Why Law Firms Shouldn’t Build Their Own Legal AI Tools

Senior associates are building sophisticated legal AI tools and sharing them on GitHub. “Vibe coding” promises that anyone can create custom applications without traditional software engineering expertise.  

The approach might be appealing: escape vendor lock-in, customize tools to your exact workflows, and save money by building internally. But this logic ignores what it takes to deploy, maintain, and govern technology in a law firm, especially when that technology handles sensitive client data and critical legal workflows. 

The control and customization might initially seem promising, but beneath the surface is the weight of risk, cost, and accountability challenges that law firms aren’t equipped to handle. 

Security & Compliance Aren’t Negotiable 

Legal work involves some of the most sensitive information that exists: privileged communications, trade secrets, merger negotiations, litigation strategy, personal data protected under GDPR and other privacy regulations. Homegrown AI tools, even those built with good intentions by talented lawyers, lack the enterprise-grade security infrastructure that dedicated legal technology vendors spend millions developing and maintaining. 

Where does client data flow when processed by an internally-built tool? Who has access? How is it encrypted at rest and in transit? 

These aren’t questions that get answered by a weekend coding project. Modern applications rely on dozens or hundreds of third-party libraries, each representing a potential vulnerability. Vendors employ security teams to continuously monitor and patch these dependencies. Enterprise platforms have 24/7 security operations centers watching for anomalies, intrusions, and data exfiltration attempts. Internal tools built by associates don’t. 

When a data breach occurs, “we built it ourselves” is not a defense clients will accept. 

Legal practice is one of the most heavily regulated professional services. Bar associations impose strict ethical obligations around client confidentiality, conflicts of interest, and data handling.  

When regulators or clients ask, “Who accessed this data and when,” can your homegrown tool answer? Enterprise platforms build comprehensive logging and reporting as core features. Obtaining SOC 2 Type II certification or ISO 27001 compliance isn’t just expensive—it requires documented processes, regular audits, and continuous monitoring. Vendors absorb this burden. Firms building internally must replicate it for every tool, and when compliance failures occur, malpractice insurance may not cover losses stemming from inadequately secured homegrown technology. 

The Trust Question: Commercial vs. Open Source AI 

There’s a deeper issue that goes beyond technical capability: trust and reputation. 

When law firms deploy commercial AI platforms from trusted vendors, they’re also deploying that vendor’s reputation. Enterprise legal technology companies stake their business survival on security, accuracy, and reliability, as outlined in recent predictions on where legal AI is headed. They carry professional liability insurance. They undergo regular security audits. They have legal teams reviewing compliance obligations. Their incentives align with law firms’ need for trustworthy, accountable technology. 

Open source AI tools, by contrast, come with no guarantees. The code may be transparent and auditable, but who’s auditing it? How many law firm IT teams have the specialized expertise to review AI model architectures, evaluate training data provenance, or assess prompt injection vulnerabilities? And even if they do audit the code, who’s liable when something goes wrong? 

Clients hire law firms in part because of their reputation for discretion, competence, and risk management. Telling a Fortune 500 general counsel that your contract review process relies on a tool an associate built using open source components and “vibe coding” doesn’t inspire confidence; it raises red flags. The client’s question will be: if this tool makes an error that costs us millions, who’s accountable? 

With commercial platforms, there’s a clear answer and contractual recourse. With homegrown tools, there isn’t. 

Maintenance Is Where Costs Hide 

Software isn’t a one-time build. It requires continuous maintenance, updates, bug fixes, and adaptation to changing requirements. What happens when the associate who built your contract review tool laterals to another firm or makes partner and stops coding? Suddenly, you have a critical tool that no one understands. 

When a vendor’s platform has issues, you call support. When your internal tool breaks, who fixes it? The same associates who built it, taking them away from billable work. AI models, frameworks, and dependencies evolve constantly. Vendors continuously update platforms to maintain compatibility and security. Internal tools become outdated and vulnerable unless someone—again, pulling from billable hours—maintains them. 

“Vibe coding” optimizes for speed, not maintainability. What seems clever today becomes an unmaintainable mess of dependencies and workarounds tomorrow. Professional engineering teams follow best practices specifically to avoid this trap. The initial build may feel cheap. The total cost of ownership over three to five years is anything but. 

Scale Reveals What Prototypes Hide 

A tool that works well for one associate or practice group doesn’t automatically work firm-wide. Modern law firms run on interconnected systems: document management, billing, matter management, time tracking, CRM. Enterprise legal AI platforms invest heavily in pre-built integrations. Homegrown tools require custom integration work—expensive, time-consuming, and error-prone. 

What works for capital markets in Hong Kong may not work for litigation in New York. Building one tool that serves everyone requires understanding diverse workflows and maintaining separate configurations—work that compounds quickly.  

A tool that works fine for 10 users often breaks under the load of 1,000. Enterprise platforms are built and tested for scale. Internal tools rarely are, until they fail during critical work. 

Opportunity Cost Compounds 

Every hour spent building and maintaining internal tools is an hour not spent on what law firms do: practice law. A senior associate spending 20 hours building an AI tool isn’t billing those hours. At typical rates, that’s $10,000+ in foregone revenue per tool, per iteration. Firm IT teams are already stretched managing email, security, network infrastructure, and vendor relationships. Adding home-grown AI tools to their workload means something else doesn’t get done. 

While your firm experiments with building a contract review tool, competitors are already using mature platforms that have been refined through thousands of user interactions across hundreds of firms. Legal AI vendors have spent years solving challenges around legal-specific NLP, citation extraction, privilege detection, and jurisdiction-specific rules. Firms building internally start from scratch, reinventing problems vendors solved long ago. 

The Better Path Forward 

Firms should push vendors to meet their needs, customize within platforms where appropriate, and maintain high standards for the technology they adopt. 

But there’s a profound difference between influencing legal technology and building it from scratch. The right approach means partnering with vendors who offer flexible, customizable platforms that can adapt to different workflows without requiring firms to maintain code. It means participating in customer advisory boards and product development discussions to ensure vendor roadmaps align with the firm’s needs. It means demanding security, compliance, and governance standards as table stakes, not optional add-ons. 

Most importantly, it means focusing internal innovation efforts on legal strategy, client service, and process improvement—areas where law firms have a sustainable competitive advantage. 

The democratization of coding through AI is real and powerful. But it doesn’t change the fact that running production software at enterprise scale requires expertise, resources, and accountability that law firms aren’t structured to provide. 

As the legal AI landscape matures, the winners won’t be firms that built the most tools. They’ll be firms that deployed the right tools—secure, compliant, supported, and focused on what matters: delivering exceptional legal service to clients. 

Greg Ingino is Chief Technology Officer at Litera, leading its engineering, devops, architecture, R&D operations, QA, IT, and security teams. He brings more than 17 years of senior management experience in PE-backed and public software companies across several industries.