Guide library
The AI training data market, organised by question
Start with the broader question, then explore the records, rights, pricing and practical guides underneath it.
Sources and guidance · Reviewed October 202633original practical guides
18platform rights guides
17jurisdiction review guides
7starting questions
33 guides
Reviewing company documents for AI licensingBuild a document collection with clear ownership, version boundaries, context, and confidentiality controls.From raw archive to a buyer-ready sampleA staged preparation workflow that tests usefulness before committing to expensive cleanup or broad disclosure.Can a closed company license its old data?Review authority, asset ownership, privacy promises, retention, and transaction costs before treating an archive as a remaining asset.Reviewing acquired datasets and third-party sourcesDistinguish original, purchased, scraped, synthetic, and commissioned material before offering a combined training collection.
Can you license company email for AI training?A practical review of email ownership, thread quality, attachments, and safer sampling before discussing a dataset with a buyer.Preparing Slack conversations for a data licenseHow to assess channel boundaries, thread context, guest contributions, and the limits of a Slack export.Evaluating CRM data for AI licensingAssess sales workflows, outcome labels, contact information, and the difference between process data and a prospect list.Licensing support tickets with useful resolution contextBuild a support dataset around genuine issue-resolution sequences while managing customer content and sensitive attachments.Before licensing source code to an AI buyerReview repository ownership, dependencies, secrets, build context, and the meaning of a usable code example.Preparing audio recordings for AI licensingReview speaker permissions, recording context, transcripts, acoustic quality, and restrictions on voice-related uses.Evaluating a video archive for AI trainingA seller’s guide to visual rights, temporal context, audio tracks, annotations, and usable footage counts.Licensing image collections with clear provenanceAssess image ownership, releases, captions, duplicates, and the rights included in an AI dataset proposal.Reviewing financial business data before licensingSeparate operational finance records from personal financial information, restricted market feeds, and unsupported outcome labels.Licensing design files and creative workflowsAssess layered files, design decisions, licensed assets, comments, and the value of revision history.Assessing meeting transcripts and recordings for licensingReview participant expectations, recording permissions, summaries, action items, and conversational completeness.Preparing IT operations records for an AI buyerEvaluate incident histories, runbooks, logs, and security-sensitive infrastructure details before sharing.Licensing Jira issues and development historiesAssess issue quality, code links, workflow changes, customer details, and evidence that a problem was resolved.
Prepare for withholding questions on a US data licence paymentWhat a foreign seller should ask its tax adviser and the payer before relying on a gross offer.Comparing AI data offers without invented market ratesNormalize scope, usable volume, cleanup costs, payment conditions, and retained rights before choosing a proposal.Negotiating exclusivity and license durationSeparate access periods, training windows, model retention, renewals, and the specific rights an exclusive deal would restrict.When a data license becomes payablePlan acceptance, invoicing, milestones, cross-border administration, and evidence of delivery before transferring a full collection.How to read AI data deal announcements criticallySeparate confirmed facts, undisclosed terms, revenue forecasts, and program eligibility before using public deals in a seller decision.
Building a provenance pack a buyer can inspectDocument origins, permissions, transformations, versions, and quality limits without creating an unnecessary second sensitive archive.How to assess an AI data buyer before sharingCheck identity, purpose, procurement, sample controls, and downstream use without assuming every inquiry is a genuine offer.
Employee communications need a separate reviewBuild a defensible review process for workplace messages, email and meeting records before an AI licensing discussion.Prepare a GDPR review for an AI data licenceOrganise the questions and evidence needed before personal data is repurposed or transferred.A rights checklist before offering a datasetSeparate access, ownership, sublicensing authority, privacy, and confidentiality before a buyer receives material.A privacy review before a buyer receives dataBuild a practical review of purpose, personal information, transformations, and recipient controls before disclosure.Health data is a specialist licensing decisionUnderstand why removing names is not a complete health-data clearance and what to prepare for a qualified review.Public data, scraping, and fair use: separate questionsAvoid treating public access, collection method, and downstream licensing authority as interchangeable claims.
Licensing task environments instead of static recordsDefine reproducible tasks, reset behavior, evaluation rules, security boundaries, and support when offering an interactive environment.AI data licensing terms in plain languageA practical glossary for discussing collection quality, training uses, rights, evaluation, and delivery without confusing them.