CIOs must rethink intellectual property protection in the age of AI coding
By Christian Stephan, Sr Director Analyst at Gartner
Organisations are rapidly adopting generative AI and autonomous coding tools to increase developer productivity, accelerate software delivery and improve operational efficiency. As AI assumes a larger role in software development, developers are increasingly shifting from writing code to supervising, orchestrating and reviewing AI-generated outputs. This evolution promises significant productivity gains, but it also introduces a challenge many organisations have yet to fully recognize: the potential weakening of intellectual property (IP) protections for some of their most valuable digital assets.
Current intellectual property frameworks are largely built around the concept of human authorship.
While AI can generate software, designs and other digital assets at unprecedented speed, legal protections do not automatically extend to content produced entirely by machines. As organisations increase their reliance on AI-generated code, questions around ownership, copyright eligibility and long-term protection of proprietary innovations are becoming increasingly important.
The challenge is amplified by the pace at which AI is being integrated into software development. AI is already contributing a growing share of newly created code and accelerating code rewrites across enterprise environments. As AI-generated content becomes more deeply embedded within software portfolios, organisations may find it increasingly difficult to distinguish between human-created and AI-created components unless appropriate governance and documentation practices are established early. Gartner predicts by 2032, more than 60% of critical corporate code will not be protected as intellectual property due to AI coding.
For CIOs, this creates a strategic dilemma. The same AI capabilities that can enhance productivity and accelerate innovation can also introduce uncertainty around ownership and protection of corporate intellectual property. Without clear safeguards, enterprises risk unintentionally reducing the legal protections associated with software assets that have historically served as sources of competitive differentiation.
The Business Implications of AI-Generated Code
The risks associated with AI-generated code extend beyond questions of legal interpretation. As organisations increase their reliance on AI-assisted development, they risk unintentionally weakening the intellectual property protections around some of their most valuable software assets.
One of the most significant risks is the commoditization of core assets. Code, product designs and other digital deliverables generated entirely by AI may not qualify for intellectual property protection because they lack sufficient human authorship. In such cases, organisations may find it difficult to prevent clients, employees or competitors from copying, modifying or redistributing assets that were previously considered proprietary. For enterprises whose competitive advantage is built on software, the erosion of ownership rights could transform years of investment and innovation into assets that others can freely use.
Organisations may also face a loss of customer moats. For software providers, proprietary code often serves as a source of customer retention and differentiation. However, when unprotected software is delivered to customers, those customers may be able to replicate or repurpose the code without restriction. This may lead to enterprises losing the exclusivity that has traditionally underpinned customer relationships and competitive positioning.
AI-enabled modernization initiatives present another set of challenges. Many organisations are using AI to accelerate application modernization by translating legacy systems into newer programming languages. Yet these translations are generally treated as derivative works rather than new creations. As a result, the translated code inherits the legal status of the original software. Any licensing obligations, third-party dependencies or compliance issues embedded in legacy applications can therefore be carried forward into modern environments, creating new risks while giving organisations no additional intellectual property protection.
Building an IP Protection Strategy for the AI Era
To address these emerging risks, CIOs should establish a modern intellectual property protection framework that combines governance, technical controls and contractual safeguards. The first priority is understanding the current state of enterprise software assets. organisations should audit existing codebases and document human contributions before AI-generated content becomes more pervasive and more difficult to trace. Maintaining visibility into the origin of software assets will become increasingly important as AI adoption expands.
CIOs should also carefully classify software assets based on their commercial value, strategic importance and business criticality. For algorithms and capabilities that create competitive differentiation, organisations may choose to limit the use of AI coding agents and maintain detailed records identifying which portions of code were created by humans and which were generated by AI. Documentation of authorship and provenance can play a critical role in supporting future intellectual property claims.
Technical controls should complement governance efforts. organisations can reduce exposure by applying code-hardening and obfuscation technologies that make reverse engineering more difficult.
They should also consider architectural approaches that keep critical business logic and proprietary workflows on the server side, preventing direct access to valuable source code. These measures can help preserve competitive advantage even as software delivery models evolve.
Contractual protections are equally important. Enterprises should establish strong nondisclosure agreements, usage restrictions and contractual safeguards with customers, suppliers and partners when proprietary code is shared. Source-code escrow arrangements can provide controlled access in exceptional circumstances while helping organisations maintain protection over critical assets. CIOs should work closely with legal teams to ensure contracts and governance policies reflect the changing realities of AI-assisted software development.
Finally, organisations should strengthen their overall approach to intellectual property protection by considering mechanisms such as trade-secret protections, digital watermarking and alternative intellectual property frameworks where appropriate. As AI continues to reshape software development, preserving evidence of human contribution and implementing layered protection strategies will become increasingly important to safeguarding enterprise innovation.