Client confidentiality in the age of AI: best practices for patent professionals

Why trust this guidance?

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.

What are the confidentiality risks when using AI in patent work?

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. 

Where things can go wrong

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.

What happens to what you type into an AI tool?

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.

Training vs. inference

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.

Storage and access

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.

Where data lives

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:

• Data residency: where data is stored/processed. 

See our  Data Residency Explained guide for more information.

• Subprocessors: who else touches the data (hosting, analytics, model providers). Verify the vendor’s security assurances (e.g., independent attestations) as appropriate to your risk.

How patent teams can use AI safely: a practical framework

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.

1) What information is off-limits vs. safe to share

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.

2) Which AI deployment is appropriate?

A common failure mode is using the wrong category of tool for the risk level.

Deployment models 

A simple decision rule: the more sensitive the data content, the more you should bias toward isolation, strict retention controls, and auditable access.

3) What to require in contracts

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:

• Confidentiality obligations that explicitly cover uploads, prompts, and outputs
• No training / no improvement use of customer content 
• Retention terms for what’s stored, for how long, and how it’s deleted
• Subprocessor controls with regards to disclosure, and notice of changes
• Audit rights / security documentation - SOC reports, policies, pen test summaries, as appropriate to your risk
• Data Processing Addendum (DPA) (where relevant) and clear controller/processor roles
• Incident response and breach notification terms (timelines, scope, cooperation)
• Termination & deletion obligations, including backups and derived data

In short, you need enforceable clarity on reuse, retention, access, and deletion.

4) What to configure technically

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:

• Single Sign-On (SSO) & Multi-Factor Authentication (MFA) - central identity, no shared accounts
• Role-based access control (RBAC) - set who can use what features
• Matter/workspace separation - avoid shared projects across clients to reduce risk of cross-matter contamination 
• Tenant isolation - logical isolation, or dedicated single-tenant infrastructure if required, to avoid cross-customer leakage
• Encryption in transit and at rest
• Logging and monitoring that supports audits without oversharing content

For a deeper security-focused evaluation, read our article on  how patent practitioners should evaluate AI tools for data security and confidentiality.

5) What to operationalise

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:

• Approved use cases (by data tier): Define which tasks AI may support and which are prohibited, mapped clearly to Tier 0–3 data (see data tiers above).
• Mandatory human review: Require practitioner review and editing of all substantive outputs (e.g., claim language, legal arguments, client drafts).
• Prompting rules: Prohibit raw invention disclosures in general tools; avoid client identifiers and full confidential uploads unless in an approved environment.
• User training: Provide onboarding before access and periodic refreshers when tools or policies change.
• Defined escalation path: Assign responsibility for approving edge cases involving highly sensitive (Tier 3) data.

Do you need to tell clients? Transparency, consent, and governance

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:

• Start with the client’s engagement terms and technology requirements (e.g. Outside Counsel Guidelines (OCG))
  Some clients already restrict certain tools, require prior disclosure, or mandate specific security controls. Confirm what is permitted before using AI on their matters. For a baseline statement of confidentiality obligations, see the SRA guidance on confidentiality of client information (UK).
• Define when disclosure or consent is required
  Be explicit about trigger points. For example, when client-confidential data is processed by a third-party AI provider, or when AI materially contributes to substantive legal work product.
• Document how AI is used
  Record which system was used, for what task, what data tier was involved, and what human review was applied. This supports auditability and defensibility.
• Integrate AI governance into engagement workflows
  Include policy language in engagement terms where appropriate, prepare a standard response for client AI/security questions, and maintain a vendor security summary for procurement reviews.

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.

How privilege and professional secrecy considerations vary by jurisdiction

Key considerations by jurisdiction include:

United Kingdom

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.

European Union

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).

United States

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.

Putting this into practice

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.

Next steps: evaluate Solve Intelligence for a governance-ready pilot

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 400+ 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.

Frequently Asked Questions

Is it safe to use AI with invention disclosures or client-confidential information?

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.

What information should patent professionals never enter into an AI tool?

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.

Will the AI tool store or reuse what I type?

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.

Does using AI risk waiving privilege or breaching professional confidentiality duties?

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.

What best-practice safeguards should a patent team implement before adopting AI?

At minimum, put in place: 

1. A written AI policy that defines allowed/prohibited use cases by data tier 
2. Vendor due diligence covering training, retention, access, residency subprocessors, and incident response 
3. Contractual protections (e.g., confidentiality + DPA where relevant, no training use, retention/deletion, breach notice)
4. Technical controls (e.g., SSO/MFA, RBAC, matter/workspace separation, encryption, tenant isolation)
5. Operational controls (e.g., mandatory human review for substantive work product, user training, logging/auditability, and periodic re-approval as tools change).

How does Solve Intelligence store and protect client-confidential patent data?

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.