AI, Data & Intellectual Property Legal Strategy
AI companies face IP challenges that didn't exist five years ago: who owns model outputs, who has rights to training data, and how do you protect a competitive advantage that lives in weights, not in patents. We advise AI and software startups on building IP protection that holds up.
What we cover
- IP ownership and assignment (PIIA, contractor agreements)
- Software copyright and trade secret protection
- Trademark registration in US, Argentina and key markets
- Training data rights and dataset licensing
- AI output ownership and documentation strategies
- LLM vendor contracts and API terms review
- GDPR / CCPA / Ley 25.326 compliance
- Privacy policies, DPAs and cookie frameworks
- Open source compliance (GPL, MIT, Apache license obligations)
- Technology licensing and SaaS agreements with IP protections
The IP challenge for AI companies
AI-generated outputs, model weights and training datasets don't fit neatly into traditional IP categories. Investors and acquirers will ask hard questions about what you actually own — and the answers need to be documented.
Using data scraped from the web or licensed datasets without careful rights analysis creates litigation risk and can block enterprise deals. We audit training data provenance and structure proper licensing.
Enterprise clients increasingly require GDPR/CCPA compliance, DPAs and data security certifications before signing. We build the compliance infrastructure that enterprise deals require.
Why Kaplan for AI & IP
We advise AI companies on the legal challenges specific to their technology — not a generic IP practice adapted to fit. We understand the difference between protecting a model, a pipeline and a dataset.
AI startups operating across jurisdictions face overlapping data regimes. We implement GDPR, CCPA and Argentine data law compliance as a unified framework, not three separate projects.
We structure IP protection to support fundraising and M&A — investors and acquirers assess IP as a core value driver. Clean ownership, documented trade secrets and comprehensive assignment agreements make your IP defensible.
Frequently asked questions
Who owns AI-generated content?
Ownership of AI-generated content is unsettled and jurisdiction-dependent. In the US, the Copyright Office has taken the position that purely AI-generated works (with no human creative selection or arrangement) are not copyrightable. The output of AI tools used as instruments by human creators may be protected. For software startups, the key is to structure your AI pipeline so that human creative choices are documented, and to use IP assignment agreements that expressly cover AI-assisted output. We advise on how to structure this for maximum protection.
What IP rights does a software startup need to protect?
Software startups typically need to protect: (1) Source code — via trade secrets and copyright; no registration required but PIIA agreements with all contributors are essential. (2) Brand — trademark registration in key markets before scaling. (3) Proprietary data and datasets — via trade secret protection, database rights and contractual restrictions. (4) Algorithms and models — trade secret protection is often more practical than patents for AI. (5) Domain names and handles — register early. The first priority is always ensuring the company actually owns what its employees and contractors built.
What are the key data compliance requirements for SaaS startups?
The core requirements depend on where your users are: GDPR applies if you process data of EU residents (regardless of where your company is based); CCPA/CPRA applies to California residents. Both require a privacy policy, data processing agreements with vendors, a lawful basis for processing, and breach notification procedures. For SaaS companies handling enterprise clients, a DPA (Data Processing Agreement) is usually required before the contract is signed. Argentine startups also need to comply with Law 25.326 for local data. We implement compliant privacy infrastructure as part of our legal infrastructure service.
How do you protect AI models and training data legally?
AI model protection combines multiple layers: (1) Trade secrets — keep model architecture, weights and training methodology confidential; have NDAs and access controls in place. (2) Copyright — code used to build and train the model can be protected, though the trained weights themselves occupy uncertain legal territory. (3) Contractual protection — license agreements that restrict reverse engineering, model extraction and distillation. (4) Training data — ensure you have rights to use all training data (licensed datasets, contractual permissions, or fair use analysis); data provenance documentation is increasingly important for enterprise deals and regulatory compliance. (5) Patents — strategic in limited cases for novel methods.
Assess your AI legal risks
Tell us about your AI product, data sources and target markets — we'll identify the IP and compliance gaps that need to be addressed.
Get your quote now
Tell us about your AI product and data practices — we'll assess your IP and compliance exposure.