AI, Data, and Client Confidentiality: The Legal Industry’s Privacy Dilemma

For legal organizations of all sizes and specialties the growing reliance on technology has introduced an undercurrent of challenges that few are willing to confront head-on. While technology promises efficiency and agility, the hidden costs of inadequate privacy solutions are becoming an increasingly pressing concern. There are multiple applications for technologies like artificial intelligence (AI), but privacy and data concerns create barriers to entry. Across the legal industry, there are firms that are experiencing hidden costs that they do not realize they are subject to. A decision to move too late, or not at all, on a technology that could bring scaling functions, more billable hours, and more accurate research presents a real opportunity cost. Here, we uncover these overlooked challenges and explore why traditional AI solutions are not sufficient to provide the privacy and security law firms need to overcome these hidden challenges. Real Stories, Real Stakes In our conversations with law firms and compliance teams, one theme emerges repeatedly: the fear of data breaches. The stakes for a data breach of client data are extremely high, but understanding where these breaches could come from is a hidden opportunity. If a firm employs an AI solution that resides in a multiparty cloud architecture, the client data it is using is exposed to unnecessary risk. If, however, the firm employs an AI system on-premise where its data, queries, and outputs never leave its own architecture, that risk is mitigated. Another common story relates to data jurisdiction issues. In the US, many states are creating their own privacy laws in the absence of federal legislation. Firms may run into issues with where data is stored or transits if they are using a multiparty cloud. The resulting regulatory fallout would not only cost the firm financially but also damage its reputation. This is another risk that is mitigated by keeping the data on which research depends within the firm’s own infrastructure. Why Existing Solutions Fall Short Traditional legal technology providers often focus on functionality—speed, ease of use, and integration capabilities. While these are critical, they often come at the expense of robust privacy measures. This is because they are built using the same engineering that is being used to build AI for public consumption. This approach creates mismatches with what the legal industry needs and what it gets on the market. Here’s why: Over-reliance on Cloud Storage: Many platforms store data in centralized cloud environments, making them attractive targets for hackers. Data Cross Contamination: Many platforms use user data to train their algorithms for onward sale to other customers. The sensitivity of legal data precludes this kind of training and must be considered by legal experts before using. One-Size-Fits-All Encryption: Basic encryption methods fail to account for the nuanced privacy requirements of legal professionals handling privileged communications. Regulatory Mismatches: Solutions designed for global markets frequently overlook jurisdiction-specific privacy laws, leaving firms vulnerable to compliance issues. Transparency and Auditability: Lawyers must show their work, especially in courtroom settings. Existing legal AI solutions do not allow attorneys to show how the algorithm got to its conclusion, which could cause long-terms problems for work product based on those outputs. Share The Hidden Costs of Inaction On one hand, firm leadership may decide that the best way to mitigate any risk is to not implement AI at all. However, this approach has never been effective with other technologies over time such as emailing documents or virtual meeting options. In every case, the firms that adopted these technologies quickly and smartly gained internal and client value by moving forward with technologies that showed significant promise. Here’s what’s at stake: Scale: Using purpose-built AI will allow legal firms to take on more cases, create more billable hours, and increase the quality of their results at a lower cost. Reputation: Having in-house AI that does not risk client data in a multiparty cloud will bring reputational benefits to early adopters. Those who choose not to use AI at all or that use cloud-based applications will be viewed as behind the times, producing lower quality work products in more time. Workforce: Freeing critical thinking and strategically minded legal experts to focus on client preparation legal strategy will create more value per unit of work for firms. AI will augment lawyers as a value added. The Hidden Risk to Competitive Edge The legal industry is competitive with many different firms working in specialized legal areas. Those firms must continue to bring in high value clients and expand their client base or risk their business. AI for legal experts, when built with privacy and security in mind, creates a higher quality of legal service by pairing humans with customized AI. Firms that can unlock the value potential of freeing a legal expert to critically think with the help of data-driven insights will ultimately produce better work than competitors and drive new clients. Many firms have large holdings of data in the form of case files, briefs, and other documents used in the course of their work over many years. What value are those data holdings bringing the firm? Many firms are paying significant prices for data storage, so they need to get value for current clients from those data holdings. With privacy preserving AI, firms can unlock case insights based on their existing data holdings to bring to bear in current cases. This creates another dimension to the competitive edge that firms with custom AI will enjoy. A New Way Forward So, how can the legal industry address these challenges? The answer lies in embracing privacy-preserving AI solutions specifically designed for the nuances of legal practice. Here’s a preview of what’s possible: Federated Learning: Instead of storing data in a central location, federated learning enables AI to train on data locally, ensuring sensitive information never leaves its source. Differential Privacy: This cutting-edge approach adds a layer of statistical noise to data, ensuring individual records remain anonymous while preserving analytical insights. Customizable Compliance Frameworks: Solutions that adapt to jurisdiction-specific laws, automating compliance while minimizing manual effort. These technologies not only address the vulnerabilities of traditional solutions but also provide a competitive edge by safeguarding client trust and enhancing operational efficiency. The hidden problems plaguing privacy in the legal industry demand more than a band-aid solution. By adopting privacy-preserving AI tailored to the legal market, firms can mitigate risks, protect their reputations, and focus on what they do best—delivering exceptional legal services. At Frontier Foundry, we’re committed to helping the legal industry navigate these challenges with the confidence your specific challenges demand. Are you ready to uncover a better way forward? Let’s start the conversation. Connect with us: Substack , LinkedIn , Bluesky , X , Website To learn more about the services we offer, please visit our product page. This post was edited by Thomas Morin, Marketing Analyst at Frontier Foundry. View his Substack here and his LinkedIn here . Subscribe now Leave a comment