AI IP Strategy: A Practical Playbook

Quick answer

This playbook gives a short, clear path to protect and use AI intellectual property. Follow four steps: audit assets, pick protection, set governance, then monetize and defend. Download a sample AI IP governance policy to start.

Download a sample AI IP governance policy

Why AI IP matters now

AI is core product and core research for many companies. That makes AI and intellectual property a top business risk and a big chance to build value. Firms with strong AI IP can win in the market, get higher valuations, and open licensing deals. Firms without clear IP plans face lawsuits, lost deals, and confused ownership of AI outputs.

For a broader view on how AI changes IP practice, see the industry take at Clarivate and a legal framework overview at Ocean Tomo.

Who should use this playbook

• CTOs and product leaders deciding protection strategy.
• In-house counsel building AI governance policy.
• Founders preparing for due diligence.
• Engineers and researchers who need rules for data and inventorship.

The 4-step AI IP playbook

1. Step 1 — Audit your AI assets

   List what you own and what you rely on. Include models, training datasets, labeled data, pipelines, documentation, code, and third-party models you fine-tune. Ask plain questions:

   • Who created this asset? Human, contractor, or vendor?
   • What data trained the model? Do we have rights or licenses?
   • Is the model reproducible from public components or unique to us?
   • Would sharing this asset make competitors stronger?

   Use automated tools for code and model provenance checks. For fast reading on legal risk tied to training data, read the WIPO summary at WIPO: Generative AI and a practical legal brief at Caldwell Law.

2. Step 2 — Choose protection: patent vs trade secret vs copyright

   Decide how to protect each asset. Use this simple table to guide choices.

   Asset type	Best fit	Why
   Novel algorithm with clear technical step	Patent	Grants exclusion rights; good if you can describe invention and plan to publish or license
   Large, unique training dataset or labeling process	Trade secret	Hard to reverse-engineer; protects value without public disclosure
   Model output (text, images)	Depends — contract + copyright where human authorship exists	Legal status varies; clarify in contracts and user terms
   Internal tooling, pipelines	Trade secret or copyright	Keep private and control access

   Decision notes: Patents are public but strong for core technical advances. Trade secrets work when you can keep things confidential. Copyright may apply if a human authors the final work. For deeper legal context, see Norton Rose Fulbright.

   Quick rule: Patent when the invention is novel and core to your product road map. Use trade secrets for datasets, training recipes, and operational practices you can keep private.

3. Step 3 — Build governance and training-data rules

   Good governance stops problems before they start. At minimum, create:

   • An acceptable use policy for generative AI tools used by staff.
   • Data sourcing rules: record licenses, consent, and redaction steps.
   • Inventorship and contribution rules: how to record human steps that create outputs.
   • Access controls and logging for models and datasets.
   • Review gates before publishing model outputs or shipping features.

   Use a standard playbook approach: who, what, when. For legal guidance on differentiating human and AI contributions, read IP Protection in the Age of AI and policy perspectives at Harvard Law.

   Sample checklist for governance:

   • Record training data lineage for every model.
   • Approve third-party models before fine-tuning.
   • Add IP and indemnity clauses to vendor contracts.
   • Train teams on IP and copyright basics for AI outputs.

4. Step 4 — Monetize, monitor, and defend

   Make IP work for you. Options include licensing models, data partnerships, and patents for core tech. Also set up monitoring and enforcement:

   • Monitor market and competitors for copied models or outputs.
   • Keep clear chain of custody for IP claims (inventor notes, commits, experiments).
   • Use trademark and copyright where relevant for branding and content.
   • Prepare licensing templates and NDAs for partners.

   For market and valuation angles, see how firms use IP to gain edge at Kantar and Ocean Tomo's view on monetizing AI as IP at Ocean Tomo.

AI IP quick checklist

• Inventory all models, datasets, code, and docs.
• Label ownership, contributors, and licenses.
• Decide patent vs trade secret per the table.
• Draft an AI governance policy and acceptable-use rules.
• Add IP clauses to vendor and hiring contracts.
• Log human authorship for model outputs you want to copyright.
• Set up market monitoring for infringement.

Common questions

Can you patent an AI model?

Yes, you can patent technical inventions that use AI if they meet novelty and non-obviousness rules. Patenting the model weights or a raw dataset is harder. Patent the unique method or system that gives a technical benefit.

Who owns AI-generated work?

Ownership depends on how the work was made and on contracts. If a human created the prompt and revised the output, copyright is more likely. If the output came fully from a tool with no human authorship, protection is unclear and varies by country. For global guidance see WIPO.

How do we avoid training-data infringement?

Keep records of data sources, get licenses when needed, and prefer curated or internal data. If you use public data, verify terms and document your risk decisions. See legal discussions at Harvard Law.

Next steps (7-day plan)

1. Day 1–2: Run a light audit and list top 10 AI assets.
2. Day 3: Map each asset to patent/trade secret/copyright choice.
3. Day 4: Draft or adopt the AI governance policy template linked above.
4. Day 5: Add IP clauses to current vendor and hiring templates.
5. Day 6–7: Train teams and start a monitoring plan.

Parting thought

AI IP is both legal and strategic. Tackle it with clear inventory, simple rules, and a plan to protect or share value. If you want a practical starting point, use the linked policy template and run the 7-day plan. Compared with doing nothing, you gain clarity, lower risk, and new business options.

Further reading: Clarivate on AI in IP practice, Ocean Tomo on AI as IP, and Kantar on proprietary IP.