Short answer: you can’t always discover the exact documents a closed LLM was trained on, but you can (1) audit everything public about your brand, (2) monitor & search the web where models harvest data, (3) probe models and retrieval systems to see what sources they cite or retrieve, and (4) use contractual, technical and takedown options to limit or remove content. Below is a practical, ordered plan you can run right now.

1. Do an immediate inventory of public content

• Crawl and index your own properties (website, blogs, docs, help center, product pages, press releases). Export the text.
• Collect third‑party references: partner pages, distributors, affiliates, guest posts, press coverage.
• Gather social posts, forum threads, reviews, FAQ entries, job listings, public spreadsheets, and images.

2. Search the web where models get training data

• Use search engine queries to find mentions:
  • site:example.com "YourBrand"
  • "YourBrand" review
  • "YourBrand" leak OR "YourBrand" password OR "YourBrand" API key
• Check:
  • News sites, blogs, aggregator sites
  • Forums and Q&A (Reddit, StackExchange, product forums)
  • Review sites (Trustpilot, G2, Yelp)
  • Public code repositories and paste sites (GitHub, GitLab, Pastebin)
  • Web archives (Wayback Machine)
• Set up continuous alerts: Google Alerts, Talkwalker Alerts, Mention.

3. Use social listening and backlink tools

• Tools (free/paid): Google Search Console, Bing Webmaster, Ahrefs, SEMrush, Moz, Mention, Brandwatch, Meltwater, Talkwalker.
• What to look for: backlinks that copy large text blocks, forum threads with detailed info, unattributed transcripts or manuals.

4. Probe models and retrieval systems

• For public LLMs that provide citations, ask them to cite sources for statements about your brand and save those citations.
• For closed models (no citations), use:
  • Prompt probing (“What sources do you know about [YourBrand]? List URLs or publication names.”)
  • Get model outputs on specific factual claims, then search exact phrases from the output in quotes to find origin pages.
• If a vendor uses RAG (retrieval augmented generation) in your product, request the retrieval logs / source list — vendors often can provide it.

5. Use similarity / provenance tracing with embeddings

• Create embeddings of:
  • Public pages about your brand (your crawl + likely sources).
  • Problematic model output text.
• Run nearest‑neighbor search (cosine similarity) to find which page a model output most closely matches. Open-source toolkits: sentence-transformers, FAISS, Pinecone, Weaviate. This technique often identifies the likely source page even if the model doesn’t cite it.

6. Look for common public corpora

• Many models are trained on large public crawls (Common Crawl), public social content, Wikipedia, and public code repositories. If you see content from such sources about your brand, it’s likely to have been included in training corpora.
• Note: you usually cannot prove a model used a specific document unless the provider shares training metadata.

7. Legal, contractual and takedown steps

• If content is copyrighted or sensitive: issue DMCA takedown notices to sites that host the content.
• Ask platforms to remove sensitive personal / secret data (credentials, PII).
• Review contracts with AI vendors — ask for provenance, data usage, and opt‑out rights. Many vendors will disclose when they use customer content in training; some offer opt‑outs.
• Use robots.txt and meta noindex for pages you don’t want crawled — this won’t undo existing copies but reduces future crawling.

8. Preventive and corrective measures

• Publish canonical, high‑quality authoritative content (FAQs, knowledge base, schema.org structured data) so retrieval systems return your pages as the authoritative source.
• Add clear copyright and usage terms.
• Remove or restrict old content (archives, support threads) that leak proprietary info.
• Where possible, move sensitive docs behind authentication or paywalls.

9. Ongoing monitoring & escalation

• Create a dashboard: web crawl results + alerts + top‑matching sources from embedding checks.
• If you discover a model or service generating inaccurate or harmful content about your brand, contact the service provider with examples and request corrective action or source attribution.

Quick checklist (practical queries and actions)

• Run these searches now:
  • site:example.com "YourBrand"
  • "YourBrand" "user manual" / "support" / "password" / "API key"
  • exact-phrase searches of suspicious model outputs in quotes
• Save model outputs you want traced (full text + timestamp).
• Build embeddings for your public corpus and run nearest‑neighbor matches against the model output.
• Ask your AI vendors for a list of sources used in any retrieval layer and for their data‑use policy.

Reality check

• You often cannot get a definitive list of training examples from closed LLMs — the best practical approach is combination: web monitoring, similarity matching, vendor disclosure requests, and legal takedowns where appropriate.

If you want, I can:

• Draft exact search queries for your brand name and a few likely sensitive phrases.
• Provide a short script (Python) that computes embeddings and finds the closest web pages for a piece of model output. Which would you prefer?