With the recent press release from SIM IP and Tangibly, a client recently asked me a simple but important question: how does litigation funding actually work?
The missing piece in trade secret enforcement
Litigation is expensive, and intellectual property litigation is among the most costly. Trade secret and patent disputes often take years and millions of dollars in legal fees, especially if the case goes to trial or appeal. Most small and medium-sized enterprises simply can’t afford the expense and time distraction of prolonged IP litigation making enforcement feel out of reach. This leaves it to the “big boys” to battle it out.
The rise of trade secret litigation and the funding gap
Trade secret litigation, however, is rising steadily year over year. While the total number of trade secret misappropriation cases remains smaller than patent litigation, the growth trend is significant.. For US federal district courts in 2023, about 3,700 patent infringement suits were filed, while only about 1,200 trade secret misappropriation cases were filed. Patent litigation has gradually declined over the past decade, while trade secret litigation continues to expand as companies place greater value on confidential information, data, algorithms, and know how.
While there are multiple patent litigation funding organizations, there are not funders specializing in trade secret disputes (until now!).
So how does litigation funding work?
In a typical litigation finance arrangement, a third-party provides capital to the plaintiff in exchange for a fraction of the award (if any). If the defendant wins, the third party does not receive anything (it is typically a non-recourse arrangement). This structure allows companies to pursue meritorious trade secret claims without bearing the full financial risk.
Is litigation funding a good option for trade secret plaintiffs to consider?
Absolutely! Plaintiffs who have a great fact pattern but insufficient funding to enforce can seek justice with the help of a litigation funder. Even large well-funded companies may want to distribute some of the risk even though they could fund the litigation entirely themselves.
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To learn more about trade secret litigation funding, trade secret enforcement strategies, and how AI driven trade secret validation supports these cases, contact Tangibly and SIM IP.
Why are programs and data considered trade secrets?
Because they contain algorithms, models, processes, and customer insights that provide competitive advantage. When exposed, the loss is immediate and irreversible.
What is the first step to protecting programs and data?
Create an inventory and identify which assets qualify as trade secrets, including code, models, datasets, prompts, and internal tools.
Why is classification important for trade secret protection?
Classification clarifies sensitivity, risk, and required controls. It strengthens governance and ensures teams understand how assets should be handled.
How does access control prevent trade secret loss?
Limiting access by role, tracking interactions, removing outdated permissions, and restricting repositories prevents internal misuse and accidental exposure.
How does AI increase the risk of trade secret leakage?
Employees often paste confidential information into public AI tools, causing it to become training data. Governance and private AI tools prevent this.
What legal agreements support trade secret protection?
NDAs, invention assignment agreements, contractor agreements, and offboarding processes ensure confidentiality obligations are clear and enforceable.
Why is employee training essential for protecting trade secrets?
Most exposure occurs through human behaviour. Training builds awareness and creates a culture of confidentiality across the organisation.
How do audits improve trade secret protection?
Audits identify weak points like outdated access, unsecured repositories, shadow AI use, and untracked datasets — key for legal defensibility.
How does Tangibly help protect programs and data?
Tangibly provides automated trade secret identification, classification, access tracking, governance workflows, and AI-driven risk detection.
