How do you solve data security challenges in LLMs?

Solving LLM security requires layered, external governance at the data infrastructure level. Selective encryption allows for fine-grained access control, ensuring sensitive data is protected during both the training and operational phases of the AI lifecycle.

What is the role of AI data governance in LLM Ops?

AI data governance ensures regulatory compliance (GDPR/HIPAA) by implementing strategies like pseudonymization and object-level encryption. This prevents sensitive information from becoming ingrained in a model's learned patterns.

What is definition leakage in LLMs?

Definition leakage occurs when a Large Language Model inadvertently memorizes sensitive training data and later reveals it in response to user prompts. This risk arises because models are often trained on large, unstructured datasets where proprietary info is not properly isolated.

How does selective encryption protect AI data?

Selective encryption provides object-level protection, allowing organizations to encrypt specific sensitive fields within a document while leaving the rest of the content readable. This ensures only authorized users can access sensitive data, even if the file is shared or used for AI training.

Why is external data governance necessary for AI?

Most LLMs lack native, fine-grained access controls. External governance at the data infrastructure level is required to ensure that sensitive information is properly classified and secured before it is ingested by the model, preventing unauthorized disclosure.

Solving the AI Data Security Challenges of Today's LLMs

Julie Taylor
Feb 27, 2024
3 min read

Updated: Jan 16

AI only succeeds where data security is effective. While Large Language Models (LLMs) offer revolutionary potential for enterprise automation, they introduce significant risks such as data breaches and regulatory non-compliance. To realize the full value of AI, organizations must implement robust AI Data Governance and security frameworks.

The Problem: Security Gaps in the Model Layer

Current LLMs do not provide the fine-grained access controls or governance frameworks needed to ensure that users see only information they are authorized to access. Because these models are trained on colossal, unstructured datasets, sensitive info can become "entangled" in the model's learned patterns.

The Threat of "Definition Leakage"

This entanglement creates the risk of inadvertent disclosure, where the model outputs proprietary secrets or PII in response to user prompts. Achieving real security requires layered, external governance at the data infrastructure level—not inside the model itself.

The Solution: Selective Encryption and Pseudonymization

To bridge the gap between AI utility and data privacy, we advocate for selective encryption applied early in the document lifecycle.

Advanced Pseudonymization: By replacing private identifiers with non-sensitive tokens, you facilitate privacy-preserving training while maintaining data integrity.
Object-Level Protection: Unlike traditional redaction, our method embeds protection policies directly within the document. This ensures that only authorized individuals can access specific content, regardless of where the file travels.
Single Source of Truth: Selective encryption allows users with different clearance levels to collaborate on the same document, seeing only the parts they are authorized to see.

Preparing for the Future of AI Security

As AI adoption scales, your security posture must be future-proof. By aligning with NIST standards and preparing for Post-Quantum Cryptography (PQC), Confidencial ensures your unstructured data remains secure against evolving threats.

Conclusion: Unlock AI Value Safely

Don't let data security challenges stall your AI initiatives. By focusing on AI Data Governance and granular encryption, you can lay the foundation for trustworthy, sustainable enterprise AI.

Frequently Asked Questions: AI Data Security & LLMs

Q: What is "definition leakage" in the context of Large Language Models?

A: Definition leakage, or training data leakage, occurs when a Large Language Model (LLM) inadvertently "memorizes" sensitive information from its training dataset and later reproduces it in response to user prompts. Because models learn from colossal, unstructured datasets, proprietary secrets or personally identifiable information (PII) can become entangled in the model's learned patterns, leading to unintentional disclosure.

Q: Why are traditional access controls insufficient for LLM security?

A: Traditional access controls typically secure the "perimeter" or specific folders, but LLMs often lack native mechanisms to understand internal security boundaries within the data they ingest. Once sensitive data is processed by the model, the model cannot distinguish between a user authorized to see that specific data point and one who is not, making layered, external governance at the data infrastructure level essential.

Q: How does selective encryption differ from standard data encryption?

A: While standard encryption makes an entire file unreadable without a key, selective encryption (or object-level protection) allows for the granular protection of specific fields or paragraphs within a document. This enables organizations to maintain a "Single Source of Truth," where different users—or AI models—can access the non-sensitive portions of a document for utility while sensitive elements remain cryptographically locked.

Q: What is the role of pseudonymization in AI training?

A: Pseudonymization is a privacy-enhancing technique that replaces direct identifiers (like names or Social Security numbers) with artificial identifiers or "tokens". This process is reversible with the correct "key," allowing organizations to facilitate privacy-preserving AI training and analysis without losing the data integrity required for high-quality model outputs.

Q: How does AI Data Governance help with regulatory compliance like GDPR or HIPAA?

A: AI Data Governance provides a framework for managing the entire data lifecycle—from collection to model retirement—ensuring that sensitive data is masked, encrypted, or removed before it reaches the AI. By implementing continuous monitoring and role-based access controls, organizations can demonstrate the auditability and transparency required to meet strict global privacy standards.