Open-Source vs. Proprietary AI in Legal Practice: A Governance Framework for Law Firms and Courts

The choice for legal teams is no longer whether to use AI. It is who owns the system that sees client files, drafts arguments, and leaves fingerprints in the record. On one side sit open-source models that firms can host and tune themselves. On the other are proprietary platforms that bundle models, data pipelines, and guardrails inside a vendor interface that lawyers do not fully control.

Who Controls the System

For most practices, open versus proprietary is not a philosophical debate about software freedom. It is a governance decision about privilege, confidentiality, evidentiary reliability, and who will answer when an automated suggestion makes its way into a filing, a negotiation, or an internal memo. The same choice will shape how a firm aligns with the NIST AI Risk Management Framework, prepares for ISO/IEC 42001:2023 style audits, and satisfies ethics opinions that now speak directly to generative tools.

Vendors pitch open and closed models as technical options, but legal workflows make them concrete. In research, the decision determines which system reads client facts alongside licensed case law. In drafting, it governs which model proposes language that will carry privilege or advocacy weight. In contract review and discovery, it decides where sensitive documents live, how outputs are logged, and whether the firm or the provider controls critical guardrails.

This article steps through those tradeoffs in transparency, cost, and control across four core use cases: research, drafting, contract review, and e-discovery. The goal is not to declare a winner between open-source and proprietary AI, but to give law firms, legal departments, and courts a practical structure for documenting why they picked one model for one task and a different model for another.

What Lawyers Really Choose When They Pick an AI Tool

Legal technology marketing tends to compress everything into a single “AI solution.” In practice, lawyers are choosing between several layers. At the model layer, they decide whether to rely on an open-source base model whose architecture and weights are published or a proprietary model whose internals are confidential. At the deployment layer, they choose self-hosted or vendor-hosted infrastructure.

The risk levers sit across three planes. The model plane governs behavior, hallucination risk, and explainability. The data plane determines where client information resides, how it is encrypted, and whether prompts are used for further training. The governance plane covers access control, logging, incident response, and vendor contract terms.

The NIST AI Risk Management Framework organizes this landscape into four functions: Govern, Map, Measure, and Manage. ISO/IEC 42001:2023, the first international management system standard for AI, provides similar structure. For law firms and courts, the open versus proprietary decision is largely a question of who will carry which parts of that framework.

Transparency: Privilege, Explainability and Evidentiary Scrutiny

Proprietary platforms have moved quickly to address confidentiality concerns with contractual and technical safeguards. Thomson Reuters acquired Casetext for $650 million in 2023, integrating CoCounsel, a proprietary AI legal assistant powered by OpenAI’s GPT-4. Fisher Phillips became the first major law firm to deploy CoCounsel firm-wide to its 500-plus attorneys. The tool couples OpenAI’s technology with Thomson Reuters’ proprietary legal databases, offering end-to-end encryption and contractual guarantees that client data is not used to train the underlying model.

Open-source deployments give firms the option to keep all client data inside their own perimeter. A self-hosted model can be restricted to a private network, with prompts and outputs stored under the firm’s retention schedule, making it easier to demonstrate that no third party has training access to privileged materials.

Many proprietary vendors now offer strong contractual and technical protections, committing that customer prompts are not used to train shared models and providing independent security attestations such as SOC 2 reports. The key question is whether those assurances are documented clearly in data processing agreements and incident reporting clauses.

Open-source models offer more transparency about architecture and training approach. Technical teams can run adversarial tests and document limitations. Proprietary platforms invest in evaluations, red-teaming, and safety layers but rarely expose technical details. The tradeoff is between depth of insight and depth of vendor testing.

The ABA’s Formal Opinion 512 emphasizes that lawyers remain responsible for understanding AI technology and reviewing output carefully. The Florida Bar’s Opinion 24-1 stresses protection of client confidences and accurate billing. The North Carolina State Bar’s 2024 Formal Ethics Opinion 1 underscores that attorneys cannot delegate professional judgment to AI. The New York City Bar’s Formal Opinion 2024-5 provides detailed supervision guidance. None mandates open or proprietary tools, but all require transparency and control from either the firm or the vendor.

Cost: Licenses, Infrastructure and Hidden Compliance Expenses

Open-source models often have little or no license fee, but require compute, storage, and engineering time. Standing up a self-hosted model capable of handling confidential client work typically requires GPU capacity, hardened hosting, and ongoing monitoring.

Proprietary platforms wrap those costs into predictable subscription or usage pricing. Vendors bring ongoing model updates and security patches that the firm would otherwise manage itself. The tradeoff is less granular control and the risk that price increases will push the tool out of reach once embedded in workflows.

Open-source deployments demand internal investment in governance. Someone must design prompts, configure guardrails, and write policies that translate ethics opinions into operational checks. Proprietary tools reduce some burdens but introduce costs in negotiating data processing terms, localization options, and audit rights. Exit costs can be significant if customizations cannot be exported to competing systems.

Many enterprises adopt hybrid strategies: open-source AI for internal tasks where tight control matters most, and proprietary AI for external-facing tools where vendor infrastructure provides scale.

Control: Governance, Customization and Risk Allocation

Open-source deployments give firms ability to adjust system prompts, safety filters, and fine-tuning data. They can block the model from speculating about jurisdictions where the firm does not practice or require structured outputs that fit internal templates. That freedom comes with responsibility: if guardrails are configured poorly, the firm cannot blame an external vendor.

Proprietary platforms centralize controls with the vendor. Many legal-specific tools enforce conservative safety rules around tasks such as predicting case outcomes. Firms should treat guardrail configuration as a governance topic, not only a user-experience issue.

Open-source deployments require firms to establish their own monitoring for model drift, the gradual degradation or shift in AI performance as the model encounters new data patterns or as the legal landscape evolves. Firms must track accuracy metrics, benchmark outputs against known-good results, and decide when to retrain or replace models. Proprietary platforms typically handle drift detection and model updates centrally, but firms must verify that vendor monitoring aligns with their risk tolerance and that they receive adequate notice before significant model changes that could affect live matters.

When AI is self-hosted, it integrates with existing identity and access management, litigation hold, and records retention systems. Proprietary tools provide their own logging dashboards and user roles, but the firm must verify those features match its obligations.

Vendor agreements are where choices crystallize. Open-source providers may disclaim responsibility for model behavior while offering commitments around hosting and security. Proprietary providers sometimes offer broader warranties and indemnities. Under ISO/IEC 42001, organizations must assign clear roles and responsibilities across the AI life cycle.

Workflow Deep Dives: Research, Drafting, Review and Discovery

Proprietary tools built by established legal information providers offer curated content, citator integration, and dedicated hallucination controls. Open-source models can be wrapped around a firm’s knowledge management system and licensed databases, but doing so requires careful technical and contractual design to avoid breaching content licenses. According to the 2024 Thomson Reuters Generative AI in Professional Services report, research, summarization, and document review are among the most common AI use cases.

Open-source models make it easier to tailor prompts, enforce citation formats, and ban fabricating authorities. Proprietary drafting tools wrap features into structured workflows with template libraries and citation checks. Courts expect human lawyers to check every authority, regardless of tool. ABA Formal Opinion 512 and state bar opinions converge on one message: AI output is subject to the same duties of competence and supervision as work produced by junior lawyers.

Open-source models tuned on internal clause banks can deliver highly customized review. Self-hosted systems can be configured so third-party paper never leaves the firm’s environment. Proprietary contract tools leverage larger cross-client datasets to offer benchmarking and analytics about which positions are commonly accepted in a given market segment.

Open-source models integrated into review platforms offer deep customization of coding guidelines. Proprietary discovery tools add years of experience with defensibility protocols and expert testimony. Reproducibility and documentation matter more than whether model weights are public. Parties and courts will want to know how the system was validated and how human reviewers were trained to interact with AI suggestions.

Courts, Public Institutions and the Open-Source Question

Public institutions face their own version of the open versus proprietary tradeoff. Courts, legal aid providers, and regulators have strong reasons to prefer tools that can be inspected, audited, and maintained over long horizons. Open-source systems can support that mandate by giving public bodies the ability to host models on government infrastructure and to publish documentation that would be impossible with fully closed systems.

At the same time, many justice-system deployments run on tight budgets and rely on external vendors for implementation and support. Proprietary systems can provide turnkey functionality for tasks such as online dispute resolution or guided form completion. The governance challenge is to ensure that procurement contracts address transparency, data ownership, and exit strategies. Where algorithmic transparency laws or public records obligations apply, courts and agencies must be able to explain and, when necessary, defend the tools they have chosen.

Turning Philosophy into a Governance Checklist

For most legal organizations, the right answer will not be “all open” or “all proprietary.” It will be a portfolio of systems, each chosen for a specific workflow and risk profile. To keep that portfolio coherent, firms and legal departments can build a simple decision matrix that scores candidate tools along five dimensions: transparency, confidentiality, cost, control, and portability. Each dimension should be rated separately for research, drafting, contract work, discovery, and client-facing tools.

The NIST AI RMF’s four functions provide a useful structure for that exercise. Under Govern, firms assign roles and responsibilities for AI selection, configuration, and oversight. Under Map, they document which legal tasks and data categories will pass through each system. Under Measure, they define metrics for accuracy, bias, reliability, and model drift, establishing baselines and monitoring protocols. Under Manage, they set out monitoring, incident response, and decommissioning plans. ISO/IEC 42001 adds a formal management-system lens, encouraging organizations to align AI objectives with overall strategy, risk appetite, and regulatory context.

Whatever mix of open-source and proprietary tools a firm adopts, two governance artifacts are becoming hard to avoid: an AI use policy that translates ethics obligations into practical rules, and an AI inventory that lists all systems in use, their purposes, and their owners. Without those, even well intentioned choices about open and closed models will be difficult to defend to clients, regulators, or courts.

Where the Line is Likely to Move Next

By early 2025, 26 percent of legal organizations reported actively using generative AI, up from 14 percent in 2024, and a 2025 Thomson Reuters survey found that more than 95 percent of professional respondents expect generative AI to become central to their organization’s workflow within five years. As adoption moves from experiments to an assumed part of practice at that scale, the line between open and proprietary tools stops being a thought experiment and turns into a daily budget procurement and governance constraint.

Vendors are already blurring the line. Some closed-weight models are becoming available through on-premise or virtual-private-cloud deployments that give customers more control over data residency and logging. At the same time, open-source models are gaining enterprise wrappers that add indemnities, service-level commitments, and compliance documentation. Regulators may eventually treat highly transparent models differently from pure black boxes, particularly in high-risk domains, but current legal and ethics guidance puts the focus on how lawyers select, supervise, and document their tools.

For law firms and courts, the safe assumption is that AI decisions will age in the file. Contracts, policies, and risk assessments drafted today are likely to be read in discovery, regulatory reviews, or professional discipline matters tomorrow. Choosing between open-source and proprietary AI is, at heart, a choice about what story the institution will be able to tell about its systems when that day comes and whether that story shows that someone was truly in control.

Sources

• American Bar Association: “Formal Opinion 512 – Generative Artificial Intelligence Tools” (July 29, 2024)
• Casetext Unveils CoCounsel, the Groundbreaking AI Legal Assistant Powered by OpenAI Technology (March 14, 2023)
• Fisher Phillips: “Casetext’s CoCounsel is Powered by OpenAI’s GPT-4”
• Florida Bar: “Ethics Opinion 24-1 – Use of Generative Artificial Intelligence” (Jan. 19, 2024)
• International Organization for Standardization: “ISO/IEC 42001:2023 – Artificial Intelligence Management System” (Dec. 2023)
• LawNext: “Thomson Reuters Survey: Over 95% of Legal Professionals Expect Gen AI To Become Central To Workflow Within Five Years,” by Bob Ambrogi (Apr. 15, 2025)
• National Institute of Standards and Technology: “Artificial Intelligence Risk Management Framework (AI RMF 1.0)” (Jan. 2023)
• New York City Bar Association: “Formal Opinion 2024-5 – Generative AI in the Practice of Law” (Aug. 7, 2024)
• North Carolina State Bar: “2024 Formal Ethics Opinion 1 – Use of Artificial Intelligence in a Law Practice” (Nov. 1, 2024)
• Thomson Reuters Institute: “2024 Generative AI in Professional Services” (2024)
• Thomson Reuters Institute: “2025 Generative AI in Professional Services Report” (2025)

This article was prepared for educational and informational purposes only. It does not constitute legal advice and should not be relied upon as such. All cases, sanctions, and sources cited are publicly available through court filings and reputable media outlets. Readers should consult professional counsel for specific legal or compliance questions related to AI use.

See also: Can Machines Be Taught to Obey Laws They Can’t Understand?