Protecting proprietary algorithms in 2026: A strategic imperative

In 2026, proprietary algorithms are central to competitive advantage in tech, powering AI, analytics, and autonomous systems. Yet legal protection remains fragmented across jurisdictions. Patents offer exclusivity but are slow and limited in scope; copyright protects source code but not underlying logic. 

Trade secrets have become the preferred route, offering indefinite protection without disclosure – but require rigorous safeguards. Contractual mechanisms, including NDAs and licensing terms, are vital to reinforce IP rights. 

Jurisdictional differences complicate strategy: the EU AI Act imposes transparency obligations, the UK relies on flexible sector-led rules, the U.S. favours trade secret litigation, and China enforces strict algorithmic controls. Emerging risks – like model leakage, adversarial attacks, and cross-border data exposure – demand integrated legal and technical governance. 

Legal teams must align IP strategy with cybersecurity, product development, and compliance. In this landscape, protecting algorithms is not just a legal challenge – it’s a strategic necessity for innovation and resilience.

Securing the future: Protecting proprietary algorithms in the global digital economy

In today’s global digital economy, algorithms power everything from social media feeds to autonomous vehicle and enterprise AI agents, making protecting proprietary algorithms a critical concern for multinational corporations investing in agentic systems. Unlike open-source alternatives, proprietary algorithms are protected by intellectual property rights that create market differentiation. The emergence of agentic AI, which autonomously manages and executes complex tasks, has emphasised the vital role of these underlying algorithms. Given the fragmented legal landscape surrounding their protection, global organisations must implement sophisticated and multilayered strategies to secure their algorithmic assets.

Strategic and economic importance

Proprietary algorithms are specialised computational processes owned and controlled by their creators and encompass the logic, decision trees, and data processing methods that drive product functionality – from search rankings and pricing models to autonomous agent decision making and safety controls. Protection is essential because these algorithms often represent years of R&D investment and provide sustainable competitive advantages in the marketplace – algorithmic superiority often translates directly into business success.

Proprietary algorithms often enable unique capabilities – advanced reasoning, optimisation, and safety mechanisms – that differentiate products in highly competitive markets. Their strategic value lies not just in individual techniques they use but how they are precisely configurated and assembled into effective systems. Proprietary algorithms can also significantly increase a business's valuation, especially in tech-driven sectors where innovation speed and intellectual property protection directly impact market leadership and profitability.

Why proprietary algorithms matter in the age of agentic AI

The emergence of agentic AI has intensified the importance of proprietary algorithms. Gartner predict that by 2026, 40% of enterprise applications will leverage task-specific AI agents. As these agents begin making high-stakes decisions in commerce [read more] , healthcare, and financial services [read more], the underlying algorithms become the backbone of trust, compliance, and competitive advantage. 

Proprietary designs typically outperform open-source alternatives in speed, adaptability, scalability, and integrated safety features. Whilst open-source frameworks offer transparency and flexibility, proprietary systems deliver cutting-edge performance and alignment with ethical standards – critical attributes as AI agents operate with increasing autonomy.

Legal tools for protecting proprietary algorithms

There are three primary legal mechanisms for protecting algorithms: copyright, patents, and trade secrets. Each offers distinct advantages and limitations, and effective protection requires understanding when and how to deploy each tool:

• Copyright: automatically protects source code upon creation. However, its limitations for algorithms are significant. Copyright protection is only granted to original works of authorship and does not extend to ideas, methods of operation or mathematical concepts. Therefore, while it covers the specific code implementing the algorithm, it does not protect the underlying mathematical logic, conceptual framework, or functional approach – the elements that often provide the real competitive value.

• Patent protection: is achievable only in specific circumstances. Proprietary algorithms often rely on adaptive and dynamic processes rather than static processes, making it difficult to define and patent them clearly. Pure mathematical formulas cannot be patented, but algorithms embedded in technical solutions that solve real-world problems may qualify. The key requirements are novelty, non-obviousness, and demonstrable technical utility beyond mere mathematical principles. However, the patent process requires public disclosure and can take years, making it unsuitable for rapidly evolving AI technologies.

• Trade secrets: offer the strongest protection for most proprietary algorithms. To qualify, algorithms must derive economic value from secrecy, not be generally known, and be subject to reasonable confidentiality measures. This protection lasts indefinitely. Because proprietary algorithms often underpin critical business processes, companies typically implement multi-layered protection including access controls, employee training, and NDAs. Note that emerging AI regulations increasingly require transparency and explainability, which can conflict with the secrecy needed for trade secret protection. 

Approach of key jurisdictions to algorithm protection

As the global digital economy evolves, the protection of proprietary algorithms has become increasingly sophisticated, with legal frameworks adapting to address both technological advancements and cross-border enforcement challenges. Although core principles align across major markets, enforcement mechanisms and requirements vary significantly making a jurisdiction-specific approach to IP strategy critical.

Here's how some major jurisdictions approach algorithm protection:

United States

The source code of the proprietary algorithms is automatically protected under the U.S. Copyright Act. However, copyright covers only the code’s specific expression and not the underlying algorithmic method, logic or function.

The U.S. offers robust patent protection but with evolving constraints. AI patent applications have surged 33% since 2018, though recent case law has narrowed eligibility for abstract algorithms. Patent protection requires technical implementation that is novel and non-obvious, but purely abstract algorithms are not protected. 

If the algorithm derives independent economic value from being held as secret and the owner of the algorithm takes reasonable measures to keep it confidential, the U.S. provides statutory trade secret protection until such time as the algorithms secrecy is maintained under the federal Defend Trade Secrets Act 2016 and Uniform Trade Secrets Act. 

United Kingdom

Software is protected as a literary work under the Copyright, Designs and Patents Act 1988, and is protected automatically on creation of original code, though copyright protection is limited to the actual code and does not extend to any underlying processes or functions.

For patents, the UK follows similar principles to the U.S., excluding patent protection for mathematical methods and computer programs “as such”, and requiring algorithms to demonstrate a relevant technical contribution before qualifying for patent protection. The UK Supreme Court is also due to provide further clarity on the patentability of artificial neural networks in the next few months.[1] 

The Trade Secrets Regulations 2018 afford protection to algorithms with commercial value when reasonable secrecy measures are maintained, with wider common law breach of confidence protections also remaining available. 

European Union 

Copyright protection arises automatically throughout the EU on the creation of software code pursuant to the Software Directive (2009/24/EC) but only protects the specific expression of algorithms and not the underlying logic or functional ideas behind the code. 

Patentability of algorithms is not harmonised by EU legislation. However, most EU member states adhere to the framework established by the European Patent Convention and European Patent Office. Similarly to the position in the U.S. and UK, abstract mathematical methods and computer programs are not patentable unless they have technical character or solve a specific technical problem.

Protection for trade secrets is harmonised across the EU by Directive (EU) 2016/943 which offers indefinite trade secret protection for algorithms if companies take reasonable steps to maintain their secrecy. This makes trade secrets a critical tool for businesses seeking to protect algorithmic inventions that cannot be disclosed through patents. 

However, AI providers will need to navigate increasing transparency requirements under EU regulation. The EU AI Act establishes a tiered framework of transparency obligations based on an AI system’s purpose and risk profile. For high-risk systems in the EU, AI providers must disclose detailed technical information - such as design specifications, architecture, training and validation processes - and to walk a careful line to ensure compliance without any unintended disclosure of trade secrets to competitors.

Asia 

Key Asian markets such as Mainland China, Hong Kong SAR, Singapore and Japan generally align with Western approaches. Copyright protection is available for proprietary algorithms but is limited to the expression of code rather than the underlying functional logic. 

In all four jurisdictions, patent protection is available where algorithms are tied to a technical application with measurable effects. Abstract algorithms, or algorithms that are purely business methods, are not patentable.

Trade secrets remain the dominant mechanism for protecting proprietary algorithms. Generally, so long as the algorithm’s owner takes reasonable steps to maintain its secrecy and the algorithm itself has an economic value, then protection extends. Each jurisdiction may differ in how the protection is enforced, but the principle and framework of protection is consistent.

A multilayered protection strategy is needed

The legal framework surrounding algorithmic protection continues to evolve, shaped by technological advancement, regulatory developments, and judicial interpretation. Companies must remain vigilant and agile, adapting their IP strategies to address emerging challenges whilst leveraging established legal mechanisms to secure their most valuable computational assets. 

However, the threat of IP infringement is heightened. Sophisticated reverse-engineering techniques and model extraction attacks enable competitors or malicious actors to replicate algorithmic behaviour without direct access to source code. 

Companies should implement multi-layered strategies combining legal mechanisms with technical safeguards. This means leveraging trade secrets for sensitive model architectures and training data, selectively using patents for innovations that can withstand disclosure requirements, and reinforcing these protections through robust contractual frameworks such as licensing and non-disclosure agreements. 

At the same time, firms must implement secure-by-design principles and AI-specific security frameworks to counter cyber threats. Proactive monitoring and provenance tracking will help prevent IP leakage and unauthorised replication, ensuring that innovation remains both protected and resilient in an increasingly complex digital environment.