How generative AI is transforming legal tech with AWS

Legal professionals often spend a significant portion of their work searching through and analyzing large documents to draw insights, prepare arguments, create drafts, and compare documents. The rise of generative artificial intelligence (AI) has brought an inflection of foundation models (FMs). These FMs, with simple instructions (prompts), can perform various tasks such as drafting emails, extracting key terms from contracts or briefs, summarizing documents, searching through multiple documents, and more. As a result, these models are fit for legal tech. Goldman Sachs estimated that generative AI could automate 44% of legal tasks in the US. A special report published by Thompson Reuters reported that generative AI awareness is significantly higher among legal professionals, with 91% of respondents saying they have heard of or read about these tools.

However, such models alone are not sufficient due to legal and ethical concerns around data privacy. Security and confidentiality are of paramount importance in the legal field. Legal tech professionals, like any other business handling sensitive customer information, require robust security and confidentiality practices. Advancements in AI and natural language processing (NLP) show promise to help lawyers with their work, but the legal industry also has valid questions around the accuracy and costs of these new techniques, as well as how customer data will be kept private and secure. AWS AI and machine learning (ML) services help address these concerns within the industry.

In this post, we share how legal tech professionals can build solutions for different use cases with generative AI on AWS.

AI/ML on AWS

AI and ML have been a focus for Amazon for over 25 years, and many of the capabilities customers use with Amazon are driven by ML. Ecommerce recommendation engines, Just Walk Out technology, Alexa devices, and route optimizations are some examples. These capabilities are built using the AWS Cloud. At AWS, we have played a key role in and making ML accessible to anyone who wants to use it, including more than 100,000 customers of all sizes and industries. Thomson Reuters, Booking.com, and Merck are some of the customers who are using the generative AI capabilities of AWS services to deliver innovative solutions.

AWS makes it straightforward to build and scale generative AI customized for your data, your use cases, and your customers. AWS gives you the flexibility to choose different FMs that work best for your needs. Your organization can use generative AI for various purposes like chatbots, intelligent document processing, media creation, and product development and design. You can now apply that same technology to the legal field.

When you’re building generative AI applications, FMs are part of the architecture and not the entire solution. There are other components involved, such as knowledge bases, data stores, and document repositories. It’s important to understand how your enterprise data is integrating with different components and the controls that can be put in place.

Security and your data on AWS

Robust security and confidentiality are foundations to the legal tech domain. At AWS, security is our top priority. AWS is architected to be the most secure global cloud infrastructure on which to build, migrate, and manage applications and workloads. This is backed by our deep set of over 300 cloud security tools and the trust of our millions of customers, including the most security sensitive organizations like government, healthcare, and financial services.

Security is a shared responsibility model. Core security disciplines, like identity and access management, data protection, privacy and compliance, application security, and threat modeling, are still critically important for generative AI workloads, just as they are for any other workload. For example, if your generative AI applications is accessing a database, you’ll need to know what the data classification of the database is, how to protect that data, how to monitor for threats, and how to manage access. But beyond emphasizing long-standing security practices, it’s crucial to understand the unique risks and additional security considerations that generative AI workloads bring. To learn more, refer to Securing generative AI: An introduction to the Generative AI Security Scoping Matrix.

Sovereignty has been a priority for AWS since the very beginning, when we were the only major cloud provider to allow you to control the location and movement of your customer data and address stricter data residency requirements. The AWS Digital Sovereignty Pledge is our commitment to offering AWS customers the most advanced set of sovereignty controls and features available in the cloud. We are committed to expanding our capabilities to allow you to meet your digital sovereignty needs, without compromising on the performance, innovation, security, or scale of the AWS Cloud.

AWS generative AI approach for legal tech

AWS solutions enable legal professionals to refocus their expertise on high-value tasks. On AWS, generative AI solutions are now within reach for legal teams of all sizes. With virtually unlimited cloud computing capacity, the ability to fine-tune models for specific legal tasks, and services tailored for confidential client data, AWS provides the ideal environment for applying generative AI in legal tech.

In the following sections, we share how we’re working with several legal customers on different use cases that are focused on improving the productivity of various tasks in legal firms.

Boost productivity to allow a search based on context and conversational Q&A

Legal professionals store their information in different ways, such as on premises, in the cloud, or a combination of the two. It can take hours or days to consolidate the documents prior to reviewing them if they are scattered across different locations. The industry relies on tools where searching is limited to each domain, and may not flexible enough for users to search for information.

To address this issue, AWS used AI/ML and search engines to provide a managed service where users can ask a human-like, open-ended generative AI-powered assistant to answer questions based on data and information. Users can prompt the assistant to extract key attributes that serve as metadata, find relevant documents, and answer legal questions and terms inquiries. What used to take hours can now be done in a matter of minutes, and based on what we have learned with our customers, AWS generative AI has been able to improve productivity of resources by up to a 15% increase compared to manual processes during its initial phases.

Boost productivity with legal document summarization

Legal tech workers can realize a benefit from the generation of first draft that can then be reviewed and revised by the process owner. Multiple use cases are being implemented under this category:

• Contract summarization for tax approval
• Approval attachment summarization
• Case summarization

The summarization of documents can either use existing documents and videos from your document management system or allow users to upload a document and ask questions in real time. Instead of writing the summary, generative AI uses FMs to create the content so the lawyer can review the final content. This approach reduces these laborious tasks to 5–10 minutes instead of 20–60 minutes.

Boost attorney productivity by drafting and reviewing legal documents using generative AI

Generative AI can help boost attorney productivity by automating the creation of legal documents. Tasks like drafting contracts, briefs, and memos can be time-consuming for attorneys. With generative AI, attorneys can describe the key aspects of a document in plain language and instantly generate an initial draft. This new approach uses generative AI to use templates and chatbot interactions to add allowed text to an initial validation prior to legal review.

Another use case is to improve reviewing contracts using generative AI. Attorneys spend valuable time negotiating contracts. Generative AI can streamline this process by reviewing and redlining contracts, and identify potential discrepancies and conflicting provisions. Given a set of documents, this functionality allows attorneys to ask open-ended questions based on the documents along with follow-up questions, enabling human-like conversational experiences with enterprise data.

Start your AWS generative AI journey today

We are at the beginning of a new and exciting foray into generative AI, and we have just scratched the surface of some potential applications in the legal field—from text summarization, drafting legal documents, or searching based on context. The AWS generative AI stack offers you the infrastructure to build and train your own FMs, services to build with existing FMs, or applications that use other FMs. You can start with the following services:

• Amazon Q Business is a new type of generative AI-powered assistant. It can be tailored to your business to have conversations, solve problems, generate content, and take actions using the data and expertise found in your company’s information repositories, code bases, and enterprise systems. Amazon Q Business provides quick, relevant, and actionable information and advice to help streamline tasks, speed up decision-making and problem-solving, and help spark creativity and innovation.
• Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. With Amazon Bedrock, you can experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that perform tasks using your enterprise systems and data sources.

In upcoming posts, we will dive deeper into different architectural patterns that describe how to use AWS generative AI services to solve for these different use cases.

Conclusion

Generative AI solutions are empowering legal professionals to reduce the difficulty in finding documents and performing summarization, and allow your business to standardize and modernize contract generation and revisions. These solutions do not envision to replace law experts, but instead increase their productivity and time working on practicing law.

We are excited about how legal professionals can build with generative AI on AWS. Start exploring our services and find out where generative AI could benefit your organization. Our mission is to make it possible for developers of all skill levels and for organizations of all sizes to innovate using generative AI in a secure and scalable manner. This just the beginning of what we believe will be the next wave of generative AI, powering new possibilities in legal tech.

Resources

• Securing generative AI: An introduction to the Generative AI Security Scoping Matrix
• AWS Security Reference Architecture (AWS SRA)
• AWS Responsible AI

About the Authors

Victor Fiss a Sr. Solution Architect Leader at AWS, helping customers in their cloud journey from infrastructure to generative AI solutions at scale. In his free time, he enjoys hiking and playing with his family.

Vineet Kachhawaha is a Sr. Solutions Architect at AWS focusing on AI/ML and generative AI. He co-leads the AWS for Legal Tech team within AWS. He is passionate about working with enterprise customers and partners to design, deploy, and scale AI/ML applications to derive business value.

Pallavi Nargund is a Principal Solutions Architect at AWS. She is a generative AI lead for East – Greenfield. She leads the AWS for Legal Tech team. She is passionate about women in technology and is a core member of Women in AI/ML at Amazon. She speaks at internal and external conferences such as AWS re:Invent, AWS Summits, and webinars. Pallavi holds a Bachelor’s of Engineering from the University of Pune, India. She lives in Edison, New Jersey, with her husband, two girls, and a Labrador pup.