An American Bar Association survey is telling: 75% of companies anticipate a spike in trade secret theft because of generative AI by 2027. This is happening right now. It forces companies to get serious about combining legal and tech defenses to protect their most valuable intellectual property.
Key Takeaways
- Establish firm data governance and clear rules for employees using AI tools. This is your first line of defense against accidental trade secret leaks.
- Run regular audits on all generative AI use, both your own tools and third-party ones, to find and plug the holes in your trade secret defenses before they become a problem.
- Scrutinize your vendor contracts for any AI service. They must spell out exactly who owns the data, your confidentiality rights, and who is liable for a breach, ensuring it lines up with Georgia’s Uniform Trade Secrets Act (O.C.G.A. Section 10-1-761).
- Have a response plan ready for when you suspect a trade secret has been stolen via AI. This plan needs to include who to call for forensic analysis and how to get your lawyers involved immediately.
The Alarming Rise of Inadvertent Disclosure: 68% of Employees Use Generative AI at Work
Generative AI tools are now everywhere in the workplace. A 2025 Gartner study found that 68% of employees are using generative AI tools for work-related tasks, and most of the time it’s happening without any company oversight. While that sounds good for productivity, it blows a huge hole in traditional trade secret protection. Well-meaning employees paste proprietary code, sensitive client lists, or unique product designs into these models to get a summary or generate content. The problem is that many of these models learn from their inputs. Developers might talk about data anonymization, but the risk that your proprietary information gets baked into the model’s training data, and spit back out to another user later, is very real. Our firm is seeing a big increase in calls about this, especially from our tech clients in the Roswell corridor near Holcomb Bridge Road, who are realizing the threat is now embedded in their team’s everyday workflow.
“Hallucinations” and Data Contamination: A 40% Increase in AI-Generated Misinformation
Generative AI models are famous for “hallucinations”, making up plausible but wrong information. This phenomenon has direct, severe implications for trade secrets. A recent National Institute of Standards and Technology (NIST) report found a 40% increase in AI-generated misinformation inside company systems in just the last year, with some of it involving manipulated proprietary data. What if an employee asks an AI to summarize a confidential project plan, and the model “fills in the gaps” with invented details that are then treated as real? Or it mashes together parts of several secret documents, creating a hybrid that exposes sensitive pieces of multiple projects. The integrity of your core IP is what’s at stake. When an AI blends actual trade secrets with fabricated junk, detecting the theft and proving what was misappropriated becomes incredibly difficult. We tell clients to have strong validation protocols for any AI output and to understand how broadly Georgia’s Uniform Trade Secrets Act (O.C.G.A. Section 10-1-761) defines “misappropriation,” which could easily cover these AI-driven disclosures.
The Vendor Risk Multiplier: Over 50% of Companies Use Third-Party Generative AI Solutions
Most organizations are buying, not building, their generative AI tools. According to a 2025 Deloitte survey, over 50% of companies are relying on third-party generative AI solutions, from massive language models to small code generators. Each vendor is another potential point of failure for your trade secrets. How are they securing your data? What are their retention policies? Do their terms of service actually prevent them from using your confidential information to train their own models? The contracts we review are often completely inadequate on these points. A standard NDA is not enough. You need specific clauses that cover AI model training, data handling, audit rights, and clear liability if the AI service itself is the source of a breach. The law in this area is still being written, and taking a “wait and see” approach is a huge gamble with your company’s most valuable assets. We regularly work with businesses in the Fulton County business district to redline these agreements so they don’t sign away their IP protection.
The Exploding Cost of Remediation: Average Data Breach Cost Hits $4.45 Million
The price tag for a trade secret breach involving AI is horrifying. IBM’s 2025 Cost of a Data Breach Report puts the average cost of a data breach at $4.45 million globally. When it’s your trade secrets, the costs balloon from loss of competitive advantage, legal fees for getting an injunction, and damage to your reputation. Breaches caused by AI are uniquely damaging because the leak may not be a single event, but a slow, quiet erosion of your proprietary knowledge that gets woven into countless other outputs. Proving the source and scale of the theft gets much harder, which means longer legal fights and higher costs. The specialized forensic work needed to trace AI disclosures just adds another bill to the pile. That number proves that proactive protection is an essential investment against a potentially catastrophic loss.
Countering Conventional Wisdom: “AI Will Make Trade Secrets Obsolete” is Flawed
There’s an argument going around some tech circles that generative AI will eventually make trade secrets obsolete. The idea is that if AI can create new things so fast, the value of any one secret goes down. This thinking completely misunderstands what a trade secret is and what AI can actually do. AI accelerates things, but it still needs foundational knowledge, unique data, and human creativity to make something truly valuable. We see this with our clients who are building modern legal tech. The very process of using AI creates new, protectable trade secrets. The true value is in their unique development methods, their data curation techniques, and even the specific prompts they write to guide the models. The challenge is adaptation. The legal framework under the Georgia Trade Secrets Act of 1990 is still strong, but the “reasonable measures” you must take to protect your information has expanded to include AI governance.
Generative AI is a permanent part of business, but it introduces serious risks to your trade secrets. Having proactive legal strategies, strict internal policies, and continuous monitoring are fundamental to safeguarding your intellectual property in this new field. For companies handling sensitive data, understanding how to protect against security negligence risks is a must. You must be diligent about data security, much like you would be about avoiding vehicle liability risks in your physical operations. When you’re dealing with specialized fields like those governed by Roswell Biohazard regulations, the need for solid data governance becomes even more intense because of the information’s sensitive nature.
What is a trade secret in the context of generative AI?
Under O.C.G.A. Section 10-1-761, a trade secret is information, like a formula, program, method, or process, that has economic value because it’s not public knowledge and is subject to reasonable efforts to keep it that way. When it comes to generative AI, this can mean your proprietary training datasets, unique prompt engineering methods, internal AI model designs, and even specific outputs generated by an AI that give you a competitive edge.
Can an employee accidentally disclose a trade secret using generative AI?
Yes, it happens constantly. An employee might paste sensitive company information into a public AI tool to summarize text or generate code, not realizing the AI model might learn from those inputs. If that proprietary data is later exposed or used by the model, it can be an accidental disclosure that leads to a misappropriation claim.
What steps can companies take to protect trade secrets when using third-party generative AI services?
You have to make sure your vendor contracts spell out data ownership, confidentiality, and liability for any breaches. The key clauses you need should cover data anonymization, strict limits on using your company data for their model training, your right to audit them, and clear rules for deleting your data when the contract ends. We always advise clients to have legal counsel review these agreements.
What is the role of an AI governance policy in trade secret protection?
An AI governance policy sets the ground rules for how your employees can use AI tools, especially when proprietary information is involved. It needs to define what kind of data is okay to input, what the review process is for AI-generated material, and include training on the dangers of accidental disclosure. This policy is a key piece of showing you’ve taken “reasonable measures” to protect your trade secrets.
How does Roswell legal tech assist with generative AI trade secret risks?
Roswell-based legal tech firms like ours have the specific experience needed to draft AI usage policies, review complex AI vendor contracts, and handle litigation for trade secret theft involving generative AI. We also help with the forensic investigation to figure out where an AI-related leak came from and how far it spread, helping clients build a defense strategy that fits the unique problems of this technology.
