AI Training Data Glossary

AI Training Data: AI training data is the collection of examples, such as text, code, documents, or records of actions, that a machine learning model learns patterns from during training. When licensing company data, its value depends on how real, complete, and well-structured it is, and on whether you have the legal right to share it.What Makes Data Valuable for AI Training? · Why Do AI Labs Need Real Enterprise Data?
Coding Agent: A coding agent is an AI system that writes, edits, tests, and debugs software by taking multi-step actions in a codebase, such as reading files, running commands, and proposing changes. Private repositories with commit history, code reviews, and tests show how real software evolves, which makes them useful for training and evaluating these agents.What Training Data Do AI Coding Agents Need? · GitHub Repositories
Computer-Use Agent: A computer-use agent is an AI system that operates software through its graphical interface the way a person does, by reading the screen and issuing clicks, keystrokes, and form entries. Records of how employees complete real tasks in business applications can help train these agents to handle realistic, multi-step work.What Data Helps Train Computer-Use AI Agents? · Screen Recordings of Real Work
Data Anonymization: Data anonymization is the process of transforming data so that individuals can no longer be identified by any means reasonably likely to be used, taking into account cost, time, and available technology. Under GDPR Recital 26, truly anonymous data falls outside data protection law, so the standard you apply shapes your obligations when sharing data for AI training.Data Anonymization for AI Training: A Practical Guide · De-identification vs Anonymization: What's the Difference?
Data Licensing: Data licensing is an agreement in which a data owner grants another party the right to use a dataset under defined terms while keeping ownership of the data itself. For AI training, the license sets what models the data may train, for how long, under which privacy protections, and for what payment.Data Licensing vs Selling Data: Which Is Right for You? · Key Contract Terms in an AI Data Licensing Agreement
Data Principal and Data Fiduciary (DPDP Act): Data Principal and Data Fiduciary are the two core roles in India's DPDP Act 2023: the Data Principal is the individual the personal data relates to, and the Data Fiduciary is whoever decides the purpose and means of processing it. A company sharing personal data for AI training typically acts as a Data Fiduciary and carries its duties.India's DPDP Act and AI Training Data: A Guide for Businesses
Data Processing Agreement (DPA): A data processing agreement (DPA) is a contract that sets out how personal data is processed when one party handles it on behalf of another, covering purpose, security measures, sub-processors, breach notice, and deletion. GDPR Article 28 requires one between a controller and processor, and similar terms are often used whenever licensed data may contain personal information.Key Contract Terms in an AI Data Licensing Agreement · GDPR and AI Training Data: What Companies Need to Know
Data Provenance: Data provenance is the documented record of where data came from, who created or owns it, how it was collected, and how it has been changed over time. Buyers of AI training data ask for provenance to confirm the seller has the right to license it, especially when data was created for or with clients.Client-Owned Data and IP: What You Can and Cannot Include in an AI Data Deal · How to Prepare Your Company's Data for AI Licensing
Data Retention: Data retention is the set of rules governing how long data is kept and what happens to it when that period ends, such as deletion, return, or certified destruction. In an AI data license, retention terms should state how long the buyer may hold raw data and copies, and whether models already trained on it may be kept.Key Contract Terms in an AI Data Licensing Agreement
De-identification: De-identification is the process of removing or obscuring information that links data to specific individuals, such as names, contact details, account numbers, and other direct identifiers. It reduces privacy risk before data is shared for AI training, but de-identified data is not always legally anonymous, so the method used and its residual risk both matter.De-identification vs Anonymization: What's the Difference? · Data Anonymization for AI Training: A Practical Guide
DPDP Act 2023 (India): The Digital Personal Data Protection Act, 2023 (DPDP Act) is India's law governing the processing of digital personal data, requiring a lawful basis such as consent or certain legitimate uses, plus notice, security safeguards, and breach reporting. Indian companies licensing data for AI training must either meet these duties or remove personal data before sharing.India's DPDP Act and AI Training Data: A Guide for Businesses · Is It Legal to Sell Company Data for AI Training?
Enterprise Data Partnership: An enterprise data partnership is an arrangement in which a company provides proprietary operational data, such as code, tickets, documents, or business records, to support AI model development under a contract covering scope, permitted use, privacy protections, and compensation. It lets a business earn from existing data while setting firm limits on how that data is used.What Are AI Data Partnerships, and How Do They Work? · How to Sell Your Company's Data for AI Training
Exclusive vs. Non-Exclusive License: An exclusive license grants one licensee sole rights to use data for the agreed purpose, while a non-exclusive license lets the owner license the same data to several parties. The choice affects pricing, since exclusivity is usually valued higher, and determines whether you can offer the dataset to other AI developers later.Data Licensing vs Selling Data: Which Is Right for You? · Key Contract Terms in an AI Data Licensing Agreement
Fine-Tuning: Fine-tuning is the process of further training an already pretrained AI model on a smaller, targeted dataset so it performs better in a particular domain or task. Company data such as support tickets, code, or internal documents is often valuable for fine-tuning because it reflects specialized, real-world work that public data rarely captures.What Makes Data Valuable for AI Training?
Frontier AI Model: A frontier AI model is one of the most capable general-purpose AI models available at a given time, typically developed by a small number of labs using very large amounts of data and computing power. Developers of these models seek high-quality real-world data, including enterprise data, to improve how models handle complex professional tasks.Why Do AI Labs Need Real Enterprise Data?
GDPR: The General Data Protection Regulation (GDPR) is the European Union's data protection law governing how personal data of individuals in the EU is collected, used, and shared, including by organizations outside the EU that offer them services or monitor their behavior. It applies to AI training data containing personal data, though anonymous data falls outside its scope.GDPR and AI Training Data: What Companies Need to Know
HIPAA Expert Determination: HIPAA Expert Determination is one of two methods under the HIPAA Privacy Rule for de-identifying health information, in which a qualified expert applies statistical or scientific principles and documents that the risk of identifying any individual is very small. It allows more useful detail to be retained than Safe Harbor, which suits richer AI training datasets.HIPAA and AI Training Data: De-identification Explained · De-identified Clinical Notes
HIPAA Safe Harbor: HIPAA Safe Harbor is one of two methods under the HIPAA Privacy Rule for de-identifying health information, requiring removal of 18 specified identifiers, such as names, most dates, and small geographic units, with no actual knowledge that the remainder could identify someone. It is simple to apply but can strip detail that makes health data useful for AI training.HIPAA and AI Training Data: De-identification Explained · De-identified Clinical Notes
Non-Disclosure Agreement (NDA): A non-disclosure agreement (NDA) is a contract in which parties agree to keep shared information confidential and use it only for a stated purpose. In AI data licensing, an NDA should be signed before any data sample is shared, so the evaluation is protected and the buyer's allowed uses of that sample are clearly limited.NDA Checklist for Sharing AI Training Data
Permitted Use: Permitted use is the contract clause that defines exactly what a licensee may do with licensed data, such as training or evaluating specific models, and what it may not do, such as reselling the data or attempting re-identification. Clear permitted-use terms are one of the main ways a data owner keeps control after data leaves its systems.Key Contract Terms in an AI Data Licensing Agreement · What Are the Risks of Selling Company Data to AI Companies?
Personally Identifiable Information (PII): Personally identifiable information (PII) is any information that can identify a specific person, either directly, like a name or email address, or indirectly when combined with other data, like a birth date and postal code. Finding and removing or masking PII is a core step in preparing company data for AI training.Data Anonymization for AI Training: A Practical Guide · How to Prepare Your Company's Data for AI Licensing
Protected Health Information (PHI): Protected health information (PHI) is individually identifiable health information, covering a person's health condition, care, or payment for care, that is held or transmitted by a HIPAA covered entity or its business associate. PHI cannot be licensed for AI training without patient authorization or another HIPAA permission unless it is first properly de-identified.HIPAA and AI Training Data: De-identification Explained · Data partnerships for hospitals and healthcare providers
Pseudonymization: Pseudonymization is the process of replacing direct identifiers with artificial codes so data can no longer be linked to a specific person without separate additional information that is kept apart and protected. Under GDPR, pseudonymized data is still personal data, so it reduces risk but does not remove legal obligations when licensing data for AI training.De-identification vs Anonymization: What's the Difference? · GDPR and AI Training Data: What Companies Need to Know
Re-identification Risk: Re-identification risk is the likelihood that de-identified or anonymized data can be linked back to specific individuals, often by combining remaining details such as dates, locations, or job titles with other available information. Assessing and reducing this risk is central to deciding whether data is safe and lawful to share for AI training.De-identification vs Anonymization: What's the Difference? · What Are the Risks of Selling Company Data to AI Companies?
Reinforcement Learning (RL) Environment: A reinforcement learning (RL) environment is a controlled setting, often a software sandbox, where an AI agent takes actions, observes results, and receives rewards so it can improve through trial and error. Real business workflows with clear success criteria can be turned into RL environments that teach agents to complete practical, multi-step tasks.How Real Enterprise Workflows Become RL Environments for AI
Reward Signal (Verifiable Outcome): A reward signal is the feedback an AI system receives during reinforcement learning that indicates how well its actions achieved a goal, and a verifiable outcome is a result that can be checked automatically, such as tests passing or figures reconciling. Company data that records real outcomes makes it possible to build reliable rewards for agent training.How Real Enterprise Workflows Become RL Environments for AI · Test Suites & QA Records
Secure Data Transfer: Secure data transfer is the practice of moving data between organizations using encryption in transit and at rest, restricted access, and an auditable record of who received what and when. In an AI data partnership, it should happen only after de-identification and contract signing, using a method both parties have agreed in writing.How to Transfer Data Securely for AI Training
Synthetic Data: Synthetic data is artificially generated data designed to imitate the structure or statistical patterns of real data without directly copying real records. It can add volume and protect privacy, but it often misses the edge cases, messiness, and real decisions found in genuine operational data, which is why real company data remains valuable for AI training.Why Do AI Labs Need Real Enterprise Data?
Trajectory (Agent Trajectory): A trajectory is the recorded sequence of observations, actions, and outcomes that an AI agent or a person goes through while completing a task from start to finish. Trajectories drawn from real work, such as resolving a ticket or shipping a code change, show agents how tasks actually unfold step by step.What Is Workflow Data, and Why Do AI Agents Need It? · What Data Helps Train Computer-Use AI Agents?
Workflow Data: Workflow data is information that records how work actually gets done, including the sequence of steps, tools used, decisions made, and handoffs between people across tickets, documents, and business systems. It is valuable for training AI agents because it captures the context and order of real tasks, not just their final results.What Is Workflow Data, and Why Do AI Agents Need It? · Jira Projects & Issue History

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AI training data glossary

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