Solving the AI Data Security Challenges of Today's LLMs

AI only succeeds where data security is effective.

Organizations can only realize the full value of AI once robust data security and comprehensive data governance are in place. AI systems frequently handle sensitive information, and risks such as data breaches, adversarial attacks, and regulatory non-compliance remain elevated when these areas are neglected. Ensuring proper safeguards like access controls, data classification, and continuous monitoring is essential for mitigating risks and establishing trust in AI-driven decisions.

However, these data security and governance challenges are unlikely to be resolved within the large language model layer alone. Current large language models do not provide the fine-grained access controls or governance frameworks needed to guarantee that users only see information they are authorized to access. Achieving real AI value requires layered, external governance and policy enforcement at the data infrastructure level and throughout the AI lifecycle, rather than inside the model itself.

An nearly eternal data protection challenge, now newly urgent

Large Language Models (LLMs) have the potential to revolutionize enterprise operations by automating complex tasks, enhancing decision-making, and providing unprecedented scalability in customer interaction and content generation. For this reason, enterprises are eager to adopt these technologies to gain competitive advantages, improve efficiency, and drive innovation in an increasingly digital marketplace.

However, leveraging the capabilities of these new and sophisticated AI systems, with their ability to produce and comprehend human-like text, is not without challenges.

Data privacy and confidentiality for both LLM inputs and outputs

The issue of data privacy and confidentiality takes on a new dimension of complexity and urgency when it comes to LLMs. These advanced AI systems are trained on colossal datasets, often encompassing an eclectic mix of content from a myriad of sources. Among these are sensitive or personal information that, if not properly managed, could become entangled in the model's learned patterns. This raises the specter of LLMs inadvertently generating outputs that mirror or disclose confidential information, thereby igniting significant privacy concerns.

This issue is not merely theoretical but poses real challenges with serious consequences for enterprises. For example, if an enterprise uses an LLM to generate market analysis reports and the model is trained on datasets with confidential financial information, there's a real risk that such sensitive data could inadvertently appear in LLM outputs generated in response to use prompts, causing unintended leaks of proprietary information. 

AI data governance is fundamental to everything

Moreover, the processing of personal data falls under strict regulatory frameworks like GDPR in Europe or HIPAA in the United States, imposing severe limitations on personal data handling. Therefore, enterprises training LLMs with personal data could be breaching these regulations, risking substantial reputational damage and fines.

Guaranteeing compliant data processing and ensuring that such private and sensitive data neither contributes to nor becomes ingrained in the learned patterns of these models adds a significant layer of complexity.

Sensitive information risk mitigation strategies and solutions

Implementing stringent data governance policies is crucial; this includes strategies of de-identification. Implementing robust data governance policies is essential when working with LLMs. The primary challenge lies in optimizing data utility—developing the highest quality LLM model—while ensuring regulatory compliance and safeguarding sensitive and private information. Achieving this involves a careful balance between data security and its utility to facilitate the compliant and privacy-preserving training of LLMs, including pseudonymization.

Pseudonymization is a process that involves altering personal data so that it cannot be linked back to a specific individual without additional information. Typically, this process, often referred to as tokenization, is achieved by replacing private identifiers with fictitious labels or pseudonyms throughout a document or an entire dataset. The supplementary information required for re-identification is stored separately, protected by technical and organizational measures to prevent attribution.

That is where we come in. 

The Confidencial Way: smarter AI data security through selective encryption

To ensure the development of compliant and privacy-conscious LLM operations (LLM Ops), we advocate for implementing strong pseudonymization and anonymization techniques as early as possible in the unstructured content and document lifecycle. These methods enhance data protection through the automated tokenization of data (the process of converting sensitive data into non-sensitive equivalents, called tokens, that retain essential information without compromising security) and meticulous fine-grained and selective application of encryption.

Strong pseudonymization, by allowing controlled re-identification, preserves the integrity of the data and facilitates the lawful and privacy-conscious training of LLMs. Conversely, anonymization becomes often indispensable for secure data archiving, and it may represent the sole method for the compliant-handling of personal data in some contexts. When executed correctly, this strategy is robust, aligns with the rigorous standards set by NIST, and supports straightforward auditing and automation processes.

Our unstructured content and document protection methods, through combining strong pseudonymization and fine-grained ‘selective encryption’, stand out by embedding all necessary information to facilitate decryption (when authorized) directly within the document itself, rather than depending on external databases for re-identification data. This strategy is more straightforward to scale and manage, preserves document formats, and ensures that documents are protected regardless of where they travel or reside, and guarantees that only authorized individuals can access the content.

Additionally, the advent of fine-grained and selective encryption technology allows for precise control over encryption levels, protecting specific words or paragraphs within documents and other forms of unstructured data. This targeted approach to encryption—applying it judiciously to protect sensitive or proprietary information within documents—ensures that such data remains secure and unutilized in LLM Ops, whether during their training or operational processes, whether these activities occur on-premises or in cloud environments.

Selective, effective, and really really smart: object-level encryption at work

This approach offers numerous benefits. First, it ensures the utility of data by making non-sensitive portions readily available for both LLM training and employee access. In addition, it enforces fine-grained access control to document contents, allowing users to access the parts of documents according to their access rights, as defined by the enterprise's security officer.

This also enables users with different clearance levels to work and collaborate on the same document. Furthermore, by embedding re-identification data and the document's protection policies within the documents themselves, we eliminate the need to store re-identification data in a separate datastore, thereby simplifying security management and enforcement requirements.

The Confidencial platform delivers end-to-end AI data security for teams and using LLMs:

Efficient Safeguarding

Confidencial introduces automated, precision encryption, safeguarding data integrity during LLM training and usage.

Guaranteed Security

Confidencial offers proven security strength, supported by detailed compliance records, ensuring safe LLM training processes.

Cost-Effective Solutions

Confidencial reduces costs and eases the burdens typically linked to data preparation and cleansing.

Seamless Compatibility

Designed to blend with various LLM training environments and tools, Confidencial bolsters existing security measures.

Embedded Data Protection

From the outset, Confidencial enhances the security of documents and unstructured data, embedding protection at the foundation.

Prepared for the Future

With an eye on upcoming NIST Post-Quantum Cryptography (PQC) standards, Confidencial prepares you for long-term data security advancements.

Benefits for AI productivity and data protection

This approach delivers several advantages. It maximizes data utility by enabling non-sensitive sections to be readily available for both large language model training and employee access, while enforcing detailed, enterprise-defined controls over sensitive content. By allowing users to see only what they’re authorized to, organizations can maintain both data integrity and security across internal teams.

Additionally, embedding re-identification information and security policies directly within documents removes the need for a separate re-identification datastore, streamlining compliance and simplifying security management. This enables seamless collaboration between users with different clearance levels on the same document, reduces overhead, and enhances auditability.

Ultimately, this method directly answers the core challenges of AI data security and governance. It protects sensitive information while unlocking greater AI value, thereby laying the foundation for trustworthy, flexible, and sustainable enterprise AI adoption.

Prepared for the Future

With an eye on upcoming NIST Post-Quantum Cryptography (PQC) standards, Confidencial prepares you for long-term data security advancements.

Download the full whitepaper ‘Navigating the AI Gold Rush: Protecting sensitive data used for and by LLMs’ to learn more about how to harness the full potential of LLMs safely.