Client-delivered AI risk assessments built on a structured testing platform
A large international law firm built an AI red-teaming platform on Airtable: a 40-area legal risk taxonomy drives prompt generation, a custom connection submits prompts to client AI models automatically, and responses are scored against a rubric for attorney review. Clients receive findings and global regulatory intelligence through purpose-built interfaces rather than static reports.
The problem
AI risk assessment for clients was manual and time-intensive, with no structured methodology for organizing testing across legal risk areas. Coordinating more than a dozen attorneys on a single project was difficult without a shared system for prompt management, response capture, and review assignment.
Delivering professional, interactive findings to clients required more than a spreadsheet or document. Regulatory tracking was siloed, with no path to expose curated intelligence to clients in a governed, up-to-date interface.
What they built
The firm built an AI red-teaming platform on Airtable: a 40-area legal risk taxonomy structures prompt generation, and a proprietary legal AI tool connection submits those prompts to client models automatically, capturing responses in linked records. Attorneys rate outputs to refine prompt quality, and a scoring rubric drives structured review before findings are delivered.
A global AI regulation database collects contributions from associates across the firm and exposes curated regulatory content to clients through purpose-built interfaces. Regulatory change monitoring uses automated ingestion, scoring, and attorney curation to keep the database current.
The outcome
Testing cycles compress significantly. The structured methodology lets analysts execute complex red-teaming workflows quickly, and attorney feedback on prompt quality accelerates iteration without starting over.
Clients prefer the interactive interface delivery over static spreadsheets. The firm now offers a differentiated service with live regulatory intelligence and risk findings accessible through a governed client-facing portal.
Inside the solution
AI Risk Testing & Regulatory Monitoring for Client Delivery
Legal & Compliance
Legal Risk & Compliance
- AI red-teaming: taxonomy design, prompt generation, automated model testing, response capture, scoring rubric, attorney review
- Prompt engineering iteration with attorneys rating outputs to refine quality
- Global AI regulation database with associate data contribution
- Client-facing interface delivery of regulatory content and risk findings
- Regulatory change monitoring via automated ingestion, scoring, and attorney curation
- Manual, time-intensive AI risk assessment processes with no structured methodology
- Difficulty coordinating 12+ attorneys on testing projects without a shared platform
- No path to deliver professional, interactive client experiences for findings
- Siloed regulatory tracking without a governed path to client access
- Slow prompt quality iteration without structured attorney feedback loops
- Compression of time to deliver AI risk testing results to clients
- Structured methodology enabling analysts to execute complex workflows quickly
- Client preference for interactive interfaces over static documents
- Accelerated prompt engineering and quality iteration with attorney feedback
- Consolidated, client-accessible regulatory intelligence
- AI Innovation
- Legal
- Knowledge Management
- Interfaces
- Automations
- Custom Extensions
- API
- Forms
AI Risk Testing & Regulatory Monitoring for Client Delivery
Run client AI red-teaming end to end: a legal risk taxonomy drives a rated prompt library, automated submissions land model responses in linked records, a five-dimension rubric scores them, and attorneys review before findings and curated global AI regulation reach clients through interface pages instead of static reports. Eleven Airtable AI columns draft prompt variants, triage responses, explain rubric scores, summarise regulatory changes and write client digests, while four automations open review assignments, advance cycles on escalation, log associate submissions, and email a weekly curation backlog digest.
From manual assessments to a structured AI testing platform
Build the risk taxonomy and prompt library
Automate prompt submission and response capture
Deliver findings and regulatory intelligence via interfaces
FAQ
Frequently asked questions
A proprietary legal AI tool connects the Airtable base to the client's AI model via a custom extension and API. When a prompt record is ready for testing, the automation submits it to the model and captures the response back into a linked record, ready for scoring and attorney review, without any manual copy-paste.
The taxonomy is a dedicated table in Airtable with one record per legal risk area. Each risk area links to its associated prompt records and, ultimately, to the response and scoring records from each testing cycle. This structure lets attorneys filter findings by risk area and compare results across testing runs.
Interactive interfaces let clients filter findings by risk area, view regulatory changes as they are curated, and explore the data at their own pace, rather than waiting for a periodic document export. The firm's clients have directly expressed a preference for this format over spreadsheets and PDFs.
Associates contribute new regulatory records from their jurisdictions into the database. Automated ingestion and scoring flags new entries for attorney review before they are published to the client-facing interface. This gives the firm quality control over what clients see, with attorney curation as the final gate.
Yes. Each red-teaming project is scoped in its own set of records, and interfaces can be configured to show only the data relevant to a specific client engagement. The shared taxonomy and prompt library apply across all projects, so prompt quality improvements benefit every active testing cycle.