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Answer the question every enterprise buyer asks first.

"Do you train on our data?" Then 149 follow-up questions about model providers, PII in prompts, retention windows, and OWASP LLM risks. The Targhee agent turns the new AI governance questionnaire into a two-day review — every answer cited from your model card, DPAs, and AI risk documentation, with your team approving before anything goes out.

AI
Enterprise AI Governance Review
152 questions · NIST AI RMF + custom
AI Complete
138 / 152 auto-completed Avg confidence 94%
Do you train on customer inputs or outputs?
No training on customer data. Zero-retention enabled for OpenAI; enterprise tier for Anthropic with no training opt-out.
Model Card v1.2 · §4 · OpenAI DPA
98%
List all LLM providers & AI subprocessors.
OpenAI, Anthropic, AWS Bedrock, Pinecone, Datadog. Full chain with regions and DPAs attached.
Subprocessor List · §3 · updated 3 weeks ago
95%
Describe your prompt-injection defense posture.
Input validation + output filtering. Red-team report not yet uploaded.
OWASP LLM01 · needs evidence
61%
2–3d
Avg turnaround
per AI review
95%+
AI first-pass
accuracy
12+
AI frameworks
out of the box
75%
Never arrive
with Trust Center
§ 01 — The problem

AI governance is the new security questionnaire.

Enterprise buyers who once sent you a SIG Lite now send 150 additional questions about model training, prompt injection, PII handling, and provider-chain risk. These questions didn't exist two years ago. They're on every vendor review today.

What they're asking

A new interview on how your AI actually works.

Every enterprise AI review now probes the same areas: whether you train on customer data, what your model provider chain looks like, how you handle PII in prompts, your OWASP LLM Top 10 posture, and your classification under the EU AI Act.

About a third of the questions map to a standard framework. The rest are written by the buyer's own AI governance committee to probe their specific risk register.

Training data Model provider chain PII in prompts OWASP LLM Top 10 EU AI Act Model cards
Why they're new

No answer library exists. Yet.

Most AI-native teams haven't answered these questions before. There's no 3-year-old SOC 2 response to copy from, no industry-standard phrasing to borrow. Every answer gets written from scratch — and every hallucination or imprecise claim costs legal hours to repair.

Meanwhile, regulations are moving: the EU AI Act is live, NIST AI RMF is firming up, ISO 42001 is gaining traction. Your buyers want conformity language, not philosophical answers.

EU AI Act · live NIST AI RMF 1.0 ISO 42001 US AI Exec Order Zero precedent
§ 02 — The approach

Two strategies for AI governance. One platform.

Answering AI governance questionnaires faster matters. Stopping most of them from arriving in the first place matters more. Targhee handles both, and they share one AI-aware knowledge base underneath.

Strategy 01

Deflect: publish the AI answers before they ask.

A Trust Center with your model card, subprocessor chain, training-data stance, and OWASP posture — behind a click-wrap NDA. Most AI governance questionnaires are buyers trying to confirm these artifacts exist. Show them first and the questionnaire often never gets sent.

  • Model card & subprocessor chain, published once
  • NDA-gated so buyers self-serve without back-and-forth
  • Access logs surface buyer intent before the deal call
−75%
Fewer inbound AI questions · 90 days
Explore Trust Center →
Strategy 02

Automate: answer the rest with citations.

When an AI governance questionnaire does arrive, Targhee's AI drafts every answer from your model card, DPAs, policies, and eval reports — each line cited and confidence-scored. Your ML and security leads review flagged answers, approve the rest, export.

  • Citations back to your model card & provider DPAs
  • Confidence score flags low-confidence answers for SME review
  • Export in the questionnaire's original format
2–3d
Avg review · per AI questionnaire
Explore Questionnaire Automation →
§ 03 — Under the hood

Built for the parts of AI governance that actually trip you up.

The model provider chain. The framework coverage. The evidence requirements that didn't exist when your SOC 2 was written. Here's how Targhee handles the two hardest parts of AI vendor review.

Subprocessor chain

Your model provider chain, answered once.

Enterprise buyers want one coherent answer to "what happens to our data in your AI stack" — not a scavenger hunt through five provider trust pages. Targhee indexes your OpenAI, Anthropic, Bedrock, and vector DB DPAs alongside your own policies and cites the right source for every question.

  • Provider DPAs parsed and versioned (OpenAI, Anthropic, Bedrock, and more)
  • Regional residency and zero-retention flags tracked per provider
  • Automatic updates when a provider changes their terms
  • One-answer output with citations to every provider in the chain
AI Subprocessor Map
5 providers · 3 regions · last audit 12d ago
Up to date
OA
OpenAI
Primary LLM · GPT-4o
Zero retention US
AN
Anthropic
Fallback LLM · Claude
No training US
BR
AWS Bedrock
Embeddings · Titan V2
No training EU
PC
Pinecone
Vector store · serverless
Encrypted US-E
DD
Datadog
Observability · metadata only
No content US
Framework coverage

Every AI framework on the enterprise questionnaire.

The AI governance stack is new and moving fast. Targhee's knowledge base stays current on every framework your buyers reference — NIST AI RMF, EU AI Act, ISO 42001, OWASP LLM Top 10, MITRE ATLAS, model card format — so you don't have to track them all yourself.

  • NIST AI RMF 1.0 — Govern / Map / Measure / Manage mapped
  • EU AI Act — Article 6 classification, Article 25 value-chain responsibilities, Article 50 transparency
  • OWASP LLM Top 10 — all 10 categories with control-doc pulls
  • Model card format — Mitchell et al. 2019 supported as evidence and output
AI framework coverage
12 frameworks · auto-updated
NIST AI RMF 1.04-function map
EU AI ActArt 6, 25, 50
ISO/IEC 42001AIMS
ISO/IEC 23894AI risk
OWASP LLM Top 1010/10 mapped
MITRE ATLASAdversarial ML
Model CardsMitchell 2019
SOC 2 Type IITrust services
ISO 27001 / 27701ISMS + privacy
GDPR Art. 22Auto decisions
US AI Exec OrderFederal proc.
CSA AI SafetyCSA Star
§ 04 — Who it helps

Every team dragged into AI vendor review.

AI governance questionnaires cross ML, security, and legal. Each of them gets pulled into every review. Targhee compresses the workflow for all three — without changing the review or approval authority any of them need.

§ 05 — Questions

What AI teams always ask us.

Common AI governance questions.

Specific to your model provider stack, your EU AI Act classification, or an enterprise review currently in your queue? Bring it to the demo — we'll walk through it live on your actual documents.

Book a demo →
Partly. Enterprise buyers want both — your model provider's posture (DPA terms, zero-retention settings, regional residency) and your own controls on top (how you call the API, what you log, whether you fine-tune, how you handle PII in prompts). Targhee indexes your provider DPAs alongside your own policies and cites the right source for each question. Your buyer gets one coherent answer, not a link to someone else's trust center.
Three evidence layers, all cited: your own policy (we don't train on customer content), your model provider's DPA (OpenAI zero-retention, Anthropic enterprise, Bedrock no-training), and your technical controls (what you log, what you exclude, your data flow diagram). Targhee assembles these into one answer with citations to all three sources. That's what satisfies enterprise legal — not a one-liner.
Yes — keeping framework coverage current is part of what we maintain. Article 6 classification, Article 25 value-chain responsibilities (provider vs deployer), Article 50 transparency obligations, and conformity assessment language. As the GPAI code of practice and implementing acts land, we incorporate major updates to the knowledge base. Your buyers don't want a philosophical answer — they want conformity language that maps to their risk register.
Supported. Targhee maps enterprise AI security questions to OWASP LLM Top 10 categories (LLM01 prompt injection, LLM02 insecure output handling, LLM03 training data poisoning, and so on) and pulls answers from your controls documentation — input validation, output filtering, guardrails, red-team results, incident response playbook. If you don't have a red-team report yet, Targhee flags the gap so you can build one before your next enterprise review.
Yes — and we'll probably help you build one in the process. Many AI-native teams haven't published a formal model card (Mitchell et al. 2019 format). Targhee can answer model-card-style questions from your architecture docs, eval reports, and product documentation, and generate a draft model card you can publish to your Trust Center. Enterprise buyers increasingly ask for it — having one differentiates you.
Every answer includes a source citation back to your actual documentation — model card, DPA, AI RMF profile, policy — plus a confidence score. Low-confidence answers surface first in the review queue. Nothing goes out without human approval. For AI-specific questions we apply stricter confidence thresholds because the consequences of an imprecise training-data answer are higher. If the source isn't in your knowledge base, Targhee flags the gap rather than inventing something.
Yes — and this is now the most common AI early-stage pattern. Enterprise buyers are asking AI governance questions before they care about SOC 2, because the risk surface is new. Targhee helps you answer honestly and defensibly using whatever documentation you have (architecture docs, eval reports, provider DPAs, draft policies), flags gaps where SOC 2 or NIST AI RMF evidence would strengthen your response, and builds the paper trail that accelerates both your SOC 2 and AI governance maturity later. Pair us with Vanta or Drata to run SOC 2 and AI questionnaires in parallel.

Bring an AI governance review to the demo.

Send us whatever enterprise AI assessment is currently stuck in your pipeline. We'll run it through Targhee live on your actual documents — model card, DPAs, policies — so you can compare the output to what your team would draft manually.

3 free questionnaires · 20-minute demo