Enterprise Data Encryption Standards

By Justin Chen · July 14, 2025

As AI adoption expands across industries, particularly in healthcare, finance, government, and legal sectors, enterprises are demanding that AI solutions meet the same rigorous data protection standards as any other part of their IT stack. For AI software manufacturers—especially those working with large language models (LLMs) and enterprise data—data encryption is not optional; it is a baseline expectation. Enterprises in these sectors routinely handle sensitive, regulated, or proprietary information, and any software they adopt must enforce robust encryption across all stages of data handling: at rest, in transit, and ideally, in use.

At Rest

The first and most widely implemented standard is encryption of data at rest. This includes any data that is persistently stored—such as fine-tuning datasets, user inputs and outputs, vector embeddings, chat histories, model checkpoints, and logs. Enterprises expect this data to be encrypted using industry-standard algorithms such as AES-256. As an AI software vendor, this means ensuring that all local and cloud-based storage systems are encrypted, including file systems, cloud object storage (like AWS S3), databases, and any intermediate caches. Data should never be written to disk in plaintext, and decryption should only occur temporarily in secure memory when processing is required. This protects against data theft or exposure in the event of disk compromise or unauthorized access to storage infrastructure.

In Transit

In parallel, data in transit—which refers to data moving between components of your system—must also be encrypted. Whether the data is traveling between frontend interfaces and backend inference servers, from the model to a database, or across microservices in a distributed architecture, it must be protected using TLS (Transport Layer Security), preferably version 1.2 or higher. For internal service-to-service communication, enterprises may also expect support for mutual TLS (mTLS) to authenticate both ends of a connection. In practice, this means all APIs should operate over HTTPS, and internal communications between your services should never occur in plaintext, even inside trusted networks.

In Use

A growing concern among data-sensitive organizations is data in use—the period when sensitive data is actively being processed by the model during inference or training. Although not yet a universal standard, some enterprises, particularly in defense, life sciences, and highly classified industries, are beginning to require technologies like Trusted Execution Environments (TEEs) or confidential computing to isolate memory during runtime. Solutions such as Intel SGX or Azure Confidential VMs are designed to protect data in volatile memory from unauthorized access, including from the host operating system. While these methods can introduce performance trade-offs, supporting or integrating with such environments is becoming a differentiator for AI vendors working with mission-critical or regulated data.

Equally important to how data is encrypted is how the encryption keys are managed. Enterprises often operate their own Key Management Systems (KMS) and expect software providers to integrate with them. A best practice is to support “Bring Your Own Key” (BYOK) or “Hold Your Own Key” (HYOK) models, which give enterprises full ownership and control over encryption keys—often a compliance or internal policy requirement. The vendor's responsibility here is to ensure keys are securely stored, rotated regularly, and never exposed in logs or application memory. If you’re managing keys yourself, using a hardened KMS with strict role-based access controls and audit logging is essential.

Certification and Auditability

In addition to these technical requirements, encryption must align with industry-specific compliance frameworks. For healthcare clients, encryption standards must meet HIPAA regulations, ensuring the confidentiality and integrity of protected health information. For financial institutions, PCI-DSS mandates encrypted handling of payment and cardholder data. Government agencies may require compliance with FedRAMP or ITAR, including use of FIPS-validated cryptographic modules. Many enterprises also demand that vendors demonstrate encryption practices through a SOC 2 Type II audit or equivalent certification. As such, vendors must not only implement encryption but also document and prove it during procurement and security reviews.

Finally, auditability is a critical part of any encryption strategy. Enterprises expect visibility into how data is encrypted, who accessed it, when, and under what circumstances. Your software should produce comprehensive logs for all data access and key usage, and optionally integrate with enterprise SIEM tools (like Splunk, ELK, or Datadog) for monitoring. Being able to demonstrate encryption enforcement through logs and reports is vital during security audits and incident response.

In summary, data encryption is a foundational requirement for any AI software manufacturer working with regulated enterprises. You must encrypt data at rest, secure it during transit, explore options for runtime protection, and support enterprise-grade key management. These capabilities must also be aligned with industry compliance standards and documented in a way that can withstand enterprise security reviews. Only by meeting these encryption expectations can AI vendors position themselves as viable, trustworthy partners for data-sensitive industries looking to adopt LLM-powered solutions.