The integration of artificial intelligence into intellectual property practice has moved from experimental to essential. Patent prosecutors, trademark attorneys, and IP professionals are increasingly reliant on AI tools to draft applications, analyze office actions, and manage sprawling portfolios. Yet as these tools become indispensable, they also introduce a fundamental tension: How do you adequately address AI security concerns when adopting IP solutions to protect our clients’ most sensitive innovations? Confidentiality is the foundation of client trust and the bedrock of patent strategy. A premature disclosure can destroy patent rights. A leaked trade secret can evaporate competitive advantage.
Black Hills AI, founded by experienced IP attorneys, recognized this security imperative early. Their Generative AI platform, Otto IP™, was built by former and esteemed IP professionals from the ground up to address these specific concerns, ensuring Generative AI enhances productivity without compromising the integrity of your most confidential work. In short, we created the tool we wish we had when we were in your shoes.
Addressing the 5 Key AI Security Concerns When Adopting IP Solutions for Confidential Work
The philosophical conflict, choosing between efficiency and confidentiality, demands a systematic solution. Here is our guide on addressing the five core AI security concerns when adopting IP solutions for confidential work:
- The Data Training Problem
- The Internet Connection Dilemma
- Authentication and Access Control
- Encryption in Transit and at Rest
- The Vendor Relationship Question
1. The Data Training Problem
The most pressing security concern with consumer AI tools is data retention for model training. When you submit text to many general-purpose AI platforms, that content may be used to improve the underlying model.
For an IP attorney drafting claims or analyzing prior art, this creates an unacceptable risk: your client’s confidential invention disclosure could theoretically inform the AI’s future responses to competitors.
Even when companies promise not to use customer data for training, the technical architecture matters.
“Where is the data processed? How long is it retained? Who has access during processing? These aren’t hypothetical concerns; they’re questions that should appear in every AI vendor evaluation checklist, and a reputable vendor should gladly answer them candidly,” said Thomas Marlow, Chief Artificial Intelligence Officer at Black Hills AI.
The solution, then, lies in zero-retention APIs.
These architectures process queries and immediately discard the data after generating a response, leaving no residual information that could be accessed, leaked, or inadvertently incorporated into future training datasets. For IP work, this should be non-negotiable.
2. The Internet Connection Dilemma
General-purpose AI tools often access the internet to retrieve current information, fact-check responses, or supplement their training data. While this connectivity can be valuable for general research, it creates two significant risks for IP work.
First, there’s the data leakage risk. An AI system that reaches out to external databases or search engines may inadvertently transmit fragments of confidential queries. Even if the AI provider doesn’t retain data, the act of querying external systems can create a digital trail.
Second, there’s the hallucination amplification problem. When AI systems access the open internet, they can encounter false, outdated, or deliberately misleading information.
“In patent prosecution, where accuracy is paramount, and errors can have expensive consequences, an AI that confidently cites non-existent prior art or mischaracterizes legal standards becomes a liability rather than an asset,” added Marlow.
3. Authentication and Access Control
Authentication and access control are next in line for AI security concerns when adopting IP solutions for confidential work; you must remediate.
Beyond data handling, robust security requires careful attention to who can access the AI system and what they can do within it.
As an example, Single sign-on (SSO) integration allows firms to manage AI access through their existing identity management systems, ensuring that access credentials are centrally managed and can be revoked immediately when attorneys leave the firm.
4. Encryption in Transit and at Rest
State-of-the-art encryption should be table stakes for any AI tool handling confidential IP data. This means not just encrypted connections (HTTPS) but end-to-end encryption that protects data both during transmission and while stored on servers.
However, encryption alone isn’t sufficient. The encryption keys themselves must be managed appropriately, with access controls that prevent unauthorized decryption.
5. The Vendor Relationship Question
Perhaps the most overlooked security consideration is the vendor’s broader data practices.
Does the AI provider store your data on third-party cloud services? If so, which ones, and under what terms? Are there subprocessors who might have access to your data?
Each additional party in the data chain represents a potential vulnerability.
Address AI Security Concerns when Adopting IP Solutions. Making the Security-First Choice
The good news is that IP professionals don’t have to choose between powerful AI capabilities and robust security. Purpose-built IP tools can deliver both. Black Hills AI, for instance, has built its Otto IP platform with enterprise-grade security at its core: state-of-the-art encryption technology, zero-retention APIs for complete data sandboxing, no data used for AI model training, and no third-party data monitoring. Their approach recognizes that for IP professionals, security isn’t a feature—it’s a requirement.
“As AI becomes standard practice in IP law, the firms that thrive will be those that adopt early while maintaining the confidentiality standards that client trust demands. The technology is ready. The question is whether your security framework is too,” mused Marlow.
Speak directly with the IP minds behind Otto IP™’s secure architecture to ensure your adoption strategy is compliant and competitive.