Legal AI security
A law firm should be able to diagram every AI trust boundary
Legal AI security for law firms: a procurement and governance checklist covering data flow, models, tenancy, identity, audit, retention and source verification.
Quick answer
The strongest legal AI security review starts with the actual data path, not the certification logo page. Identify the tenant boundary, matter permissions, retrieval sources, model endpoint, retention state, subprocessors, connectors, logs and human-review controls. Then map the contractual commitments to that architecture.
Reviewed September 2026
RFP checklist
Twelve questions procurement should ask
| Area | Question | Evidence to request |
|---|---|---|
| Inference | Where does the model request execute? | Architecture diagram and approved model route list. |
| Tenancy | What is shared between customers? | Tenant/data-plane design and logical/physical isolation statement. |
| Identity | How are lawyers provisioned and revoked? | SAML/OIDC/SCIM flow and group-to-matter permission mapping. |
| Matter access | Can AI cross ethical walls or matter boundaries? | Permission-enforcement design and test evidence. |
| Retention | What is stored, for how long and where? | Data lifecycle schedule for documents, prompts, outputs, logs and backups. |
| Models | Which providers can receive content? | Model allowlist, routing policy and provider contractual terms. |
| Egress | Can matter content leave the defined environment? | Network policy and private-perimeter attestation where applicable. |
| Sources | How are quotations and authorities verified? | Source-coordinate and authority-verification workflow. |
| Agents | What can an autonomous workflow actually change? | Action allowlist, review gates and audit trail. |
| Connectors | Which external systems can the product access? | Connector scope, permissions and data-flow diagram. |
| Incident response | What happens after a security event? | Security addendum, SLA and incident response process. |
| Audit | Can the firm reconstruct consequential AI actions? | Immutable/persisted audit event examples and export controls. |
Governance
Security and legal quality are connected
A secure model that cannot show the source of a legal proposition still creates professional risk. Likewise, a highly accurate model with weak matter permissions creates confidentiality risk. Enterprise legal AI should treat source provenance, access control and action governance as parts of the same system.
Sanctum Lex is designed around that combined boundary: matter permissions determine what the system can retrieve, model routing determines where inference can occur, and review gates determine what an agent can commit.
Frequently asked questions
What security controls should legal AI have?
At minimum, enterprise legal AI should have strong identity and access controls, audit logs, encryption, retention controls, matter permissions, documented subprocessors and model routes, and a clear data lifecycle. Sensitive deployments may also require single tenancy, customer-managed keys, IP restrictions or a private inference boundary.
What is the biggest legal AI security question?
Where the matter data goes during inference. Firms should be able to trace the path from the lawyer to the application, retrieval layer, model endpoint, storage, logging and any external connector.
Does no model training mean the data never leaves the vendor?
No. 'No model training' is a use restriction. The data may still be transmitted to a processor for inference. Buyers should distinguish training, retention, hosting, egress and tenancy.
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