Enterprise buyer guide
Legal AI procurement checklist for law firms
A legal AI procurement process should test legal quality, matter permissions, source grounding, model routes, retention, audit, integrations, deployment boundaries and human review on representative work.
Quick answer
The best legal AI procurement process is evidence-led. Shortlist products against a real matter, require a written architecture and data-flow explanation, test source fidelity and refusal behaviour, verify identity and matter permissions, and record which deployment claims are contractual versus technically enforced.
Reviewed September 2026
Evaluation
Test the system on consequential legal work
A polished demo is not enough. Give each shortlisted platform the same representative matter and compare retrieval, source faithfulness, reasoning quality, adversarial challenge, drafting, latency and the ability to navigate back to the underlying record.
The test should also include negative cases: material that a user should not be able to see, an authority that should be refused or qualified, and a workflow that requires lawyer approval.
Architecture
Ask where data moves, not whether the product is 'secure'
Procurement teams should request a concrete data-flow diagram covering storage, embeddings, inference, subprocessors, logs, support access, model routing, retention and deletion. Terms such as private, zero retention, single tenant and zero egress describe different properties.
Sanctum Lex is designed to expose the matter boundary, approved inference route, source state, review state and audit state so enterprise buyers can evaluate the operating model rather than rely on a generic security label.
Decision
Score legal value and control together
A platform that drafts well but cannot preserve matter permissions may be unsuitable for institutional use. A highly controlled platform that cannot retrieve the decisive document is equally unsuitable.
A useful scorecard therefore combines legal performance, source fidelity, governance, deployment, integrations, administration, user adoption and commercial fit instead of optimizing for a single benchmark.
Frequently asked questions
What should be in a legal AI RFP?
Include representative legal workflows, security architecture, data handling, model providers and routing, retention, identity, matter permissions, audit, integrations, support access, incident response and evidence of legal-quality testing.
How should firms compare legal AI vendors?
Use the same matter and scoring rubric across vendors, then separate product capability from deployment and governance claims so trade-offs are explicit.
Should legal AI procurement include lawyers and security teams?
Yes. Lawyers should assess legal usefulness and source quality, while security, privacy, knowledge and IT teams validate the operating boundary and institutional controls.
Continue comparing
Evaluate on your own matter