Regarding large language models and data loss prevention, most discussions today revolve around the growing need for DLP for LLMs. They are constantly exposed to sensitive data during training and through user prompts. However, there’s another dynamic to the relationship between LLMs and DLP. A less talked about fact is that LLMs can be integrated with DLP solutions to enhance their accuracy and overall functionality. As data protection laws take hold across the world and the consequences of data loss become more severe, let’s take a closer look at the transformative potential that LLMs bring to the table, offering deeper and more accurate insights into data and its security.

Traditional DLP and its Limitations

Data Loss Prevention solutions are designed to protect sensitive data and information by detecting and blocking unauthorized data transfers over the network. Sensitive data may include records and documents containing personally identifiable information (PII), financial transactions, patent filings, legal documents, confidential contracts and more. Typically, organizations can choose between dedicated DLP solutions for endpoints, networks, cloud environments and email, or a comprehensive cloud-based DLP, often included as part of a complete secure access service edge (SASE) or security service edge (SSE) portfolio, that covers the entire digital environment.

Traditionally, DLP prevents accidental or malicious data loss, leaks and breaches through multiple steps, which include data classification for identifying and categorizing sensitive data within the organization, policy creation for defining the rules for how authorized entities can access, share and use this sensitive data, and finally, continuous monitoring and analysis of data activity across the organization’s systems. These tools can identify violations of the DLP policies and enforce measures like data encryption or blocking certain actions like data transfers for highly critical data.

Traditional DLP solutions rely on techniques like pattern-matching for data classification. While this technique excels at identifying specific patterns, predefined keywords, or formats, such as credit card numbers or social security numbers, it falls short when it comes to nuanced document/information classification. For starters, it fails to understand the context of the information. For example, it might flag an email containing an employee’s address even if it’s just a harmless birthday invitation. This reliance on specific patterns leads to a high number of false positives, resulting in resource wastage, workflow disruptions and user frustration.

Secondly, malicious actors can mask sensitive data through slight variations in formatting or wording to render pattern-matching techniques ineffective. Many DLP solutions may not be configured to scan for Cyrillic characters or other non-Latin alphabets, allowing cybercriminals to substitute Latin characters with visually similar Cyrillic characters before transferring sensitive data over the network.

Essentially, traditional DLP can identify clear violations but struggles with anything outside the strict criteria. This limitation is especially problematic because many sensitive documents don’t have specific keywords or patterns that distinguish them from others. Financial reports, trade secrets, or confidential M&A discussions all require a deeper understanding of the content. This necessitates a shift towards more sophisticated DLP solutions that can analyze full text for a more effective approach to data security.

How LLMs Overcome DLP’s Context Blindness

The best approach to enrich DLP solutions with context awareness is through advanced natural language processing tools and techniques, particularly LLMs. LLMs are trained on vast amounts of data to understand the nuances of human language, allowing for a more intelligent and context-aware approach to DLP.

Unlike pattern-matching, LLMs analyze the entire text of a document, which allows them to grasp the context and meaning of the information. For instance, an LLM can differentiate between the keyword “social security number” casually mentioned in a harmless email thread versus when it is included in a financial transaction document. This contextual awareness happens because LLMs can go beyond identifying keywords to understand the semantic meaning of words and phrases for accurate data and document classification.

This contextual and semantic understanding can be achieved through specialized LLMs like Sentence-BERT, which implements bidirectional (from left to right and right to left) processing to understand the nuanced meaning of words in different contexts. S-BERT creates a compact numeric representation (i.e., vector) for entire sentences that captures semantic relationships and contextual information and uses a technique called contrastive learning to further ensure accurate contextualization and classification.

Future-Proofing DLP With LLMs

LLM-powered DLP can enforce complex rules to define what constitutes sensitive information and how it should be handled. It can analyze and accurately classify files uploaded to cloud services, external websites, or shared through collaboration platforms and ensure that outgoing emails and data traffic, and public-facing documents and assets like code repositories do not contain sensitive information like PII, API keys, passwords and more. Accurate classification and detection are fundamental to adequate and consistent policy enforcement.

Consider combining both LLM and pattern-matching techniques to create modern DLP solutions with powerful policies. One example is a DLP rule that prevents resume documents with contact details (PII) from being uploaded to ChatGPT for analysis (the free version can use uploaded data for training). LLMs should be used for enhancing the current DLP solution. i.e., work alongside and in combination with current pattern-matching and EDM techniques.

LLMs are constantly learning and evolving and can be trained on vast amounts of data related to real-world cyberattacks and prevalent attacker techniques for masking and exfiltrating sensitive data. DLP integrated with advanced LLMs with up-to-date training data sets can stay ahead of the curve and adapt to emerging tactics employed by malicious actors. Given their efficacy, organizations looking for a DLP solution, whether standalone or as part of a comprehensive SASE or SSE offering, should strongly consider future-proofing their DLP implementations with LLMs.