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Local LLMs26 October 20266 min read

Private AI for regulated industries: on-premise LLMs for law, health and finance

Law firms, clinics and financial teams handle material they may not be free to send to an outside service. Running a model on infrastructure you control can keep that material inside your boundary, but it does not make a deployment compliant by itself.

In this post
  1. Why organisations choose it
  2. What you still have to do
  3. Questions to answer first
  4. A note on compliance
  5. Sources and further reading

Why organisations choose it

  • Data stays on systems the organisation controls.
  • Data-residency and contract limits are easier to meet.
  • The system can run in a network with no outside access.
  • Costs are predictable once the hardware exists.

What you still have to do

  • Access control. Restrict who can use the model and what documents it can read.
  • Logging and retention. Decide what is recorded, who sees it and for how long.
  • Isolation. Segment the network; log and review administrator access.
  • Updates. You own patching for the runtime, the drivers and the model.

Questions to answer first

  1. What data will the system touch, and under which rules?
  2. Who may use it, and for what?
  3. What happens when it is wrong, and who checks?
  4. Who is accountable for the system?

A note on compliance

Regulations differ by country and sector. Ask your compliance or legal team what applies. Nothing in this post is legal advice.

Sources and further reading

  1. NIST. AI Risk Management Framework (AI RMF 1.0). nist.gov/itl/ai-risk-management-framework.

IICTP is a new independent credential and is not ISO/IEC 17024 accredited.

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