Using AI in Law Practice: Safeguards That Make Speed and Accuracy Work Together

Before you ever roll out an AI tool, you need an AI governance framework in place. That's the part a lot of organizations skip because they're excited about the technology. At our firm, we started with governance first. We surveyed employees, involved leadership, HR, IT, operations, and marketing, and made sure everyone understood when AI could be used, what tools were approved, and what information could never be entered into a public large language model.

If you work in legal or healthcare, compliance has to drive those decisions. There are confidentiality obligations, advertising rules, and privacy laws that don't disappear just because a new technology comes along. One of the biggest risks is an employee copying and pasting an email, medical record, or client document into a public AI tool without realizing they're exposing sensitive information. That's an education problem as much as it is a technology problem.

We've also found that industry-specific AI tools outperform general LLMs. On the case management side of our firm, we use EvenUp because it's built specifically for personal injury law. It understands the terminology, the documentation, and the workflows our team already uses. That allows us to spend less time on repetitive administrative tasks and more time focusing on our clients.

AI absolutely helps people do their jobs better. We use it to reduce manual work, identify gaps in documents, and make our team more efficient. But every AI-generated output is still reviewed by a human before it reaches a client. The technology makes us faster. Governance, training, the right industry-specific tools, and human oversight are what make us confident enough to use it on live matters.

We built confidence by creating a record first workflow for catastrophic injury matters. The medical chart is often scattered, and key facts can be hidden in missing or late entries. AI helps us organize large amounts of information, but it cannot decide what the record truly proves. So we collect and index source documents before using any tool for summaries or reviews.

We then check every output with people who understand both medicine and litigation. We watch for changes in terms of timing and causation because small errors can grow quickly. Our rule is simple. If a tool does not save time without changing the meaning of evidence, we do not use it.

The reason why AI made such an impression on me from the very beginning was that it can potentially save a lot of time, yet I have come to realize that time saving is absolutely pointless if people stop trusting its output. Every piece of new technology requires certain boundaries. We implement AI where appropriate, which includes organizing data, summarizing data, coming up with ideas, and creating draft materials. This is not making any legal decisions or final documents, and our lawyers still make all legal analyses and revisions to anything that goes out of the office.

The security that ensures my peace of mind is approaching all AI outputs as just another beginning step and not as an end result. The system never uses confidential information from our clients in unapproved platforms, we strip off all possible identifying characteristics and our lawyers should go through any legal document produced by AI before any other person outside the company gets hold of it. Facts, citations and case references are verified separately since even if AI produces something that sounds very convincing, it can still be wrong. Time-saving is great, but only in situations where there's no need to compromise either time or the clients' security.

We build AI automation for law firms, mostly Social Security disability practices, so this is basically our whole job. Short version, we don't let the AI make the final call on anything with a deadline or a filing attached. It does the reading and the sorting, which is where the speed comes from, and a person still looks at the part that matters.

The thing that actually got us comfortable running this on live cases is dry-run mode. Say we build something to sort incoming SSA mail or draft a client update. Before it does anything for real, it just runs quietly next to the paralegal already doing that by hand, for a few weeks, and we watch whether the two match. It doesn't take over until the logs say it's boring and predictable. Nothing goes live on a hope.

Couple other things. Everything it does gets logged, so if something looks off later we can go back and see what actually happened. When a case is weird, it doesn't try to be clever, it hands it to a person with all the context attached. And if something breaks, we get the alert, not the firm, so we catch it that day instead of two weeks later when a client calls asking why nobody got back to them. On disability work a blown appeal deadline can sink someone's whole case, so that part we're a little obsessive about.

Confidentiality is the simple one. The data never leaves the firm's own accounts. We build inside their systems, each automation only gets the access it needs, and none of it is ever used to train a model. We sign the confidentiality agreement before we touch a file.

The firms that get wrecked by AI are the ones who dumped client data into some random chatbot and crossed their fingers. Treat it like a new hire instead. Give it the boring work, watch it for a while before you trust it, keep a person on the judgment calls, and it's fine.

• Coach Junior Lawyers Under Deadline Pressure
• Delegation That Works in Law Firms: Better First Drafts With Less Rewriting
• How Do Law Professionals Balance Thorough Research With Tight Deadlines?