Practitioner, not theorist
The advice comes from a firm that runs AI in its own legal practice daily — every recommendation has been tested on the firm's own files first.
Aun & Co. helps firms and businesses adopt AI safely: governance, confidentiality, vendor review and supervised rollout — grounded in the firm's own daily use.
Legal AI Implementation is guidance for organisations that want AI working inside their practice without gambling their confidentiality, privilege or regulatory standing. It is grounded in lived experience: Aun & Co. runs AI in its own operations — intake, scheduling, document preparation — with every output behind human approval. The firm advises on adoption the way it practises adoption: capability first, control always.
The firm implements before it recommends. Each tool the firm advises on is assessed against the same tests applied internally: where the data goes, who can see it, what the vendor's terms actually permit, and where the human approval gate sits. Deliverables are operational — a policy people can follow, a checklist a vendor must pass — not slideware.
The advice comes from a firm that runs AI in its own legal practice daily — every recommendation has been tested on the firm's own files first.
Policies written at the level of the actual workflow: what may be pasted where, who approves what, and what gets logged.
Data flows are designed so AI assistance does not become an accidental waiver of confidentiality or professional privilege.
Training rights, retention, sub-processors and jurisdiction pulled out of the fine print and translated into a go or no-go decision.
The firm has built usage policies defining approved tools, prohibited inputs and human-approval gates, sized for small professional teams rather than enterprises.
A structured review of competing tools' data-handling terms, producing a comparison an operations lead could act on without reading a single contract.
Designing which document classes may pass through which systems, so efficiency gains never touch the most sensitive tier of client material.
A phased introduction — training, sandboxing, then live use with review checkpoints — turning an informal habit into a governed capability.
Described in abbreviated, anonymised form to preserve client confidentiality.
Yes, if the data flow is controlled: choosing tools whose terms exclude training on your inputs, restricting what may be submitted, and keeping a human approval gate on outputs. The breach risk is in unmanaged habits, not in the technology itself.
Approved tools, prohibited input categories, human-review requirements, logging, and an owner responsible for the policy. It should fit on a few pages and match how people actually work — a policy nobody follows protects nobody.
Four questions first: does the vendor train on your data, where is it stored and for how long, who are the sub-processors, and what happens on exit. If the terms cannot answer these plainly, that is itself the answer.
An inventory of tools already in use — formally and informally — a map of what data currently flows into them, and a gap list against confidentiality and regulatory duties. It ends with a prioritised, realistic adoption roadmap.