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This article forms one part of a broader decision framework for evaluating whether patent or trade secret protection is appropriate for AI innovation.
The framework is designed to help decision-makers (e.g., innovators, in house, CTOs) align patent and IP strategy with underlying business realities and moving beyond purely "legal" considerations.
How an AI system is commercialized and deployed affects whether patent protection is appropriate. In many cases, the degree of control retained by the provider over the deployed system acts as a counterweight to other considerations that might otherwise favor trade secret protection.
Where the deployment model is customer-controlled, such as when the innovation is licensed for use to the customer, sold as a stand-alone product or deployed within a customer’s own IT environment, patent protection can play an important role. These delivery models necessarily expose the technology to customers or integration partners, increasing the risk that key aspects may be accessed, replicated or reused. In such cases, patents provide enforceable rights that extend beyond contractual use restrictions and confidentiality obligations.
By contrast, where the deployment model is provider-controlled, such as when the AI is offered as a hosted SaaS service or used internally within the company, access to the underlying implementation is more tightly controlled. In these scenarios, trade secret protection may be more suitable for certain aspects of the technology, particularly where customers interact only with outputs rather than the system itself.
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Examples: Business Delivery Model
- Patents (Customer-Controlled Deployment): Financial Risk-Scoring AI Platform
An AI risk-scoring platform that analyzes transaction data to generate fraud or compliance scores and is deployed within a financial institution’s own IT environment.
Under this customer-controlled deployment model, the platform is licensed to third-party financial institutions for local installation and operation on their internal training data repositories.
Because the developer does not retain operational control over the deployed system, patent protection plays an important role in protecting the core technology once it is transferred to customers for independent use within their IT infrastructure.
- Trade Secrets (Provider-Controlled Deployment): Logistics AI Optimization for Manufacturing Workflows
An AI-based supply chain visibility platform offered to third-party customers as a centrally hosted SaaS service. The platform ingests logistics and operational data from multiple customers to provide customer-specific real-time insights, forecasting and alerts. The AI models and core system logic remain fully controlled and operated by the provider.
In this example, customers may interact with the service through dashboards and APIs but do not receive access to the underlying models or deployment environment.
Because the provider retains control over the AI and its execution, exposure to competitors is limited, making this deployment model more conducive to protecting key aspects of the innovation as trade secrets.
- Hybrid Approach (Patents/Trade Secrets): Retail E-Commerce AI Recommendation Engine
A company develops an AI-based recommendation engine for the retail e-commerce industry using a hybrid delivery model that combines customer-controlled and provider-controlled elements.
The core recommendation engine is licensed to merchants and integrated into their online storefronts, where it operates within the merchant’s environment to generate personalized product recommendations based on local user behavior and product data. This customer-controlled deployment exposes the system architecture and integration interfaces to third parties, making this component well suited to patent protection and external licensing.
At the same time, the company retains a provider-controlled AI system that operates centrally across the platform. This internal system analyzes aggregated interaction data across multiple merchants to identify platform-level performance patterns and guide ongoing product development. Because this functionality is never deployed to customers and derives its value from cross-merchant aggregation under the provider’s control, it is not offered for licensing and is better protected as a trade secret.
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Applying the Decision Tool: Patents or Trade Secrets
For a further discussion of the decision framework and remaining decision factors in the framework, please see the following:
Framework: Patents or Trade Secrets
Factor 1: Nature of AI Innovation
Factor 2: Enforceable Scope of Patent Protection
Factor 3: Reproducibility of AI Innovation
Factor 5: Commercial Longevity
Factor 6: Competitor Defensive Positioning
Factor 7: Patentability Potential and Layered Strategies
If your organization needs assistance evaluating which aspects of its AI innovation are better suited to patent protection versus trade secret protection, our team can help. Our team can also support patent filing and the development of a broader IP strategy.
Bennett Jones