Over the past year enterprise AI vendors and customers have shifted focus from encrypting data at rest and in transit to protecting embeddings while they are being used. As vector search powers more revenue-critical workflows—searching contracts, medical records, and customer conversations—companies are deploying confidential computing, hardware security modules (HSMs) and encrypted runtimes to reduce the risk of embedding leakage and meet tightening compliance expectations.

What changed: embeddings-in-use are a new attack surface

Embedding vectors—dense numerical representations of text, images and other content—are the foundation of modern semantic search and retrieval-augmented generation (RAG). Unlike files or database rows, embeddings are transient: they are created, stored in vector databases, indexed, retrieved and fed to models during inference. Security researchers and incident responders have demonstrated that embeddings can leak sensitive information and enable reconstruction attacks when adversaries gain access to vector stores or inference pipelines.

Traditional protections (disk encryption, network TLS, RBAC) address many risks, but they don’t stop someone who can run or tamper with code that processes vectors in memory. That has driven enterprises to treat "embeddings-in-use" as a distinct privacy and compliance threat, requiring runtime isolation and cryptographic guarantees.

How vendors and clouds are responding

Three technical approaches have converged in vendor roadmaps:

  • Confidential computing enclaves — hardware-based isolated runtimes (Intel SGX, AMD SEV, AWS Nitro Enclaves, Azure Confidential VMs, Google Confidential VMs) that limit access to memory and CPU state even from cloud hypervisors or admins.
  • HSM-backed key management — FIPS 140-2/3-compatible HSMs and KMIP/PKCS#11 integrations that ensure keys used to encrypt embeddings are never exportable and can be rotated or revoked centrally.
  • Client- and application-side encryption — encrypting vectors before they enter the vector database and decrypting only inside a trusted runtime for inference.

Together, these measures let enterprises enforce a model where vectors are stored encrypted and only decrypted inside attested enclaves that have cryptographic proofs of their configuration. That closes a critical gap between static protections (at-rest, in-transit) and dynamic protections (in-use).

Real-world use cases and regulatory drivers

Enterprises in regulated sectors—finance, healthcare, legal—have clear incentives to adopt runtime protections. For example, hospitals using semantic search over patient notes or banks using vector search for AML analytics face high compliance risk if unauthorized parties access embeddings that encode personal data. The NIST AI Risk Management Framework and existing privacy laws such as GDPR and HIPAA encourage risk-based controls; confidential computing and auditable key controls provide a defensible technical posture under those frameworks.

Tradeoffs: performance, cost and developer friction

Adopting confidential computing and HSMs is not free. Enclave-enabled VMs and Nitro-like technologies incur compute premiums. HSM calls introduce latency and cost, and encrypting/decrypting embeddings adds CPU overhead and complexity to pipelines.

Operationally, enterprises must solve:

  • Key lifecycle and governance: who can create, rotate and revoke keys, and how to integrate with identity providers and audit logs.
  • Performance engineering: batching, caching and hybrid on-device inference strategies to offset enclave and HSM latency.
  • Testing and reproducibility: ensuring models and retrieval pipelines behave the same inside attested runtimes as they do in unprotected dev environments.

Where the market is heading

Expect three near-term patterns:

  1. Bundled offerings: Cloud providers and vector DB vendors will bundle enclaves and key management into managed offerings tailored for enterprise workloads, reducing the integration burden.
  2. APIs for attestation and auditability: Standardized attestation results, signed by cloud providers, will be surfaced to compliance tooling and SIEMs to provide continuous assurance that vectors were processed in the expected environment.
  3. Hybrid models: For latency-sensitive applications, vendors will offer split architectures that do sensitive operations in confidential runtimes while leaving lower-risk tasks out-of-band, reducing cost while preserving protections for critical data.

Practical guidance for AI teams

IT and AI teams preparing to adopt confidential computing for vector search should take these concrete steps:

  • Inventory embedding flows: Map where embeddings are created, stored, and consumed. Classify embeddings by sensitivity and regulatory relevance.
  • Define key governance: Establish HSM-backed key policies (rotation, custodianship, emergency revocation) and integrate with your identity and audit systems.
  • Prototype in a controlled environment: Run benchmarks using confidential VMs or enclaves to measure latency and cost; test fallback and circuit-breaker logic.
  • Require attestation and logs: Insist vendors provide verifiable enclave attestation, signed audit trails, and SLA language that reflects runtime protections.
  • Train developers: Update secure-coding practices to handle encrypted vectors, avoid leaking plaintext to logs, and instrument observability inside enclaves where permitted.

Bottom line

Protecting embeddings while they’re being processed is rapidly becoming a baseline expectation for enterprise AI, not an optional hardening step. Confidential computing, HSM-backed key management and encrypted runtimes address a unique and growing risk—embeddings-in-use—that standard perimeter controls do not cover. The transition will add cost and engineering complexity, but for organizations handling regulated or sensitive data, the tradeoff increasingly favors runtime protection; vendors and cloud providers that make that model simple and auditable will win enterprise adoption.