Grab the full download below—perfect for saving or sharing with your team.
Artificial intelligence is changing how licensed data is accessed, consumed, transformed, and distributed. What was once a straightforward relationship between a data provider, a licensee, and a defined group of authorized users can now involve AI models, agents, APIs, third-party platforms, software embeddings, and derivative datasets.
For data licensors, the question is no longer simply who has access to the data. Increasingly, the questions are: Where did the data go? What interacted with it? What was created from it? And were those uses authorized?
Below are five emerging AI-driven risks and complexities that are becoming central to data licensing compliance and data licensing audits.
Not sure your license agreements or audit program account for AI use? Talk to our Royalty & IP Services team about a data licensing risk assessment.
Licensed data can become embedded within AI models, raising new questions around permitted use, retention, and ownership.
The commercial value of AI data rights is already clear. Reddit reportedly entered into an agreement worth approximately $60 million annually allowing Google to use Reddit content to train AI models. The Associated Press licensed access to part of its archive to OpenAI, and News Corp entered into a multi-year agreement giving OpenAI access to current and archived content from publications including The Wall Street Journal, Barron's, and MarketWatch.
These deals highlight an important distinction: having a license to access data does not necessarily mean having a license to ingest that data into an AI system.
From an audit perspective, licensors increasingly need to determine whether their information entered AI development environments, which models or applications received it, whether it was used for training, fine-tuning, or grounding, and whether those activities were permitted under the agreement.
This can require reviewing far more than a traditional user list. Training datasets, data repositories, model documentation, development environments, and ingestion pipelines can all become relevant to determining compliance.
A licensee may never redistribute the original dataset. Instead, AI can transform licensed data into new model outputs, software embeddings, and synthetic datasets that blur traditional usage boundaries.
This raises a hard question for licensors: when does something created from licensed data become sufficiently transformed that it is no longer subject to the original licensing restrictions?
For a modern data licensing audit, reviewing derivative use can be just as important as reviewing the original dataset. Audit procedures may need to consider embeddings, derived databases, synthetic datasets, model outputs, and other artifacts created using licensed information.
Licensed data can flow through models, APIs, agents, and third-party platforms, making its ultimate use hard to trace, though not impossible.
A modern data flow might look something like:
Data Provider → Licensee → AI Platform → Model → Agent → API → Customer Application
Each stage represents another potential use, transformation, storage location, or distribution point for licensed information.
This is increasingly relevant as data providers make their information available through cloud and AI ecosystems. Reuters, for example, announced in 2026 that its content would be available through Snowflake Marketplace for integration into enterprise AI applications.
These models create real commercial opportunities for data providers, but they also make compliance more technically complex. A customer may have the right to use information internally while being prohibited from redistributing it externally. What happens when that information passes through a third-party AI provider? What if an agent uses the data to generate an answer delivered to another application? What if multiple AI systems interact with the same licensed dataset?
The next generation of data licensing audits needs to follow the data across the technology ecosystem, not just count users at the original point of access.
See how a data flow assessment works. Schedule time with Connor to map your licensed data across your AI ecosystem.
AI agents can autonomously access and consume licensed data beyond traditional human-user models.
Historically, most data licenses were built around identifiable consumption models: named users, employees, business units, applications, servers, or API calls.
Agentic AI changes that assumption. Technologies such as Model Context Protocol (MCP) let AI applications and agents connect directly to databases, enterprise systems, and other tools. Instead of an employee manually searching a licensed database, an AI agent might autonomously query that database, combine the information with other sources, generate an analysis, and pass the result into another application.
That raises entirely new licensing questions. Is an AI agent an authorized user? Can one agent access information on behalf of hundreds or thousands of employees? Does an existing API license permit autonomous AI consumption? Can an agent pass retrieved information to another agent or system?
For data licensing audit programs, understanding agentic access may require reviewing MCP connections, usage and access logs, service accounts, authentication methods, permissions, and automated workflows.
The definition of a "user" is becoming considerably more complicated.
Connor has developed new verification methods to determine where licensed data went, how AI systems used it, and whether that use was authorized. Companies are increasingly taking a proactive approach to compliance.
This may be the most important development for data licensors. AI has made traditional compliance verification more complicated, and at the same time, proprietary datasets have become more valuable than ever.
A modern AI data licensing audit may need to examine training datasets, ingestion pipelines, data lineage, databases, third-party AI platforms, derived datasets, data retention practices, and downstream applications.
The audit question is evolving from:
"Who has access to our licensed data?"
to:
"Where did our licensed data go, what interacted with it, what was created from it, and was each use authorized?"
AI presents a significant opportunity for data owners. Proprietary datasets that historically powered research, analytics, and software applications can now power models, AI agents, and entirely new information products.
But the same technology introduces compliance risks that traditional license agreements and audit methodologies were not built to address. Organizations with established data licensing audit programs should consider whether their current approach adequately addresses AI training and ingestion, derivative and synthetic data, downstream AI ecosystems, agentic data access, and AI-specific audits.
At Connor, we continue to develop new approaches to AI data licensing audits and work with data licensors to understand how their proprietary information is being consumed across increasingly complex technology environments.
As AI adoption accelerates, proactive compliance is becoming more important, not less. The technology is new, but the underlying principle is not: understand where your licensed data is being used, verify that the use is authorized, and protect the value of the underlying asset.
Christopher Lincoln is a Director at Connor Consulting, where he guides clients in building robust, self-sustaining license compliance programs. For organizations ready to move data licensing compliance from a cost center to a value driver, the starting point is understanding that every data transaction is a business opportunity waiting to be protected and optimized.
Connor helps data licensors trace how their information moves through AI models, agents, and third-party platforms, and verify that every use is authorized.
