AI Algorithms as Trade Secrets: Hiding the Brains Behind the Machine

Introduction:

Any time you see a credit card transaction approved in a few milliseconds, an application reviewed by an algorithm that's never been read by a pair of human eyeballs, or your social media feed rearranged itself according to your preferences, an algorithm has made a choice. Algorithms aren't tools that gather dust. They're the corporate heartbeats of some of the planet's most profitable businesses, working in silence, earning quietly, and largely kept hidden away from public view. Should or can an algorithm be treated as a trade secret? It's a question that lies at the heart of some of the most fraught moments in the worlds of intellectual property law and AI governance.

In this article, we'll take a look at how the legal system for trade secrets is being adapted, contorted, and sometimes twisted to apply to algorithms. We'll look at existing frameworks in the United States and India, look at the increased conflict with regulations demanding transparency, and explore the very real practical challenges companies encounter when the intellectual property they value most is also the intellectual property regulators are desperate to access.

What Makes an Algorithm a Trade Secret?

So, what is a trade secret? Traditionally, it's any piece of commercial information that a company keeps secret. The definition generally accepted by most U.S. states through the Uniform Trade Secrets Act (UTSA) and the federal Defend Trade Secrets Act, 18 U.S.C. 1836 (“DTSA”), passed in 2016 by Congress, hinges on three elements. First, the information must gain an independent economic advantage from remaining secret. Second, the company must take reasonable steps to protect that secrecy. Third, the information shouldn't be common knowledge or reasonably accessible.

On their face, AI algorithms seem like a perfect fit for this definition. After all, the architecture of a trained machine learning model, its weights, engineers' tuning choices, and the pipelines used to create its behaviours can represent years of investment and create an undeniable competitive advantage. And patent protection, long the standard for software innovations, has become increasingly unreliable in this realm. Since the landmark 2014 Alice Corp. v. CLS Bank Int'l ruling, courts have generally deemed most software and algorithms to be "abstract ideas" and thus unpatentable. On top of that, even when a patent can be secured, it means the world has a roadmap to the company's technology, and there's always the possibility that patent litigation could drag on for years.

There's also another key difference when it comes to the nature of protection. Patents expire, and they require a company to reveal exactly what they're protecting. Trade secrets, on the other hand, can potentially last forever, as long as a company continues to protect the information and keeps reasonable security measures in place. The recipes for Coke and Google's search algorithm are prime examples; they’ve deliberately stayed out of the patent system for exactly that reason.

The Anatomy of Misappropriation: Waymo v. Uber and Beyond

When Anthony Levandowski left Waymo in December 2015, he took more than 14,000 design files with him. These files were for Waymo's LiDAR sensors and were worth 9.7 gigs of secrets. After that he started a company. Uber bought it. This led to a lawsuit called Waymo LLC v. Uber Technologies, Inc. The lawsuit ended in February 2018 with Uber paying $244.8 million in stock. Many people thought this was one of the awards for trade secrets in Silicon Valley.

This case taught us some things that other courts have used since then. First the design of a system and its software are both secrets that can be protected. Second, when a senior engineer takes files with him when he leaves a company, that is an example of taking something that does not belong to him. Most importantly, the case showed that the value of AI infrastructure can be very high and worth fighting for in court.

Recently courts have used this law in cases where employees leave a company. Courts want companies to be clear about what they are suing for. In the case of Modus v. Williams-Arowolo in 2023, a court in Texas did not accept trade claims that were described with phrases like 'artificial intelligence', 'machine learning' and proprietary'. The court said that these phrases are not enough to prove that a trade secret was taken. This is a point: companies need to say exactly what parts of their AI infrastructure are secrets, not just say that their technology is cool and advanced. Companies need to be specific about what they're claiming as trade secrets and what makes those secrets so valuable to their AI infrastructure and their business.

The Transparency Problem: When Regulators Want to See Inside

But here's where things get a little messy. On both sides of the pond, the general direction is toward more transparency when it comes to AI systems, especially those that operate in areas of high impact. The EU AI Act (Regulation (EU) 2024/1689) entered into force in August 2024, categorising systems that conduct credit scoring, filter employment applications, and operate critical infrastructure as high-risk. Deployers of these systems are required to provide "clear, complete and correct" documentation that should "include the training methodology, the relevant accuracy metrics and details of the data sets used."

And this is where the transparency vs secrecy debate among practitioners begins. Trade secrets are valuable in that they are secret and they are secret by nature. The EU AI Act's push for transparency forces developers to document aspects of their systems that, if released to a competitor, would amount to giving up their keys to the kingdom. This includes the model weights, system architecture, and the inner algorithmic logic, otherwise known as your secret sauce.

Across the pond in the U.S., we're seeing a similar issue. Federal employees are prohibited from releasing trade secrets under the Trade Secrets Act (18 U.S.C. 1905). However, they can be forced to do so if such release is "authorised by law". With the move toward AI transparency, a future federal mandate for release would remove the protective shield that companies had believed they had.

We're already seeing the impact of this in lawsuits. Take The New York Times v. OpenAI and Microsoft, for example. In this copyright infringement case, the plaintiffs are requesting discovery of Open AI's training data and model documentation, which the company argues are core trade secrets fundamental to its competitive position. This case clearly illustrates the clash between legal transparency obligations and trade secret secrecy, regardless of the outcome.

India’s Fragmented Framework: A System Under Strain

Indian tech companies and legal practitioners find themselves in an even trickier spot because there's no dedicated law for trade secrets in India. Instead, algorithmic IP rights are handled under various bits and pieces of laws, including the Indian Contract Act of 1872 (which enables NDAs and confidentiality agreements); the Information Technology Act of 2000 (which has sections on computer offences, including Section 72 dealing with breaches of confidentiality); and equity jurisprudence based on common law for breaches of confidentiality.

There's a gaping hole in all of this. As of yet, there hasn't been any recorded court case in India where an AI algorithm has been held to be a trade secret.

Luckily, the 22nd Law Commission of India has identified this gap in its report on trade secrets and economic espionage that was published in March 2024 and has recommended the Protection of Trade Secrets Bill, 2024, a new law which would create a distinct regime. The "protected information" is so widely defined in Section 2(f) of the bill to include algorithms and AI systems, and Section 7 of the bill sets out the remedies. Upon its enactment, Indian courts will have a clear legal footing to pursue misappropriation of algorithms rather than looking to laws that were written for a completely different set of problems.

This lack of a dedicated law has real-world consequences. With no dedicated legislation, companies in India are left to their own devices to use contracts like NDAs, non-compete agreements (some of which have been held by Indian courts to be invalid as restraint of trade), and employment contracts. If an employee absconds with model weights or training code, a company must rely solely on the efficacy of its contractual documents and whether the actions are covered under computer offences sections of the IT Act. This leaves a large gap for a country aiming to become a leader in AI across the globe.

The Accountability Question: Who Pays When the Black Box Gets It Wrong?

Can we talk about AI trade secrets and accountability? Because you really can't separate the two. Think about it: when AIs start making high-stakes decisions, like whether someone gets a loan or how long they spend in jail, the "black box" effect that trade secret law enables becomes a thorny ethical and legal issue in ways that guarding a Coke formula never will.

The EU has tried to thread this needle by putting high-risk AI applications into their own category with transparency requirements, but meanwhile, they also have an AI Trade Secrets Directive (2016/943) that allows businesses to protect their algorithms in the commercial sphere. German courts have also chipped away at the idea that algorithms are always a free-for-all, and a 2024 Supreme Court ruling (KVB 69/23) applied a "proportionality" standard to the disclosure of trade secrets in commercial matters. This meant that any compelled disclosure has to be "suitable, necessary and appropriate", balancing the public interest with a company's constitutionally protected trade secrets.

But how can regulatory bodies gain access without this becoming public information? One possible answer is a model that the European Securities and Markets Authority has put in place, with special regulatory access facilities. Here, qualified technical experts can look at confidential trading algorithms while bound by strict nondisclosure agreements. No examination means no public disclosure. It's the kind of structure where regulators can see what they need to see without the downside of all of that sensitive information spilling into the marketplace, and it might just provide a blueprint for other sensitive AI applications.

Practical Considerations for AI Companies

So, for businesses that use private AI systems, there are a few things that can be gleaned from the legal environment discussed previously:

First, there can be no wiggle room on specificity. Companies have found that judges tend to laugh their claims out of court when they try to make vague, generalised assertions about their AI as a trade secret. If you want your AI to be a trade secret, you need to be able to point, specifically, to the parts that are the secret. Document it internally. It helps in a legal battle and in terms of restricting who can do what to your model internally.

Second, you have to be able to prove reasonable effort to keep it secret. To be considered a trade secret, you've got to have done your due diligence and made reasonable attempts to keep it hidden. When we're talking about an AI system, this can mean putting some guardrails in place, such as access controls, secure development environments, employee agreements and policies stating that any outputs from a given model cannot be used for competing products.

Third, regulatory compliance and secrecy go hand in hand. With the EU AI Act and other such legal frameworks demanding more transparency, companies will have to disclose more about their AI systems than they ever have before. This makes meticulous tracking of any and all disclosures you've made (which regulators you made them to, under which agreements and NDAs) an essential part of doing AI business from a legal perspective.

Finally, AI Reverse Engineering. The threat of AI model output being used as a basis for creating new competitive models is becoming very real. Increasingly, terms of service are popping up that specifically forbid such uses. When the user violates these terms of service, courts have increasingly deemed this to be a "breach" that is a means of improper trade secret acquisition. If you're doing any kind of AI work, pay close attention to this developing trend.

Conclusion

Let's put it this way: the AI algorithm is a new breed of asset (in business speak) that offers tremendous value, erodes through exposure, and doesn't fit easily into intellectual property (IP) regimes from the last technological epoch. Though imperfect for this purpose, trade secret law remains the best and most prevalent protection tool available.

The issue, however, is that this push and pull between protecting the algorithm and revealing information can't be a win/lose scenario. The opacity trade secrets offer carries legitimate risks, especially when used to obscure the crucial decisions being made. The transparency the regulators are pushing for, likewise, can lead to competitive firms getting access to valuable proprietary tech. In the EU, the US, and even tentatively in India, legal scholars are trying to strike that balance through tests of proportionality, ways of giving regulators safe access, and disclosure requirements based on specific types of algorithms.

As an IP practitioner, you should expect to see both litigation and legislation unfold around this area in the next 10 years. Businesses that view their algorithms as trade secrets from the outset-keeping meticulous records, protecting them fiercely, and establishing compliance processes around the obligations of their disclosure will be far better prepared than those who wait for the law to work things out for them.

Author: Vansh Chouhan, in case of any queries please contact/write back to us via email to chhavi@khuranaandkhurana.com or at  Khurana & Khurana, Advocates and IP Attorney.

Endnotes:

1. Defend Trade Secrets Act, 18 U.S.C. § 1836 (2016), enacted to provide a federal civil cause of action for trade secret misappropriation in the United States.

2. Uniform Trade Secrets Act (UTSA), National Conference of Commissioners on Uniform State Laws, 1985, adopted in various forms by the majority of U.S. states.

3. Alice Corp. Pty. Ltd. v. CLS Bank International, 573 U.S. 208 (2014), wherein the U.S. Supreme Court clarified the patent eligibility of software-related inventions.

4. Waymo LLC v. Uber Technologies, Inc., No. 3:17-cv-00939 (N.D. Cal. 2017), settled in February 2018 with Uber agreeing to transfer approximately USD 244.8 million in equity to Waymo.

5. Modus Create, LLC v. Williams-Arowolo, No. 4:22-cv-00743 (S.D. Tex. 2023), emphasizing the requirement that plaintiffs identify alleged trade secrets with reasonable specificity.

6. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on Artificial Intelligence (EU AI Act), entered into force on 1 August 2024.

7. Law Commission of India, Report No. 289: Trade Secrets and Economic Espionage (March 2024), recommending the enactment of a dedicated Protection of Trade Secrets legislation in India.

8. Directive (EU) 2016/943 of the European Parliament and of the Council of 8 June 2016 on the protection of undisclosed know-how and business information (trade secrets) against their unlawful acquisition, use and disclosure.