Private AI for legal teams
Keep Client Data Under Your Firm's Control
Bonelli helps Dallas law firms evaluate private AI and law firm AI solutions for document search, summarization and evidence workflows. Start with 3 boundaries: the matter data it may access, the people allowed to use it, and the reviewer accountable for its output. For firms using Microsoft 365, review identity and sharing exposure before connecting an AI workflow.
Review the Microsoft 365 assessment
See an anonymized example of private AI for legal evidence review.
Bonelli Systems is an AI-first Information and IT Service Provider in Dallas, combining Microsoft 365, cybersecurity, compliance support, and Applied AI Engineering.
Start with the matter boundary, not the model
Bonelli helps law firms evaluate private AI for approved document and evidence workflows. Decide which matters the system may access, which users may retrieve results, and who reviews its output. A private deployment still needs access controls and accountable human review.
What Bonelli built for a McKinney firm
For a McKinney, Texas criminal defense firm, Bonelli built a fully local AI evidence-processing system with video, image, and document ingestion, transcription, and speaker diarization. The system runs on the firm’s own hardware; no privileged material is sent to a public AI service. Read the anonymized legal evidence case brief.
That describes this implementation. Your firm’s deployment boundary and workflow need their own evaluation; the example does not establish measured time savings or legal privilege.
Agree the pilot’s controls and responsibilities
- The firm identifies permitted source material, matter access rules, retention requirements, and the reviewer responsible for each output.
- Scope Bonelli’s engineering work around ingestion, retrieval, identity, logging, and integration. Include prompts, outputs, backups, and external connections in the data-flow review.
- Test authorized and unauthorized access, unsupported answers, transcription errors, and the path back to the original evidence before expanding use.
- Name who can pause the workflow, approve changes, and handle exceptions once it is in operation.
Can the output replace attorney review?
No. Keep legal judgments and client-facing use under the firm’s review. Treat transcripts, summaries, and retrieved answers as material to check against the source, not as a substitute for it.
Is private AI part of our managed IT agreement?
Do not assume so. Law-firm IT supports the operating environment; Applied AI Engineering scopes the evaluation, build, and production ownership. If the use case is undecided, start with AI assessment and planning.
Discuss a private AI workflow using a non-confidential description. Do not submit client files or privileged evidence through the inquiry form.
Private AI starts with matter and data boundaries
The first design question is not which model to buy. It is what the AI system is allowed to access, which matters or repositories are in scope, what outputs require attorney or staff review, and how the firm can show that sensitive data was handled appropriately.
Client-data control
Define which documents, messages, and repositories are permitted in an AI workflow and which should stay out.
Secure retrieval
Design retrieval over approved knowledge sources with access checks instead of open-ended document dumping.
Human review gates
Keep material legal, client, or business decisions under accountable human control.
Logging and evidence
Maintain practical records of access, prompts, outputs, exceptions, and workflow changes where appropriate.
Law-firm AI workflows to evaluate
- Internal policy and procedure assistants.
- Knowledge retrieval across approved SharePoint or document repositories.
- Client-intake triage and matter-routing support.
- Document summarization with review and confidentiality controls.
- Operations assistants for billing, support, HR, or administrative procedures.
Private AI for law firms: questions before a pilot
What does private AI for law firms need to keep separate?
Start with the firm’s matter-access rules. Identify approved source documents, authorized users and outputs that require attorney review. Include prompts, logs, backups and external connections in that review; a private deployment does not by itself establish confidentiality or legal privilege.
Can a law firm run AI evidence processing on its own hardware?
Yes. Bonelli’s anonymized McKinney case brief describes a local system for video, image and document ingestion, transcription and speaker diarization. That is an example of one implementation, not a guarantee that every model or workflow can run locally.
Is a Microsoft 365 security assessment a private-AI deployment?
No. The assessment reviews identity, sharing and security settings in Microsoft 365. Model selection, matter-level access design, engineering and attorney review need their own scope.
Which law-firm workflows are worth evaluating for private AI?
Consider search across approved documents, document summarization, internal policy questions and client-intake triage. Choose a bounded workflow, define who may access its sources and decide which outputs require review before expanding use.
How should a firm check AI evidence summaries?
Check summaries, transcripts and retrieved answers against the original evidence. Test for unsupported answers, transcription errors and unauthorized access. Keep legal judgments and client-facing use under the firm’s review.
Review Microsoft 365 security before connecting an AI workflow
The assessment reviews identity, access and sharing exposure, security configuration and remediation priorities. Use it to understand the Microsoft 365 environment; agree private-AI engineering and ongoing support separately.
For support of the operating environment, explore Bonelli’s managed IT services.
Do not submit client files or privileged evidence through an inquiry form.
Where Microsoft 365 matters
Many law firms already keep critical work in Microsoft 365. Microsoft 365 AI readiness depends on identity, SharePoint structure, Teams sprawl, external sharing, retention, sensitivity labels, endpoint posture, and administrative access—not just the AI tool itself.
Turn this into a practical operating plan.
Bonelli Systems helps teams connect AI goals, Microsoft 365 readiness, security controls, and compliance evidence into work that can be implemented, monitored, and improved.
Make Microsoft 365 AI readiness concrete.
Download the checklist, review the sample output, or request a readiness review before Copilot or private AI expands access to company data.