Most corporate legal departments are buying AI tools the way they buy office supplies — whoever demos best wins. That's how you end up with five overlapping subscriptions, no data governance, and a security team that finds out about your AI vendor from a penetration test.

Legal AI procurement needs a real process. An RFP framework, a security questionnaire, data handling requirements, and evaluation criteria that go beyond 'the interface looked nice.' The companies getting burned by AI vendor decisions in 2026 aren't the ones who chose wrong — they're the ones who didn't have a process for choosing at all.

Traditional software procurement evaluates functionality, price, and vendor stability. Legal AI adds three dimensions that most procurement teams aren't equipped to evaluate. Data sensitivity — legal AI tools process privileged communications, confidential strategy documents, and client data that's subject to professional responsibility rules no other department faces. Output reliability — unlike a CRM or ERP, AI tools can generate plausible but wrong outputs that create legal liability. Regulatory exposure — the EU AI Act, state-level AI laws, and bar ethics opinions create compliance requirements specific to legal AI use. Your standard procurement team can handle the first dimension. They need legal guidance on the second and third.

Your RFP should cover seven categories beyond standard procurement. Data handling: Where is data processed and stored? Is it used for model training? Can you delete data on demand? What's the data retention policy? Security: SOC 2 Type II certification? Encryption at rest and in transit? Access controls and audit logging? Penetration testing frequency? Privilege protection: How does the tool handle attorney-client privileged material? Can you segregate privileged from non-privileged data? Accuracy and reliability: What's the tool's error rate? How does the vendor measure accuracy? What safeguards exist against hallucination? Regulatory compliance: EU AI Act classification? State law compliance? Bar ethics opinion alignment? Integration: API availability? SSO support? Compatibility with your existing tools? Exit strategy: Data portability? Contract termination data handling? Migration support?

The Security Questionnaire Every GC Should Send

Don't accept the vendor's standard security documentation at face value. Send a targeted questionnaire that covers the legal-specific risks. Key questions: Does client data ever leave your environment for model training, fine-tuning, or improvement purposes? (If yes, stop here.) Can we configure data residency requirements? (Critical for clients with EU data or government contracts.) What happens to our data if you're acquired or shut down? (Many startups don't have a good answer.) Who at your company can access our data, and under what circumstances? (Demand named roles, not vague policies.) Do you use sub-processors, and if so, who are they and what data do they access? (The vendor's vendor is your risk too.) What's your breach notification timeline? (Anything over 48 hours is a red flag.) Get these answers in writing, attached to your contract as an exhibit.

Build a weighted scorecard with six categories. Functionality (25%): Does it solve the specific problem you're buying it for? Test with your actual documents, not the vendor's demo data. Security and data handling (25%): Score based on security questionnaire responses. Non-negotiable minimums: SOC 2 Type II, no training on client data, encryption at rest and in transit. Accuracy (20%): Run a blind test — give the tool 50 documents you've already reviewed manually and compare results. Anything below 90% accuracy on your data (not their benchmarks) is disqualifying. Integration and usability (15%): Can it connect to your existing tools? Will your team actually use it? Vendor viability (10%): Funding, customer base, revenue trajectory. Legal AI startups fail at a high rate — make sure yours will be around in 3 years. Total cost of ownership (5%): License, implementation, training, and ongoing support costs over 3 years.

The Procurement Playbook: From Evaluation to Contract

Week 1-2: Define requirements. What problem are you solving? What does success look like? What's your budget? Week 3-4: Issue RFP to 3-5 vendors. Don't evaluate more than 5 — you won't have bandwidth to do them all justice. Week 5-8: Demos and testing. Every vendor gets the same test data set and the same evaluation criteria. Include end users in testing, not just decision-makers. Week 9-10: Security review and reference checks. Talk to at least 3 current customers in similar industries. Ask specifically about implementation challenges and ongoing support quality. Week 11-12: Contract negotiation. Key terms to negotiate: data handling exhibit, SLA with teeth (credits for downtime), termination for convenience with data portability, and a cap on price increases at renewal. Week 13: Decision and deployment planning. Total timeline: 90 days from requirements to signed contract. Anything faster means you're cutting corners. Anything slower means you're overthinking it.

The Bottom Line: Legal AI procurement isn't harder than other enterprise software — it just has unique risks that your standard procurement process doesn't address. Build a process that evaluates data handling, accuracy, and regulatory compliance alongside functionality and price. The 90-day procurement playbook gives you rigor without paralysis. The companies that get this right buy tools that actually work and avoid the ones that create more risk than they eliminate.

AI-Assisted Research. This piece was researched and written with AI assistance, reviewed and edited by Manu Ayala. For deeper takes and the perspective behind the research, follow me on LinkedIn or email me directly.