It’s no secret AI is changing the way legal teams work, and in many ways, that's a great thing. But as technology advances and generative AI becomes a fixture in the workplace, there are real risks that legal professionals need to understand, especially around data privacy and the protection of sensitive information. The consequences of getting this wrong aren’t hypothetical. They play out in courtrooms.
What is AI Privacy?
Data and information privacy in an AI world, at its core, is about controlling what happens to information once it enters an artificial intelligence system. That sounds simple, but the development of AI has made it anything but.
Modern AI systems—including large language models and generative AI tools that can produce text, images, code, and more—are trained on enormous datasets. The companies that win the AI race, as I often tell clients, are the ones with the resources to gather the most valuable information to train their models. That information includes personal data, proprietary business data, and in many cases, data collected from individuals without their knowledge or consent.
And this is exactly what makes AI data and information privacy different from traditional data security. It's not about keeping a database locked but rather about understanding what happens to information the moment it interacts with AI.
The greatest AI privacy risks often arise from ordinary use of tools employees trust every day.
Understanding the Privacy Risks of AI
There are a few categories of risk that I see come up most often when I work with companies thinking through their AI exposure.
The first is inadvertent disclosure. Users who input confidential information into a publicly available AI tool (think things like business plans, source code, or internal formulas) can inadvertently destroy the protected status of that information. Imagine an employee at a major company who wants to move faster on a project and decides to paste a proprietary document into a consumer AI tool to get a quick answer. In that moment, they may have handed that information to a system that will use it to train future models and potentially expose it to other users. This isn’t a hypothetical, either. In fact, we have case law on it now.
In Trinidad v. OpenAI (2026), a federal court ruled that voluntarily submitting proprietary frameworks to a public generative AI platform forfeits trade secret status. The court's reasoning was straightforward: When you click “agree” on the terms of a public AI tool, you are consenting to the possibility that your inputs will be retained and used to train the model. The protection is gone.
A related case drives the point home from a different angle. In US v. Hepner, a senior executive facing federal charges for securities fraud and wire fraud tried to shield his communications with Anthropic's Claude from use as evidence at trial, arguing they were protected by attorney-client privilege. Hepner claimed the conversations contained information he had received from legal counsel and were made in furtherance of seeking legal advice.
But the court rejected that argument on two grounds: Claude is not an attorney, and more to the point, the communications were not confidential because Hepner had entered them into a publicly available AI system. The privilege claim failed entirely. The lesson for legal teams is the same one Trinidad teaches, just from a different direction: The moment sensitive information enters a public AI tool, the legal protections you assumed were around it may no longer apply.
The second risk is inference. If an AI tool has accessed behavioral data about an individual, it can infer sensitive information beyond what that person intended to share. This includes health status, financial situation, or other personal details. This is one of the more under-discussed risks, and it deserves more attention from legal teams.
The third is data retention. Once information has been used to train an AI model, it is extremely difficult to extract. It may become mixed with other datasets, potentially from parties who never consented to its use. The AI retains it, and you have very little recourse.
Beyond the workplace, AI privacy risks extend to the broader data landscape. Facial recognition technology is one of the most visible examples. AI systems have been used to scrape billions of images from social media platforms and build identification databases, often without the knowledge or consent of the people pictured. The litigation around Clearview AI, which was barred from selling its database to most private companies in the U.S. following action by the Illinois Attorney General, established the precedent that posting a photo publicly does not constitute consent to biometric processing. The shift toward what researchers have called ubiquitous data collection, where AI systems gather and analyze information at a scale that was previously impossible, is exactly why legal frameworks are struggling to keep up.
Privacy Protection Laws
The regulatory landscape for AI and personal data is moving fast, and it looks different depending on where your clients operate.
In the European Union, the General Data Protection Regulation (GDPR) remains the foundational framework governing how personal data can be collected, processed, and stored. The EU AI Act, which came into force in August 2024 and is being implemented through 2027, builds on that foundation by establishing a risk-based framework for AI governance. High-risk AI systems, including those used in hiring, credit scoring, healthcare, and law enforcement, face strict requirements around data governance, transparency, and human oversight. Facial recognition in public spaces, for instance, is heavily restricted under the Act.
In the United States, there is no single federal equivalent to the GDPR, but state-level legislation is proliferating. California, Texas, and other states have enacted data privacy laws that impose requirements on how companies handle personal data. AI-specific transparency laws are emerging as well, and as I have seen firsthand with cases like XAI v. Bonta, AI developers are already pushing back on disclosure requirements through the courts.
The lack of uniformity across states and between the U.S. and the European Union creates real compliance complexity for companies operating across jurisdictions. This is an area to watch closely.
AI Privacy Best Practices
This is where I spend a lot of time with clients, because knowing the risks is only useful if you do something about it.
Never use free, publicly available AI tools for sensitive workloads.
Consumer versions of AI tools, or the ones that are free and accessible to anyone, are typically built on terms of service that allow the company to retain your data and prompts and use that information to train their models. That means anything your employees type into those tools could be retained, analyzed, and potentially surfaced to other users. There is no enterprise privacy protection there.
Use enterprise versions of AI tools, and read the terms carefully.
Enterprise licenses typically include provisions stating that the vendor will not retain your data or use your prompts to train public models. But I tell clients not to assume. Read each tool's terms and conditions for the particular license tier. What one enterprise contract says can be very different from another. Be aware that uploading or disclosing trade secrets, or protected data (think protective order requirements in cases), even into enterprise-level AI may erase legal protections and violate confidentiality mandates.
Build an AI use policy with real specificity.
The policies I have seen work are the ones that give employees a clear AI governance framework, like a tiered approach, almost like a stoplight system, where category one information is off-limits for any AI use, and category three is generally safe. Without that kind of structure, you are relying on individual employees to make judgment calls they are not equipped to make. And when something goes wrong, it often isn't intentional. It's employees who thought they were just doing their job faster. For help developing AI governance frameworks, companies can consult with artificial intelligence lawyers.
Train your employees.
A policy that lives in a handbook is not enough. Employees need to understand what a confidential document actually looks like in their worlds, what confidentiality levels there are, what the consequences are of inputting it into an AI tool, and who to ask when they are unsure. Make that last part easy. Give them a named person or team to go to before using AI on anything that feels sensitive.
Implement technical controls.
On the infrastructure side, there are tools that can help. AI gateway proxies can act as a protective layer that automatically removes or cleans sensitive data before it is sent to an external AI model. Specifically, AI gateway proxies can intercept outgoing prompts, analyze that content, redact or transform sensitive information, and clean the prompt before it reaches an external model. Granular access controls, sometimes called least-privilege access, are a security approach where users, systems, or applications are given only the minimum level of access necessary to perform their specific tasks, nothing more. These access controls reduce the risk of confidential data being included in prompts or outputs. Endpoint data loss prevention (DLP) tools can actively monitor user actions and prevent sensitive or proprietary information from being copied and pasted into unauthorized AI platforms or workspaces. These are not inexpensive, but for companies with significant trade secrets or sensitive client data, they are worth the investment.
Monitor and audit.
Activity logging, prompt history review, and forensic auditing all give companies visibility into how employees are actually using AI tools. This matters both as a deterrent and as a resource if something goes wrong, particularly when an employee departs and there is a question about what they may have taken.
Review your contracts.
Any time AI use is possible in the context of a vendor or partner relationship, your agreements need to address it explicitly. Can the other party use AI in connection with your shared information? What are the restrictions? What happens to the data? These are not questions you want to answer after the fact.
Use your leverage.
Companies that possess valuable proprietary data—often more than they realize—hold greater bargaining power than they might expect when working with AI developers. Because this data is so critical, organizations can leverage it to negotiate stronger safeguards, enforce strict technical controls, and require robust contractual protections from the AI vendors they engage with.
Frequently Asked Questions
What is AI data and information privacy?
AI data and information privacy refers to how confidential information is handled, used, and protected within artificial intelligence systems. Because AI models are trained on large amounts of data, there is a significant risk that sensitive information—whether entered into a system or scraped from the web—will be retained, used in unintended ways, or exposed to other users.
What are the biggest AI privacy risks?
For legal teams and their clients, the most significant risks are inadvertent disclosure (employees entering proprietary or sensitive information into public AI tools), data retention (information that is extremely difficult to recover once it has been used to train a model), and inference (AI systems deriving sensitive personal information from behavioral data beyond what an individual knowingly shared). There is also the risk of AI-enabled data scraping, in which systems access information from external sources through automated means, which may or may not constitute lawful access.
What are the best practices for AI privacy?
The most important steps are: prohibit the use of consumer AI tools for sensitive workloads; require enterprise-grade AI tools with strong data protection terms; build a clear, tiered AI use policy; train employees to recognize what constitutes confidential information; and implement technical controls like automated redaction, access restrictions, and data loss prevention monitoring.
What types of information should never be entered into AI systems?
As a general rule, anything that would qualify as a trade secret, attorney-client privileged communication, unreleased product information, or personally identifiable information should not be entered into a publicly available AI tool. If you are uncertain whether a document crosses that line, ask legal before you act, not after.
How can businesses reduce AI privacy risks?
A combination of policy, training, technical controls, and contract review is the most effective approach. Companies should also conduct regular audits of how AI tools are being used internally and stay current on relevant privacy laws in their jurisdictions, which are evolving rapidly. For companies with proprietary datasets, negotiating strong protections directly with AI vendors rather than accepting standard terms is an underutilized tactic.
The balance between advancing AI capability and protecting the rights of information owners is one of the central legal challenges of our moment. Legislation is coming, and case law is developing faster than most people expected. Getting ahead of it with the right policies, the right contracts, and the right employee training is good risk management. It's also what protecting your clients actually requires right now.