Is Microsoft Copilot enough for a law firm?

For general drafting and productivity, often yes. For work involving privileged or client-confidential documents, the deciding factor is not capability but whether that material can be put into the tool at all. A seat license sends data to the vendor's environment, so most firms reserve off-the-shelf tools for non-confidential work and use a private deployment where privilege is involved.

Is Microsoft Copilot HIPAA compliant?

Compliance depends on the specific product, the agreement in place, and how it is configured, not on the Copilot brand alone. A business associate agreement and the right enterprise controls are required before any tool touches protected health information. A private deployment is the cleaner path because PHI never leaves infrastructure you control.

What are the alternatives to Microsoft Copilot for regulated industries?

The realistic options are an enterprise tool under the right agreement and controls, a vendor cloud service with a signed BAA or equivalent, or a private deployment inside your own environment. For the most sensitive data, a private deployment is usually preferred because it removes the question of data leaving your perimeter entirely.

Does ChatGPT Enterprise train on our data?

OpenAI states that it does not train its models on ChatGPT Enterprise business data by default. That is meaningfully different from the consumer product. Even so, the data still runs in the vendor's environment, which is the part a regulated security review focuses on, and is the reason sensitive workflows often move to a private deployment.

All comparisons

Comparison Updated June 2026

Soren vs. Microsoft Copilot and ChatGPT Enterprise

Off-the-shelf assistants win for general productivity; a custom build wins when the work depends on your own data, systems, and compliance constraints.

The short answer

If your need is generic productivity inside Microsoft 365 or a general assistant for your staff, buy Microsoft Copilot or ChatGPT Enterprise. They are cheaper, available today, and good at it. Choose a custom build (Soren) when the work depends on how your team actually works, needs a workflow no off-the-shelf seat can perform, or is valuable enough that a flat fee for exactly what you need beats a per-seat subscription for capacity most people never fully use. A private deployment also keeps data inside your perimeter where HIPAA, GLBA, or SOC 2 require it. Most organizations end up using both, for different jobs.

Soren is an AI consulting and deployment firm that builds custom, context-aware AI workflows around the way a team actually works, specialized to its practice areas and trained to get more accurate over time, for banks, law firms, hospitals, and government agencies, deployed inside infrastructure the client controls.

What Copilot and ChatGPT Enterprise are great at

These are excellent products, and pretending otherwise would make this page useless. For a large set of jobs they are the correct purchase, and they are correct today rather than after a build.

Drafting, summarizing, and rewriting everyday documents and email.
General question-answering and research where the answer lives in public knowledge.
Working inside the Microsoft 365 or OpenAI surface your staff already use.
Giving a whole organization a capable assistant in days, with no engineering.

Where off-the-shelf hits a wall

The limits show up the moment the work depends on how your organization actually operates. A general assistant is the same product for every customer: it has no grounding in your systems of record, and it cannot run a process built around your team. Where it runs and what it logs become the problem only after that.

Proprietary context: it has not read your contracts, your policies, your case files, or your codebase, and bolting that on properly is an engineering problem, not a setting.
Bespoke workflows: a multi-step process that reads from your systems, applies your rules, and writes back is not something a chat box does.
Specialization over time: an off-the-shelf seat gets the vendor's general model updates, not a system that grows more accurate at your specific domain the longer you use it.
Deployment location: with a seat license, your data goes to the vendor's environment, which is the exact thing a regulated security review is trying to prevent.
Audit depth: you get the logging the product ships, not a queryable record of inputs, outputs, sources, and model version designed for an examiner.

Custom versus off-the-shelf, side by side

Dimension	Copilot / ChatGPT Enterprise	Soren (custom private deployment)
Fit to how your team works	Same product for every customer	Built around your team and practice areas
Customization to your data	Limited, connector-based	Deep, grounded in your systems of record
How it improves	General vendor model updates	Tuned to your domain, more accurate over time
Pricing model	Per seat or per token; you fund unused capacity	Flat-rate, fixed-scope, only what you need
Setup time	Days, self-serve	Weeks, built around your workflow
Deployment location	Vendor cloud	Your cloud tenant, VPC, or on-premise
Data-training policy	Per vendor enterprise terms	No third-party training on your data
Audit-trail depth	Product-level logging	Inputs, outputs, sources, model version, queryable
Best for	General productivity for everyone	A specific, high-value workflow built for your team

Off-the-shelf assistants versus a custom private deployment.

The compliance question

For a law firm, a hospital, or a bank, the deciding factor is rarely capability. It is whether confidential material can be put into the tool at all. A seat license sends that material to the vendor's environment, and a signed enterprise agreement narrows the risk without changing where the data goes.

Compliance is a property of the deployment, not the model. A private deployment answers the hard security-review question by removing it: the protected data never leaves your perimeter in the first place. We go deeper on this in deploying AI in regulated industries.

Off-the-shelf is not less secure because the model is worse. It is harder to clear because your data has to leave the building to use it.

How the costs actually compare

Microsoft lists Microsoft 365 Copilot at around twenty-one dollars per user per month on an annual commitment as of 2026 (Microsoft), and ChatGPT Enterprise is priced per seat as well. For broad, general use across many people, that per-seat model is hard to beat and you should take it.

A custom build is a flat, fixed-scope cost rather than a recurring per-seat or per-token meter. A per-seat subscription bills the whole team for a broad feature set most of them never fully use; a custom build is priced for exactly the workflow you need and nothing you do not. It earns its price when a single workflow is valuable enough that paying per seat for a generic tool would either never do the job, or fund a lot of capacity that sits idle.

A short decision guide

Choose off-the-shelf if

The job is general productivity for a lot of people.
The answers live in public or non-sensitive knowledge.
You want capability this week with no engineering.
Your compliance posture allows data in the vendor's cloud.

Choose custom if

The work depends on how your team operates and your proprietary systems.
You want a system specialized to your practice that sharpens over time.
You would rather pay a flat fee for exactly what you need than per seat for capacity you do not use.
The workflow is specific enough that a chat box cannot do it.

Frequently asked questions

Is Microsoft Copilot enough for a law firm?: For general drafting and productivity, often yes. For work involving privileged or client-confidential documents, the deciding factor is not capability but whether that material can be put into the tool at all. A seat license sends data to the vendor's environment, so most firms reserve off-the-shelf tools for non-confidential work and use a private deployment where privilege is involved.
When is custom AI worth it over ChatGPT Enterprise?: When the work depends on how your team actually works, needs a multi-step workflow that reads from your systems and applies your rules, or is valuable enough that a flat fee for exactly what you need beats a per-seat subscription for capacity most people never fully use. Keeping data inside your perimeter for compliance is a further reason. For general productivity across many people, ChatGPT Enterprise or Copilot is usually the better and cheaper choice.
Is Microsoft Copilot HIPAA compliant?: Compliance depends on the specific product, the agreement in place, and how it is configured, not on the Copilot brand alone. A business associate agreement and the right enterprise controls are required before any tool touches protected health information. A private deployment is the cleaner path because PHI never leaves infrastructure you control.
What are the alternatives to Microsoft Copilot for regulated industries?: The realistic options are an enterprise tool under the right agreement and controls, a vendor cloud service with a signed BAA or equivalent, or a private deployment inside your own environment. For the most sensitive data, a private deployment is usually preferred because it removes the question of data leaving your perimeter entirely.
Does ChatGPT Enterprise train on our data?: OpenAI states that it does not train its models on ChatGPT Enterprise business data by default. That is meaningfully different from the consumer product. Even so, the data still runs in the vendor's environment, which is the part a regulated security review focuses on, and is the reason sensitive workflows often move to a private deployment.

Trying to work out which path fits your data and your regulator? We can walk through it with you.

Book a demo

Sources