Anonymizing Data for Secure LLM Interactions in Legal Practice

Introduction

AI in the legal profession can be immensely valuable for the generation and analysis of legal documents. However, safeguarding personally identifiable information (PII) is not just a best practice—it's a necessity. Lawyers handle sensitive information daily, making data protection paramount. Lawyers using LLMs for any purpose must ensure that no sensitive data is being leaked to public models. To address this challenge, we developed a tool designed to anonymize PII data, send the anonymized data to a large language model (LLM), and remap the anonymized data to real data after receiving the response. Our approach considers both data security and contextual integrity, providing a robust solution for legal professionals.

The Problem

Creating a system that could effectively anonymize PII data while maintaining the context and relevance of the information sent to the LLM was a complex challenge. The key problems addressed in this project include:

Pseudonymization Strategy for Context Preservation:We must ensure that the model receives enough context about the anonymized data to generate relevant and coherent responses. A random string of characters to disguise original data, for example, may confuse the model. We decided to use a pseudonymization process that would generate fake PII data.
Prompt Engineering to Maintain Pseudonyms:Prompts need to be crafted such that pseudonyms are not altered by the model, ensuring consistency and correctness in the generated output.
Coreferencing Entities:It’s often the case that multiple nouns are used to describe the same entity throughout a prompt, and the only way to track the entity is with contextual understanding. For example: "My name is Ben Benson. Her name is Sally Jones. We call her Sal though! Sal's Phone number is 123-456-7890.” Sally Jones and Sal are the same entity. Traditional text classifiers often fail to understand this.
Re-mapping Anonymized Data:Accurately remapping the anonymized data back to the real data in the response without loss of information or context.
Ensuring Data Security:Maintaining the security and privacy of PII throughout the process, from anonymization to re-mapping.

Solution

1. Pseudonymization for Context Preservation

Pseudonymization in a legal context is like giving characters in a case new names without changing their roles. To ensure the model could still understand the narrative, we replaced identifiable information with pseudonyms. For example, "Jane Smith" became "Client1," and "456 Elm St" turned into "Address1." This approach allowed the LLM to grasp the context and relationships, crucial for generating meaningful responses.

Implementing pseudonymization involved:

Identification of PII: First, we pinpointed all PII within legal documents—names, addresses, phone numbers, email addresses, etc.
Generation of Pseudonyms: Unique pseudonyms were created for each piece of PII, ensuring consistency.
Mapping Pseudonyms to PII: A secure mapping between the original PII and the pseudonyms was maintained, crucial for the re-mapping process after receiving the LLM’s response.

2. Prompt Engineering to Maintain Pseudonyms

Prompt engineering in a legal setting is akin to giving the LLM clear instructions so it doesn’t go off-script. We designed prompts to ensure that pseudonyms remained unchanged by the model. Key strategies included:

Consistent Pseudonyms: Using consistent pseudonyms throughout the prompt to avoid confusion.
Markers or Tags: Employing markers or tags to highlight pseudonyms, making it clear to the model that these should not be altered.
Structured Prompts: Creating structured prompts that provided context while indicating the significance of the pseudonyms.

By implementing these strategies, the prompts provided to the LLM were clear and unambiguous, reducing the likelihood of pseudonyms being altered in the response.

3. Coreferencing Entities

Coreference refers to the situation where multiple expressions in a text refer to the same entity. In legal documents, it is crucial to maintain these references to preserve the context and meaning of the text. Coreferencing entities while anonymizing data involved:

Mapping of Entities: Keeping a mapping of original PII to pseudonyms and ensuring that all occurrences of a particular entity were replaced with the same pseudonym.
Handling Pronouns and References: Developing algorithms to handle pronouns and other references correctly. For example, replacing "Jane Smith" with "Client1" and ensuring that subsequent references like "Jane" were appropriately linked to "Client1."
Maintaining Context: Ensuring that the anonymized data retained the same context and meaning as the original data.

This step was critical in ensuring that the anonymized data remained coherent and understandable to the LLM.

4. Re-mapping Anonymized Data

Once the LLM had processed the anonymized data and generated a response, the next step was to remap the pseudonyms back to the original PII. This re-mapping process had to be accurate and secure to ensure that the response made sense in the context of the original data. The re-mapping process involved:

Maintaining Secure Mapping: Ensuring that the mapping of pseudonyms to real data was secure and accessible only to authorized systems.
Accurate Re-mapping: Implementing checks to ensure that the re-mapping process was error-free and retained the original context.
Post-Processing Checks: Conducting post-processing checks to verify the accuracy and completeness of the re-mapped data.

This step ensured that the response from the LLM was relevant and meaningful, with the original PII correctly restored.

5. Ensuring Data Security

Data security was a critical aspect of this project. Throughout the process, measures were taken to ensure that PII was protected at all stages, from anonymization to re-mapping. Key security measures included:

Secure Environment: Any mappings to sensitive are stored in a secure environment.
Short-lived mappings: Mappings to not need to live longer than the conversation. Thus, mappings are short-lived.

By implementing these security measures, the project ensured that PII was protected at all times, reducing the risk of data breaches and unauthorized access.

Results

The solution successfully anonymized PII data, processed it with the LLM and re-mapped the anonymized data to the original PII without loss of context or information. This approach safeguarded sensitive information while maintaining the integrity and relevance of the data throughout interaction with the LLM.

The project demonstrated the effectiveness of the pseudonymization and re-mapping processes, with the following outcomes:

Data Security: PII was protected throughout the process, with no unauthorized access or data breaches.
Context Preservation: The anonymized data retained its context and meaning, enabling the LLM to generate relevant and coherent responses.
Accurate Re-mapping: The re-mapping process was accurate, ensuring that the original PII was correctly restored in the LLM's response.
Compliance: The project complied with data protection regulations, protecting the privacy and rights of individuals.

Conclusion

This project demonstrated that it is possible to effectively anonymize PII data, leverage the power of LLMs, and maintain data security and context integrity within the legal profession. The key to success lay in meticulous pseudonymization, prompt engineering, and robust data re-mapping processes. This solution provides a framework for securely integrating LLMs into legal practices, paving the way for more secure and efficient AI-driven solutions.

By addressing the challenges of pseudonymization, prompt engineering, coreferencing, and data security, the project achieved its goal of anonymizing PII data while maintaining the utility and relevance of the data. This approach can be applied to various legal applications, from client communications to case analysis, ensuring that sensitive data is protected while leveraging the capabilities of LLMs.

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