At Solve Intelligence, we've worked with hundreds of IP teams globally to roll out AI patent software. This means we’ve spent a lot of time in the unglamorous (but essential) parts of adoption: AI security reviews, confidentiality assessments, and structured pilots. The goal here isn’t to scare you away from AI - it’s to help you adopt it in a way your clients, your firm, and your regulators are comfortable with.
Patent workflows are uniquely exposed because they often involve non-public technical information, and because of the time-sensitive first-to-file nature of the patent system. In addition to the usual risks for client-attorney work, loss of confidentiality in patent workflows carries the additional risk of loss of IP rights and preventing the grant of a patent.
1) Invention disclosures and trade secrets
These are some of the highest-risk inputs. If these leak, or are retained in the wrong system, it can result in significant commercial harm.
2) Client confidential business information
Even if the technical content is already public, patent matters routinely include confidential business context and internal decision-making, such as infringement targets.
3) Personal data
Patent files can include personal data (inventor names, emails, home addresses) and sometimes sensitive personal data depending on the matter. That triggers privacy and data protection obligations, not just professional secrecy.
4) Cross-matter contamination
A subtle (but real) risk: attorneys work across many clients and matters. If an AI tool retains conversational history, “memory,” or embeddings across projects, you can accidentally blend confidential context from different clients.
Most teams focus on the visible interaction (“I entered text, it generated a draft”). The risk often lives in the invisible layers: training, retention, logs, human review, and subprocessors.
Inference (processing your input to generate an output) is inherent to how AI tools work.
However, you need a clear, written statement about whether customer data is used for training, and whether you can contractually prohibit it.
Even without training use, prompts, uploads, outputs, logs, and support workflows may be retained or accessed.
Confirm: what’s retained by default; whether retention can be shortened/disabled; who can access data (including vendor/support); and whether access is logged and least-privilege.
For many firms and in-house teams, the decision comes down to “where is the data processed and stored, and under what security controls?”
Make sure you understand:
See our Data Residency Explained guide for more information.
If you want a repeatable process that you can sign off on, start with a framework for how to manage data, workflows, and controls.
For a step-by-step rollout playbook, read our guide for AI adoption and firm rollout.
A simple data tier model helps teams move fast without constant escalations.
Example data tiers for patent work can include:
Treat tiering as matter-specific, and make Tier 3 ‘default no’ unless you have explicit approval and a controlled environment.
A common failure mode is using the wrong category of tool for the risk level.
A simple decision rule: the more sensitive the data content, the more you should bias toward isolation, strict retention controls, and auditable access.
Your contract should do more than say “we take security seriously.” It needs enforceable commitments you can rely on in a client conversation, or in the worst case scenario of a breach.
Consider requiring:
In short, you need enforceable clarity on reuse, retention, access, and deletion.
Even the most watertight contract won’t save you from insecure defaults, if those are not considered ahead of time. Treat AI tools like any other system that touches confidential client data, and consider the following technical configurations for improved security:
For a deeper security-focused evaluation, read our article on how patent practitioners should evaluate AI tools for data security and confidentiality.
This is where governance becomes practical. After contracts are signed and security is approved, you still need defined workflows that guide how AI is actually used on matters. Operational controls convert high-level policy into consistent, auditable practice.
Operational controls that can materially reduce risk include:
This is where a formal law firm AI policy and a clear client-facing position become essential. AI should be governed, as opposed to being improvised at the matter level.
A practical approach:
A simple governance rule applies: if you cannot clearly explain to a client what happens to their data (where it goes, who can access it, and how it’s controlled) you’re not ready to use that tool for their matter.
Key considerations by jurisdiction include:
UK legal professionals have strict duties around confidentiality and client information. In practice, assess whether AI use introduces third-party access or retention risk, and ensure you have appropriate safeguards plus documented supervision/governance aligned with current professional guidance.
For example, for UK patent attorneys, see IPReg’s Artificial Intelligence guidance.
In the EU, confidentiality duties often interact with data protection requirements. If personal data is processed, your evaluation should be GDPR-aligned (purpose limitation, minimisation, access controls, residency, subprocessors, retention).
Also note the EU AI Act can impose additional obligations depending on the AI system and your role (provider/deployer).
In the US, privilege and confidentiality analysis focuses on whether sharing information with an AI vendor is consistent with maintaining privilege and complying with ethical duties of confidentiality and competence (including tech competence).
In practice, teams often restrict AI use to approved tools, control retention/vendor access, and require human verification of AI-assisted work products. See ABA Formal Opinion 512 (professional ethics guidance) and the USPTO’s “Guidance on Use of Artificial-Intelligence-Based Tools in Practice Before the USPTO”.
Note: this article provides a general overview and does not constitute legal advice. Readers should consult qualified professionals before acting on any of the information presented.
AI can be deployed responsibly in patent practice, but it requires deliberate governance rather than ad hoc usage. Teams that implement AI successfully treat it like any other system handling client-confidential information: they define clear data boundaries, select an appropriate deployment model, secure enforceable contractual protections, configure technical controls, and embed operational guardrails so practitioners can comply consistently.
If you’re assessing AI for patent workflows and want a structured, audit-ready approach - from due diligence and security review through controlled pilots and firm-wide rollout - Solve Intelligence supports patent teams with purpose-built tools and governance-aligned implementation.
Solve Intelligence’s AI platform for patent drafting, prosecution, and claim charting is trusted by 700+ IP teams, including DLA Piper, Siemens, Finnegan, and Perkins Coie.
To evaluate Solve Intelligence against these best practices, book a demo with our team, or contact partnerships@solveintelligence.com to discuss a structured pilot for your firm.
It can be, but not by default. Treat raw invention disclosures and trade secrets as “Tier 3” (off-limits) unless you have explicit approval and an AI environment designed for highly sensitive data, with strict retention and access controls.
As a starting point: raw invention disclosures, unpublished experimental data, trade secret parameters, and confidential client communications should be off-limits in general-purpose tools. Use data tiers to define this clearly for your team.
Many tools retain prompts, files, outputs, and logs unless you disable retention or contractually prohibit it. Don’t assume, instead verify the vendor’s terms and your admin configuration.
Yes, it can, mainly if client-confidential or privileged material is disclosed to an AI provider without appropriate protections. The risk increases where the tool retains content, allows vendor/human access, uses inputs for product improvement, or is used outside approved firm/client controls.
A defensible approach is to (i) restrict use to approved tools with clear non-use/non-training and retention terms, (ii) avoid putting privileged analysis or raw invention disclosures into general purpose tools, and (iii) ensure outputs are reviewed and the use is documented.
At minimum, put in place:
At Solve Intelligence, we built our AI Patent Copilot for patent workflows with confidentiality-first controls. We do not use customer data (inputs or outputs) to train any AI model, and customer environments are sandboxed to prevent cross-customer exposure, with additional project separation to reduce cross-matter risk. Further, data is encrypted in transit and at rest (TLS 1.3 and AES-256).
For full documentation—including our pen test report, key policies, and subprocessors—refer to the Solve Intelligence Trust Center or contact our security team for diligence materials.
