A Guide to Trusting AI Under Pressure
Litigators operate in a high-stakes, high-pressure environment where strategic choices require meticulous preparation and analysis. That’s what makes the ability to work confidently under pressure a required skill. Decisions need to be made quickly, securely, and with as much efficiency as possible. For many firms, generative AI offers an attractive technological solution. AI-powered tools have been developed to streamline workflows across almost every stage of litigation, from early case assessment to trial preparation and strategy.
However, trusting AI when the clock is ticking requires confidence in the technology itself. That’s why navigating the integration of AI into legal work starts with a clear-eyed understanding of the risks and benefits associated with its use.
Confidentiality, Leaps in Logic, and Other Risks
One of the most immediate risks when using AI in legal work involves breaches in confidentiality. When anyone feeds sensitive case data into public or free AI models, that data can be absorbed into the system's training dataset. The issue is that the information absorbed could be reproduced in part, or in whole, during a separate output, thereby inadvertently exposing privileged information to third parties.
Of course, outside of the technical risks, there’s a consideration to be made for an increase in human error. Because AI-generated outputs can sound remarkably human and highly confident, a reviewer may be lulled into a dangerous state of overreliance. Instead of properly vetting an output, legal professionals may find themselves skimming for polished, professional formatting while missing deeper, substantive inaccuracies.
Common among those inaccuracies are leaps in logic. Large language models operate on probability rather than real, actual legal reasoning, which means they run the risk of bridging the gaps that exist in the law with statistically likely assumptions that are often fundamentally flawed.
Sanctions, Citations, and Lessons from the Courtroom
One of the most relevant examples of AI misuse occurred in Mata v. Avianca, Inc., where the plaintiff’s attorneys submitted a brief featuring entirely nonexistent case citations generated by ChatGPT. This became a textbook demonstration of how weak prompting and unverified outputs lead to hallucinations. The presiding judge ultimately imposed a $5,000 fine and required the attorneys to formally notify every single judge they had falsely cited.
A similar situation occurred in Lacey v. State Farm, where a judge sanctioned a plaintiff’s law firm more than $31,000 after they submitted a brief opposing defense privilege claims that included both AI hallucinations and fake citations. The court explicitly noted that the firm had prioritized convenience over accuracy, delivering a steep financial penalty for the negligent use of technology.
In Jordan v. Chicago Housing Authority, following a staggering $24 million verdict, a major law firm submitted a post-trial motion containing multiple fabricated legal citations and error-ridden case summaries generated by AI. As a result, an Illinois circuit court judge imposed a $59,500 penalty.
Why Human-in-the-Loop Verification Matters
The missteps in these cases reflect the challenges of navigating a new technology without an established strategy for verification. Under the pressure of deadlines, it’s understandable how busy practitioners might turn to quick and accessible consumer-grade chatbots, especially in a time before the risks of public models were widely recognized. The conversational fluency of these systems makes it easy to mistake confident phrasing for verified information.
When legal teams use these tools without a formal process to cross-check citations against primary sources, a workflow meant to increase efficiency can quickly lead to unintended complications. Rather than avoiding the technology out of fear, legal teams should safely adopt it along with clear internal guidelines maintained by professional oversight.
In the right hands, AI can be an effective tool for litigation, as long as human lawyers continue to serve as the safeguard for accuracy and accountability.
Leveraging Secure AI for Ediscovery and Review
While consumer chatbots are unacceptable for most litigation, AI that’s built directly into secure ediscovery platforms offers legal professionals enhanced speed and accuracy without bringing undue risk.
Instead of fabricating text based on public internet data, these specialized tools are designed to analyze specific case files with transparency, security, and verification, all built into the software itself. By integrating targeted capabilities into repeatable workflows, legal professionals can move past the fear of sanctions and safely leverage technology to organize, search, and review large document sets under tight deadlines.
A few of the most impactful areas in which AI-powered tools can be especially helpful are:
Concept Clustering: Perfect for use during ECA, tools like Everlaw Clustering can instantly group massive document sets by conceptual similarities. This tool doesn’t require keywords, prior coding, or any training data. Instead, it uses an unsupervised machine learning algorithm that analyzes words and metadata within the documents provided to determine conceptual similarity.
It then provides a visual map of the document set to easily identify themes, related groups of documents, and even outliers within the data.
Clustering is most helpful when legal teams aren’t yet sure what they’re looking for. Instead of starting with assumptions, this tool helps teams understand the full scope of the data, identify unexpected themes, and quickly locate the documents that most likely require further review.
Coding Suggestions: Ediscovery tools like Everlaw Coding Suggestions help quickly achieve accuracy and consistency in document coding and classification. Legal teams can define case context, category guidance, and code criteria, all in plain language. The tool then analyzes documents against those instructions to suggest whether a code should or should not apply.
For example, if a legal team uses a code called “Meetings with FDA regulators,” and the purpose of that code is to capture documents related to such meetings, the code criteria provided could be something like:
“Any direct evidence of interactions between employees of Company A with Federal Drug Administration (FDA) regulators, including, but not limited to evidence or mentions of email communication, phone communication, in–person meetings, etc. Use any title, contact information (like email domains), or contextual information in the document to determine if the individuals involved are employees of Company A or the FDA.”
For each code, Coding Suggestions returns a recommendation along with the rationale behind the suggestion. When available, the tool will also provide a link to the part of the document that is relevant to the rationale provided.
Document Analysis: Generative AI has a lot to offer legal teams by way of quality document analysis. Everlaw Review Assistant, for example, provides summaries, topic analysis, custom extractions, and the ability to ask questions about a document within the document itself.
This tool is particularly helpful when documents are long, dense, or especially unfamiliar. Instead of reading every page, reviewers can use AI-generated summaries or ask targeted questions to better understand their documents more quickly. After that, they can jump to the cited portions of the source document to confirm the necessary information.
In privilege workflows, tools like Everlaw Review Assistant expedite document analysis and help reviewers more quickly identify those documents that might include privileged information.
Advanced Search: One of the key benefits of working with AI-powered tools is the advanced search capabilities they offer. Everlaw Deep Dive is a natural-language investigation tool built for asking questions in plain English across a whole matter and getting citation-backed answers that help teams find facts, prioritize review, and validate their understanding faster.
The tool is designed to work specifically within the document set provided, meaning its response will only include information from within that document set. With Deep Dive, teams can ask focused questions about attorney involvement, instances of legal advice, sensitive topics within the document set, or other potentially privileged information.
AI offers a broad range of practical capabilities that address many of the day-to-day challenges of litigation. By securely automating the more repetitive aspects of document review, this technology provides the efficiency needed to handle tight deadlines with confidence.
How to Select the Right AI Tools
It's tempting to quickly read an AI draft and assume it’s correct because it looks professional. However, as the matters discussed have already illustrated, successful practitioners should treat every AI output as a draft from an incredibly fast, but occasionally overconfident assistant.
Before prompting AI to help summarize a brief or generate coding suggestions, it’s important to recognize the ways in which teams should preemptively set up their workflows to move quickly and confidently. In most instances, this starts with choosing the right tool.
Confirm that the data uploaded will not be used to train either the AI tool in use or any other third party LLM. Practically speaking, this means avoiding free and low-cost AI tools.
Confirm that there is zero data retention within the tool, meaning that users can permanently delete any and all data upon request.
Use purpose-built litigation tools that are designed for practical AI use in legal work. Tools like Everlaw give legal professionals access to generative AI tools in a framework that aids in verification, does not use case data for training, and allows users to fully delete data at will.
Best Practices for Mitigating AI Risk in High-Pressure Litigation
By implementing a few practical habits, legal professionals can confidently integrate advanced AI-powered tools into their daily work.
Leverage citation-based verification. Utilizing outputs that provide hyperlinked citations directly back to the source documents allows for rapid verification. By clicking these links, users can review the surrounding context and confirm that the AI interpreted the facts of the matter correctly.
Review the logic, not just the output. The right AI tools do more than suggest document codes or classify files. In Everlaw, Coding Suggestions provides a plain-language rationale for each recommendation based on user-defined case and coding criteria, with links to the relevant part of the recommended document. Reviewing those rationales helps legal teams verify that the tool’s reasoning aligns with case strategy.
Restrict use to closed environments. True confidence under pressure starts with knowing that client data is completely safe. Legal teams should use secure ediscovery platforms that completely isolate files and don’t use the information to train external models. This foundational safety measure reduces the risk of confidentiality breaches from the very start.
These simple safeguards make it possible to capture the time-saving benefits of automation while mitigating the risk of unverified mistakes. In the right hands, AI can be an incredible tool, provided that human lawyers remain in the loop for accuracy and strategic judgment.
Balancing Speed and Accuracy with Responsible AI
The pressure to do more in less time is real, but so is the opportunity to work more intelligently. As courts, litigators, and legal professionals continue to define what responsible AI use looks like, one thing has become clear: trust in AI comes from process.
The most effective legal teams won’t be the ones that use AI the fastest. They’ll be the ones that use it with the strongest guardrails. By combining purpose-built tools with rigorous verification habits, legal professionals can move quickly without losing sight of accuracy, confidentiality, or strategic control.
