Enterprise reviews commonly focus on evidence such as SOC 2/ISO attestations, SSO/SCIM, RBAC, audit logs, encryption, data retention, subprocessors, incident response, and contractual controls.
Evaluate vendors that provide:
Security packet requirements:
Examples of use cases:
Evaluate:
For systems that can take actions:
Add controls for:
Evaluate:
Security focus:
Some enterprise AI coding vendors package items such as SOC 2 materials, security questionnaires, SSO/SCIM, RBAC, audit logs, and data-retention controls specifically for procurement review.
Examples:
Additional checks:
Includes:
Evaluate:
These help govern AI usage across the enterprise:
Look for:
Require every vendor to provide:
A practical shortlist process is usually:
This structure lets you compare very different AI vendors using the same procurement framework rather than evaluating only model capability.
For an enterprise procurement process, evaluate AI vendors by **capability category + risk domain**, not just by product type. A vendor that looks like “a chatbot” may actually involve a model provider, data processor, identity layer, and workflow automation platform. A strong review packet should cover security,…
For an enterprise procurement process, evaluate AI vendors by capability category + risk domain, not just by product type. A vendor that looks like “a chatbot” may actually involve a model provider, data processor, identity layer, and workflow automation platform. A strong review packet should cover security, privacy, AI governance, architecture, and operational controls.
Examples: large language models, multimodal models, embedding models, speech/image models.
Evaluate:
Security packet requests:
VettedAIAgents.com AI Shared Responsibility## 2. Enterprise AI assistants and knowledge copilots
Examples: internal search, employee assistants, document Q&A, productivity copilots.
Evaluate:
Security packet requests:
Examples: autonomous task execution, business process automation, AI workers.
Evaluate:
Security packet requests:
Agentic systems typically require deeper review because they can take actions, not just generate responses.
Examples: cloud AI platforms, model hosting, MLOps platforms.
Evaluate:
Security packet requests:
AI Shared Responsibility## 5. AI-enabled business applications
Examples: CRM, HR, legal, finance, customer support, analytics platforms with embedded AI.
Evaluate:
Security packet requests:
Examples:
Evaluate:
Additional evidence:
AI Agent Rank## 7. AI security, governance, and monitoring vendors
Evaluate:
Security packet requests:
Evaluate:
Security packet requests:
A practical procurement scorecard usually includes:
| Category | Key questions |
|---|---|
| Security | SOC 2, ISO 27001, encryption, IAM, vulnerability management |
| Privacy | Data use, retention, residency, deletion, DPA |
| AI governance | Model controls, evaluations, transparency, responsible AI |
| Architecture | Data flows, integrations, isolation, dependencies |
| Operations | SLA, uptime, support, incident response |
| Compliance | Industry regulations and audit evidence |
| Contracting | Liability, IP, indemnity, termination/export |
| Deployment | Pilot controls, rollout, monitoring, exit plan |
SOC 2 Type II and ISO 27001 are common baseline expectations, but they should be treated as starting points rather than complete AI risk assessments.
A procurement-ready vendor package should ideally include:
This structure lets procurement, security, legal, privacy, and business owners evaluate the same vendor consistently.
SOC 2 Type II and ISO 27001 are common baseline expectations, but they should be treated as starting points rather than complete AI risk assessments.
A procurement-ready vendor package should ideally include:
When sourcing procurement-ready AI vendors with solid security review packets, you should structure your evaluation across distinct functional categories. This ensures every layer—from data privacy to model behavior—meets enterprise risk thresholds.[](https://nhimg.org/glossary/enterprise-ai-procurement/)…
When sourcing procurement-ready AI vendors with solid security review packets, you should structure your evaluation across distinct functional categories. This ensures every layer—from data privacy to model behavior—meets enterprise risk thresholds.
The primary categories to evaluate include:
To build a resilient enterprise AI stack with procurement-ready vendors, your evaluation matrix should span `six core categories` . Each category directly addresses the security review packets, compliance artifacts, and operational controls required by legal, security, and procurement…
To build a resilient enterprise AI stack with procurement-ready vendors, your evaluation matrix should span six core categories . Each category directly addresses the security review packets, compliance artifacts, and operational controls required by legal, security, and procurement teams.
If you'd like, let me know:
I can tailor this evaluation framework with specific compliance questions or red flags tailored to your sector.
For a **procurement-ready AI vendor review**, I’d evaluate vendors across these categories. This goes beyond a generic SaaS questionnaire: NIST specifically recommends updating acquisition/vendor due diligence for AI to cover privacy, security, IP, value-chain risk, ongoing monitoring, and third-party AI controls.…
For a procurement-ready AI vendor review, I’d evaluate vendors across these categories. This goes beyond a generic SaaS questionnaire: NIST specifically recommends updating acquisition/vendor due diligence for AI to cover privacy, security, IP, value-chain risk, ongoing monitoring, and third-party AI controls.
| Category | What to evaluate | Evidence to request |
|---|---|---|
| 1. Corporate security & compliance | SOC 2 Type II, ISO 27001, penetration testing, vulnerability management, security program maturity | SOC 2 report, ISO certificate, pen-test summary, security whitepaper |
| 2. Data protection & privacy | What happens to prompts, files, outputs, embeddings, telemetry; encryption; retention/deletion; data residency | DPA, data-flow diagram, retention policy, subprocessor list |
| 3. AI/model governance | Models used, model provenance, training-data practices, model changes, fine-tuning, third-party model providers | Model inventory, AI governance policy, model cards, change-management process |
| 4. Identity & access control | SSO/SAML/OIDC, SCIM, RBAC, least privilege, service accounts, admin separation | Enterprise feature matrix, IAM architecture, configuration documentation |
| 5. Tenant & infrastructure isolation | Logical/physical isolation, dedicated environments, VPC/private deployment options, key management | Architecture diagram, isolation controls, encryption/key-management documentation |
| 6. Auditability & monitoring | Immutable audit logs, admin actions, prompts/outputs where appropriate, tool calls, approvals, exports, API access | Sample audit logs, SIEM/API documentation, retention settings |
| 7. AI-specific security | Prompt injection, data leakage, jailbreaks, malicious files, unsafe tool use, model abuse | Threat model, red-team results, security testing methodology, mitigations |
| 8. Agent/tool permissions | What the AI can read/write/do, delegated identity, approval gates, sandboxing, rollback and kill switches | Permission model, tool inventory, workflow diagrams, control demonstrations |
| 9. Reliability & operational resilience | SLA, uptime, disaster recovery, RTO/RPO, capacity limits, dependency failures, model/provider outages | SLA, BCP/DR documentation, status history, recovery-test evidence |
| 10. AI quality & evaluation | Accuracy, hallucination rates, domain-specific evals, regression testing, monitoring for drift | Evaluation methodology/results, benchmark data, release gates |
| 11. Legal, IP & regulatory | Ownership of inputs/outputs, training rights, indemnification, applicable AI regulations, liability allocation | MSA/DPA, AI-specific terms, indemnification language, regulatory responsibility matrix |
| 12. Incident response | AI-specific incident definitions, notification timelines, containment, forensics, customer communication | IR plan, notification commitments, recent exercise/results |
| 13. Supply-chain & subprocessors | Model providers, cloud providers, data processors, open-source dependencies, geographic processing | Current subprocessor list, dependency/model-provider map, change notification terms |
| 14. Governance & human oversight | High-risk use-case restrictions, approval workflows, human review, acceptable-use controls, accountability | Responsible-AI policy, use-case restrictions, governance documentation |
| 15. Commercial & exit risk | Pricing predictability, usage limits, portability, deletion on termination, escrow/continuity, vendor lock-in | Pricing schedule, exit/deletion procedure, data-export specification |
For each shortlisted vendor, I'd require a standardized security review packet containing at minimum:
Don't let “SOC 2 compliant” become the pass/fail criterion. SOC 2 addresses important controls, but it doesn't by itself answer whether your prompts are used for training, what model/subprocessors receive them, what an agent can do with your systems, or whether AI-specific attacks are tested. Current procurement guidance similarly emphasizes those AI-layer questions.
For agentic AI, I'd make tool authority, delegated identity, approval gates, action-level auditability, and rollback hard gates, rather than merely scoring criteria. An agent that can modify records or trigger external actions has a materially different risk profile from a read-only chatbot.
A useful structure is Pass / Conditional / Fail / Evidence Missing for each control, with separate Security, Privacy, Legal, AI Governance, IT, and Business Owner sign-offs. NIST's GenAI profile explicitly recommends use-case-based supplier risk assessment and maintaining approved AI-provider lists.
If you're building an actual procurement program, I can also turn this into a 100-point AI vendor scorecard with weighted categories, hard-stop criteria, and the exact questionnaire questions/evidence requests.
For a procurement-ready AI vendor evaluation, I’d organize the market into **capability categories** rather than vendor names. Each category should have its own security review packet requirements, because the risks differ between a chatbot SaaS, an AI agent platform, and an AI security control layer. [SC…
For a procurement-ready AI vendor evaluation, I’d organize the market into capability categories rather than vendor names. Each category should have its own security review packet requirements, because the risks differ between a chatbot SaaS, an AI agent platform, and an AI security control layer.
Examples: workplace copilots, knowledge assistants, meeting assistants, writing assistants.
Evaluate:
Request:
Examples: platforms for building internal chatbots, RAG applications, AI workflows, or custom AI apps.
Evaluate:
Request:
Examples: agents that can execute tasks, call APIs, modify records, or trigger business processes.
Evaluate heavily:
Request:
Agentic systems need additional scrutiny because the risk shifts from “what data can the model see?” to “what actions can the system take?”
Examples: foundation model APIs, hosted open-source models, private model deployments.
Evaluate:
Request:
Examples: AI gateways, AI discovery tools, AI policy enforcement, AI risk management platforms.
Evaluate:
Request:
AI governance products vary significantly because some extend existing security tools while others provide AI-specific controls.
Examples: enterprise search, document intelligence, AI knowledge bases.
Evaluate:
Request:
Examples: model training, evaluation, deployment, monitoring platforms.
Evaluate:
Request:
Examples: CRM, ERP, HR, customer support, finance applications with embedded AI.
Evaluate:
Request:
Require every vendor to provide:
A strong procurement process should score vendors not only on features, but on whether they can produce evidence that security, legal, privacy, and IT teams can independently validate.
A practical first pass is usually to create three review tracks:
Those tracks prevent over-reviewing simple tools while applying deeper controls where business impact is higher.
A practical first pass is usually to create three review tracks:
When sourcing procurement-ready AI vendors that can immediately clear InfoSec, legal, and compliance, you should evaluate the market across **six core architectural and functional categories**. A defensible vendor package in these categories must include standard enterprise controls: SOC 2 Type II, explicit…
When sourcing procurement-ready AI vendors that can immediately clear InfoSec, legal, and compliance, you should evaluate the market across six core architectural and functional categories.
A defensible vendor package in these categories must include standard enterprise controls: SOC 2 Type II, explicit zero-data-retention or isolated tenant policies, RBAC/ABAC integrated with your IdP (Okta/Azure AD), data residency guarantees, and a clear model-substitutability boundary.
If you'd like to narrow this down, tell me:
I can help tailor a specific scoring matrix or questionnaire framework for your team.
For a **procurement-ready AI vendor program**, I’d evaluate vendors across these categories. I’d separate **baseline enterprise security** from **AI-specific risk**, because a vendor can have excellent SaaS security while still leaving important AI questions unanswered. NIST’s AI RMF specifically calls for managing…
For a procurement-ready AI vendor program, I’d evaluate vendors across these categories. I’d separate baseline enterprise security from AI-specific risk, because a vendor can have excellent SaaS security while still leaving important AI questions unanswered. NIST’s AI RMF specifically calls for managing third-party AI resources, including their security, privacy, IP, and supply-chain risks.
NIST specifically treats AI security/resilience as including confidentiality, integrity, and availability of AI systems and their training/output data. NIST 6. Auditability & governance
I'd make Security, Data Protection, Identity, AI Governance, and Legal hard gates, then score the remaining categories:
| Category | Suggested weight |
|---|---|
| Security & compliance | 15% |
| Data/privacy | 15% |
| AI/model governance | 15% |
| Identity/access | 10% |
| AI-specific security | 10% |
| Audit/governance | 10% |
| Legal/contractual | 10% |
| Resilience/operations | 5% |
| Integration | 5% |
| Responsible AI | 3% |
| Vendor maturity | 2% |
This aligns well with the NIST approach of Govern → Map → Measure → Manage, including explicit treatment of third-party AI and supply-chain risks.
Most important procurement principle: don't accept “enterprise-ready” as a product tier. Require evidence for each control—certificate/report, contractual commitment, configuration screenshot/documentation, or demonstrable technical control. Current enterprise AI buyer checklists similarly emphasize SOC 2/ISO 27001, SSO/SCIM/RBAC, residency/retention, model-training controls, logs, and SLAs.
If you're building an actual AI vendor intake questionnaire, I can turn these categories into a procurement-ready 100-point scorecard with mandatory pass/fail controls and the exact questions to send vendors.
When sourcing procurement-ready AI vendors, your evaluation framework needs to split the focus: traditional SaaS security controls (the baseline) and AI-specific data governance (the differentiator).[](https://getagency.com/soc-2-for-ai-companies)…
When sourcing procurement-ready AI vendors, your evaluation framework needs to split the focus: traditional SaaS security controls (the baseline) and AI-specific data governance (the differentiator).
To build a defensible RFP and security review process, evaluate vendors across these core categories:
Essential Checklist Items for the Review Packet
Ensure your security team requires more than standard SaaS assurances before greenlighting:
If you'd like, I can:
