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Knowledge base Updated: February 5, 2026

AI in Legal Research: How to Ensure Compliance and Confidentiality of Queries

Traditional legal research is time consuming . New AI assistants can analyze hundreds of rulings at once . But can they be trusted and does their use comply with regulations?

Łukasz Szymański Łukasz Szymański

Legal research - that is, searching and analyzing regulations, case law and literature - is the daily bread of every law firm. Traditionally, it involves painstaking digging through databases, which is time-consuming and fraught with the risk of missing something .

Modern legal chatbots and AI assistants promise a revolution, understanding questions asked in natural language . However, every inquiry describing a client’s facts sent to an external AI cloud is a potential risk of violating RODO and professional secrecy. The foundation here is the Compliance/GRC Suite, which helps design this process in an auditable and secure way.

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Legal research - that is, searching and analyzing regulations, case law and legal literature - is the daily bread of every law firm. Traditionally, it involves formulating queries and digging through sources: codes, commentaries, case law databases. Lawyers had to spend long hours over a book collection or in an electronic legal database, typing successive passwords, filtering results, reading judgments in search of a similar case. It’s an occupation that requires patience and experience - knowing where to look and how to formulate a query to find what you need. The limitations? First, time. In an urgent case, searching all potentially relevant sources is difficult, so that an important judgment or regulation may be overlooked. Second, fragmentation of results - traditional search engines rely on keywords, so if you don’t hit perfectly with a keyword, relevant results may not appear. Third, timeliness and scope - the law is dynamic, and there are many sources (not only national laws, but also EU, international, local laws). It is difficult for a person to be sure that he has covered everything. Research can consume a lot of energy, and still leave some anxiety: “are you sure I haven’t missed anything?”. Under such conditions, the need for more efficient methods arises - and this is where AI steps in, promising to speed up and deepen the process of finding information.

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How can AI speed up the search for rulings and regulations?

Artificial intelligence is revolutionizing legal research, making it more like talking to a wise assistant than digging through a library catalog. Thanks to techniques natural language processing, AI can understand a lawyer’s question asked in normal language and find the answer to it in huge collections of legal texts. For example, instead of typing the phrase “Article 429 of the Civil Code tort liability case law” into the database, a lawyer can ask AI: “How do the courts interpret Article 429 of the Civil Code in the context of liability for subcontractors?”. A well-trained AI model will analyze a huge number of judgments and extract the essence - for example, it will state: “The Supreme Courts have ruled that Article 429 of the Civil Code does not exclude the main contractor’s liability if… (and here’s the key conclusion),” and then it will provide a list of rulings for confirmation. It’s as if a lawyer had super search engine, which not only finds documents, but immediately reads and summarizes them. AI also speeds up searches because it works in parallel and contextually. This means that even very complex queries - combining different threads - can be handled in a single step. For example: “Find rulings from the last 5 years regarding warranty on sales where the customer was a consumer and damages were awarded.” An ordinary search engine might have trouble with such a precise query, and AI will interpret it and filter the results. Studies show that AI tools can conduct research of considerable complexity faster than a human - for example, there is a well-known comparison where the ROSS Intelligence AI system found relevant precedents 30% faster than a lawyer. It’s not even about seconds, but about the fact that a human can give up after 5 hours, while AI is still methodically processing gigabytes of text. In practice, this means that an issue that used to require two days of research, for example, can now be largely resolved in a matter of hours - a lawyer gets to the answer faster, or at least narrows the search field.

A traditional search engine - be it LEX, Legalis or even Google - relies mainly on keyword matching and simple filters. It works a bit like an index in a book: find documents where word X or phrase Y appears. AI, on the other hand, acts more like an intelligent consultant. Instead of just searching for words, it tries to understand the meaning of the question and the legal context. This allows it to find answers even where literal keywords are missing. Example: we traditionally ask “auxiliary liability of a partner of a general partnership ruling.” We will get rulings where these words are present. Asking the AI, “Is a partner of a general partnership liable for the partnership’s debts with all his assets?”, the system can cite the relevant laws and rulings, although they may use different terminology (e.g., “subsidiary liability”). Another difference is that AI can scintillate information from multiple sources at once. A classic search engine will list 100 judgments for us and we have to read them ourselves. AI can read them for us and, for example, generate a summary: “In 80% of the judgments, the courts found A, but in 20% they found B, which is due to a difference in the facts.” This makes a huge difference in convenience. In addition, AI tends to be more interactive - you can query, narrow down the question and get refined answers without going out of context. A traditional search engine will simply show the result, and if you formulate the query incorrectly, you won’t find anything meaningful. Finally, AI can (in some implementations) take into account the timeliness of regulations - for example, know that a particular article of a law has been repealed or amended and suggest the current state of the law, which an ordinary search engine will not do on its own. Of course, these advantages of AI require an advanced background and huge data to “read,” but the effect for the user is that he gets an answer, not just a list of results. So you could say that the difference between AI and a classical search engine is like between a library and a legal advisor: the library will make the books available, but it is the advisor who will summarize and tell you what the results are.

Can AI tools analyze hundreds of judgments simultaneously?

Yes - and this is one of their biggest advantages. For AI, analyzing hundreds or even thousands of rulings is simply an operation on a set of data, which it performs in bulk and in parallel. While we read the rulings one by one, AI can “read” many at once, because it has the computing power to process the text on a massive scale. For example, if we have 500 rulings on franking and we want to find in them different ways to apply an abusive clause, AI can read all 500, identify passages that speak of abusive clauses and categorize the courts’ arguments. In a few minutes, for example, we get information that 300 rulings invalidated the contract, 150 upheld but modified the course, and the rest - other solutions, along with a list of references and justifications to review. No human can process such a mass of case law so quickly. What’s more, AI won’t get lost in it - it has an absolute memory for what it has processed. If you ask a cross-sectional question (“how did the Supreme Court’s jurisprudence change between 2018 and 2022 in case X?”), it is able to compare it and draw a trend. Already in the UK or the US, such mass analysis is being used, for example, to create so-called judge analytics - AI systems analyze all the rulings of a given judge to determine what tendencies he or she has (pro-claimant, pro-class action, pro-consumer, etc.). For lawyers, this is valuable data when planning litigation strategy. In Polish realities, too, one could imagine reviewing the mass of rulings in search of, for example, differences between Warsaw and Krakow appellate courts in certain cases. AI will do it without a problem. However, it’s important to realize that while AI will read everything, it won’t always interpret the context perfectly - e.g., it may need guidance on what to pay attention to. That’s why often the process looks like this: the lawyer “feeds” the system a large number of rulings, gets statistical results and lists, and then reviews the key passages (which AI will point out to him anyway) on his own. Even so, just getting to those passages is an incredible improvement. To sum up: yes, AI is such a super-fast analyst that can read whole volumes of case law and draw generalized conclusions or find specifics - something that would be titanic work for a human team.

This is a very important issue: can we trust AI answers 100%? Generative models, like GPT, have made a huge impression with their ability to formulate answers that sound like they were written by a human. However, there are times when AI “hallucinates” - that is, it creates something that sounds plausible, but may not be true. In a legal context, this could be, for example, an invented ruling or a twisting of a legal thesis. Therefore, at this stage, AI answers cannot be taken for granted without verification. Good AI tools try to remedy this by supporting answers with specific sources - for example, quoting a passage from a judgment or indicating the article number of a law . If AI gives the answer “The Supreme Court stands by X” and next to it we have a footnote with the reference of the ruling, this is verifiable and inspires more confidence. However, in practice, lawyers must be vigilant. AI will give the correct answer if the right question is asked and if the answer is actually in the data. It handles simple things just fine - such as asking about the current legal status of a given provision (if the database is up to date) or general legal doctrine. On complex questions - such as whether a particular state of facts falls under a given provision - AI can get confused or not have the full picture, especially if it requires interpretation rather than just knowledge. That’s why there is increasing talk of an approach in which AI is the first-line tool: it gives the initial answer, points out the clues, and the lawyer then checks and clarifies everything. A notable fact is worth citing here: the GPT-4 (OpenAI’s AI model) “passed” the US bar exam and scored in the top 10% of passers - that is, it can answer a great many legal questions correctly. This is impressive, but the exam is a test of knowledge, not responsibility for someone’s case. Therefore, in real-world use, there will always be an element of human control. It’s best to treat AI like a savvy junior lawyer: it will prepare the analysis, but the partner still has to review it before it goes to the client. As the technology develops, the number of errors will diminish, but the rule will probably remain that AI is meant to support, not replace, a lawyer’s judgment.

How do lawyers use AI in research in practice?

The applications are manifold. Many lawyers simply start by using popular language models (e.g., ChatGPT) as a helper: they ask them a legal question to see what they will answer, and then verify. It’s a quick way to get an initial grasp of a topic, especially from areas where the lawyer doesn’t practice every day. More advanced uses are specialized platforms. Thomson Reuters, LexisNexis and other industry giants have released their own AI assistants integrated with their databases - Westlaw Edge, for example, has a feature that answers questions in full sentences and provides footnotes to legislation or rulings. In-house lawyers (in-house lawyers for companies) are also using AI to monitor changes in the law: the systems can automatically scan the law journals, inform them of new acts, and immediately summarize what has changed and how it might affect the company’s business. In law firms, we are also seeing a combination of research and document writing - for example, a lawyer instructs AI to “draft a lawsuit based on such and such circumstances, taking into account the latest case law,” and the tool generates a draft lawsuit with built-in footnotes to the rulings that support the arguments. It sounds futuristic, but such functionality is offered by, for example, the aforementioned CoCounsel or the new modules of Harvey AI (a system implemented at Allen & Overy, for example). In practice, lawyers appreciate that AI can quickly do the groundwork: gather quotes from rulings on a given thesis, generate a list of arguments for and against a particular interpretation, create a table comparing regulations in different countries (if it has access to this data). It’s like having a very capacious memory and a huge “knowledge base” at your fingertips. It’s also important that AI is sometimes available 24/7 and responds immediately - lawyers tell us that the night before the hearing they could still ask the chatbot about a thread and got a few extra points to include in the closing speech. Of course, approaches vary: some lawyers are cautious and treat AI only as a curiosity, others integrate it heavily into their workflow. Nonetheless, the trend is that increasingly legal research is a joint effort between human and AI - the human formulates the problem and evaluates the outcome, the AI does the hard work of digging into the texts.

Several leading solutions have already emerged. Here are some of them:

  • ROSS Intelligence - although this company has gone out of business, it was one of the pioneers. Their AI (based on IBM Watson) was able to answer legal questions based on analysis of US case law. The aforementioned +30% speed relative to humans in finding case law became famous .

  • Westlaw Precision / Edge - Thomson Reuters has added AI-based features in its products, like Westlaw Answers. A lawyer types in a question and the system searches a database of rulings and comments to give a specific answer, usually with links to sources. In addition, TR launched the CoCounsel assistant after acquiring Casetext - he integrates research with other tasks.

  • Lexis+ AI - rival LexisNexis is not being left behind. Their Lexis+ platform has built-in NLP mechanisms for understanding questions and generating summaries of rulings or documents. There have also been announcements of a tool called Lexis AI capable of complex tasks (announced in 2023).

  • Harvey - a GPT-based tool that gained notoriety when law firm Allen & Overy announced it was implementing it for all lawyers. Harvey can answer legal questions in dozens of languages and generate documents using legal knowledge. Allen & Overy boasted that it was like hiring a virtual super-applicant available globally.

  • Barista - that’s the name of the experimental chatbot being tested by the Supreme Court of India to help judges summarize lengthy case files. Its job is precisely research: to extract key facts and rulings cited by the parties.

  • Other tools: CaseText (which has become CoCounsel), Blue J Legal (specialized in tax law performance forecasting), JPMorgan COIN (for searching financial and regulatory documents). In Poland, we don’t yet have a dedicated Polish legal chatbot on a large scale, but work is underway and we will probably live to see native solutions integrating our legal base.

All of these tools have a common idea: make it faster and easier to get to legal information. They differ in interface and scope of specialization, but ultimately most will operate on a similar basis - you ask a question, you get an answer with references, possibly an opportunity to dig deeper.

Good AI systems are trained to understand not only colloquial language, but also legal terminology. Models such as the GPT-4 have “read” millions of pages of texts in the learning process, probably including the texts of laws, contracts, legal articles. As a result, they associate the meaning of even quite specialized terms. If we ask: “Is a claim for a retainer subject to the statute of limitations, and after what period of time?”, the AI will correctly understand what is meant by “retainer claim” (something that sounds exotic to a layman). The models are also learned through interaction - if multiple users ask about a particular issue, the AI “knows” that it’s something important. Of course, to fully understand the legal language, the model should be additionally trained on the legal corpus of the country. And this is happening - dedicated models are being created, fed with judgments, legal acts in different languages. In the Polish context, there are also initiatives to train AI on our laws and judgments, which will allow it to better “feel” the peculiarities of legal language (e.g. long sentences, frequent references, Latin phrases). It is worth noting that lawyers often use mental abbreviations or certain jargon - e.g. “524 kpc” (i.e. Article 524 of the Code of Civil Procedure). A well-prepared system will recognize this as well, but here much depends on the implementation. Some tools even have built-in dictionaries and OCR mechanisms to, for example, capture article numbers from a scan of a pleading and immediately check their content in the actual law. In general, we are at a stage where AI can handle legal language quite well - as evidenced by the fact that GPT-4, as mentioned, passed the legal exam, or that specialized chatbots are able to answer questions from tax or patent law using the terminology correctly. Of course, sometimes a lapsus can happen (such as misinterpreting an abbreviation if it is ambiguous), but that’s what human review is for. Each month AI “learns” more as it gets feedback from users, so its understanding of legal language will continue to improve. It is safe to say that AI has no complexes when faced with paragraphs - it reads and understands them often faster than we do.

Does the use of AI in research reduce the risk of overlooking relevant sources?

Definitely yes - this is one of the main advantages of using AI in information retrieval. When we work manually, we often focus on familiar sources and areas of the law that we consider crucial. We may inadvertently overlook, for example, a ruling from another area that could nevertheless be analogously applied, or some amendment to the law that we have not heard of. AI, especially when it has a complete and up-to-date dataset, will search everything it has in its “sight” - without bias, without fatigue. If there is a paragraph in some distant sentence that perfectly fits our case, AI will probably find it, as long as the query doesn’t confuse it. This reduces the risk that something will escape the attention of the legal team. We mentioned earlier that some analysis shows a huge percentage of legal tasks that AI could theoretically take over. Goldman Sachs suggested that as many as 44% of tasks in the legal sector could be automated . That would mean that roughly the amount of work we put into tedious searching and reviewing could be done by a machine - including the work of finding needles in a haystack. In short: AI is great at combing through the haystack. It won’t get bored after 10 hours, and it won’t overlook a small needle because it was a different color than the previous ones. Of course, you have to remember that AI is also limited by what it has in its base. If a source has not been fed into the system (for example, a very fresh law that has not yet been added), it will not conjure it up. Therefore, the timeliness and completeness of the data feeding AI is important. Assuming, however, that we are working on decent collections (e.g., all court decisions from the last decades, all legal acts and commentaries), then yes - AI significantly reduces the risk of missing something important. Lawyers can be more confident that they are basing themselves on the full legal picture. This doesn’t mean he can stop being vigilant - it’s always worth checking, for example, whether AI has missed something by misinterpreting a query. But in practice, especially in areas where there is a lot of data, AI is doing an excellent job as a sieve catching everything that is potentially important. It can be said that instead of relying on luck in research (“I hope I didn’t miss anything”), a lawyer can rely on AI as an assurance.

The use of AI in the preparation of opinions or legal advice can enhance their quality - provided, of course, that it is used judiciously. First, the opinion is more comprehensive and supported by more extensive research. Since AI helps quickly access unusual sources or recent rulings, a lawyer can include arguments and examples in his or her opinion that he or she would not otherwise know about. This makes the analysis more complete. Secondly, AI can suggest different points of view (e.g., for and against some interpretation), so the lawyer can include a more complete argument in the opinion - not just the one that first came to mind. This is important especially with difficult issues, where balancing rationales and pointing out risks is crucial. Third, AI can speed up the preparation of an opinion, and sometimes this determines its usefulness. If a client gets an analysis the day after asking a question (because the lawyer, with the help of AI, has quickly gathered materials and thought through the case), the practical value of such advice is greater than a late, even if very brilliant, opinion. What about the potential dangers? One must keep an eye on AI to ensure that it does not introduce an error in the opinion - for example, through some unverified quote or overinterpretation. That’s why some law firms have procedures: if an opinion uses data from AI, every quote must be manually checked against the original source. But that’s a small price to pay for being able to get to those citations quickly. In general, clients may not even know that the lawyers used AI in their case - they will simply see a reliable, reference-rich opinion, often prepared in record time. And this is the main advantage: quality in the sense of substance (completeness, timeliness, multifaceted arguments) plus timeliness. In the future, it will probably become the norm to attach some “AI report ” to the opinion - e.g., a list of rulings with generated theses - which will increase transparency and show the client that the analysis covered the broadest possible spectrum. To conclude: AI, used wisely, is like an additional expert consulted when writing an opinion. The final opinion belongs to the lawyer, but thanks to AI it can be delivered with more certainty and supported by stronger material.

LegalTech Revolution : Artificial Intelligence in the Service of Law Firms](https://nflo.pl/ebook-legaltech/)

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