Prompts and Outputs
Define whether prompts, drafts, summaries, extracted data, and generated outputs are stored, archived, or deleted.
Data Retention Compliance
Control how legal AI data is stored, retained, deleted, and audited.
LAW.co designs data retention compliance systems for legal AI, helping firms govern prompts, outputs, uploaded documents, embeddings, source records, audit logs, reviewer actions, and deletion policies across private and hybrid AI workflows.
01Retention rules for AI-generated data
02Deletion, archive, and access controls
03Audit-ready compliance records
Data Retention Control Center
Data CreatedPrompt, retrieved source list, AI output, and review notes captured.
CREATEPolicy AppliedMatter-level retention rule assigned based on data type and workflow.
RULEAccess ControlledUser, role, matter, and practice group permissions enforced.
ACCESSArchive or HoldRecords preserved, archived, or held according to firm policy.
HOLDDelete or ExportExpired records deleted, exported, or retained with audit evidence.
DONE
Retention Is Governance
Legal AI creates new data exhaust: prompts, outputs, retrieved sources, summaries, logs, embeddings, and review records. Firms need explicit rules for what is kept, where it lives, who can access it, and when it is deleted.
Retention Coverage
What legal AI retention policies should govern.
Data retention compliance should cover the full legal AI lifecycle, not just final documents.
Retrieved Sources
Track and govern source records, citations, documents, templates, precedents, and knowledge retrieved by AI.
Embeddings and Indexes
Define how vector indexes, metadata, document chunks, and retrieval data are retained or removed.
Review Records
Preserve reviewer actions, edits, approvals, escalations, rejected outputs, and final decision records.
Access Events
Log who accessed AI workflows, source data, outputs, records, and matter-linked knowledge.
Legal Holds and Deletion
Support holds, export packages, archive rules, expiration policies, and defensible deletion processes.
Retention Architecture
Retention rules should be built into legal AI workflows from day one.
LAW.co helps firms connect retention policies to private LLMs, legal RAG, document intelligence, audit trails, access controls, workflow orchestration, and hybrid deployment architecture.
Policy-Driven Storage
Apply retention rules based on data type, matter, workflow, user role, and risk level.
Lifecycle Automation
Move data through creation, storage, review, hold, archive, export, and deletion states.
Audit Evidence
Preserve records showing what was retained, deleted, accessed, exported, or placed on hold.
Data ClassificationIdentify prompts, outputs, sources, embeddings, uploads, logs, and review records.
Retention Policy AssignmentApply rules based on matter, client, data type, workflow, sensitivity, and jurisdictional needs.
Access + Storage ControlsGovern who can view retained data and where each record type is stored.
Hold, Archive, DeleteRoute records to legal hold, archive, export, expiration, or defensible deletion.
Audit + Reporting LayerMaintain compliance records for retention decisions, deletion actions, access events, and exports.
Compliance Controls
Retention compliance depends on clear rules and clean evidence.
The goal is not to keep everything forever. The goal is to keep the right records for the right period, under the right controls.
Record Classification
Separate prompts, source records, drafts, final outputs, uploaded files, logs, and metadata.
Retention Schedules
Apply schedules based on matter type, client policy, firm policy, data sensitivity, and workflow context.
Access Review
Control who can view, export, delete, archive, or place records on hold.
Vector Data Controls
Govern embeddings, document chunks, retrieval indexes, metadata, and knowledge base updates.
Deletion Workflows
Trigger expiration, review, approval, deletion, and confirmation workflows for AI-created records.
Audit Evidence
Maintain records of retention policy application, access, deletion, export, hold, and approval events.
Implementation Process
From unmanaged AI data to governed retention.
LAW.co designs retention systems around your AI architecture, matter workflows, document systems, compliance policies, and audit requirements.
01
AI data inventory
We identify the AI data being created, retrieved, stored, indexed, logged, exported, or passed into workflows.
02
Retention policy design
We define data categories, schedules, storage locations, access rules, holds, deletion requirements, and export needs.
03
Workflow integration
We connect retention rules to legal AI workflows, audit logs, private LLMs, RAG systems, and document repositories.
04
Testing and reporting
We validate retention behavior, deletion workflows, access controls, audit evidence, exports, and reporting views.
Govern Legal AI Data
Build retention rules before AI data sprawl becomes a problem.
LAW.co helps legal organizations design data retention compliance systems for prompts, outputs, uploaded documents, source records, embeddings, audit logs, access events, and review history.
FAQ
Data retention compliance questions.
Legal AI governance is incomplete if the firm cannot explain what AI data is retained, where it lives, who accessed it, and when it is deleted.
It is the process of defining how AI-related data is stored, retained, archived, deleted, exported, accessed, and audited across legal AI workflows.
Prompts, outputs, uploaded documents, source retrieval records, summaries, drafts, review decisions, embeddings, metadata, access logs, and workflow events may all require retention rules.
Yes. Retention and deletion policies can be designed to address document chunks, vector indexes, metadata, source records, and knowledge base updates.
Yes, but deletion should usually be governed by approval workflows, hold checks, audit records, and clear policy rules before records are removed.
