Skyflow Data Privacy Vault

Enforces that raw sensitive values are stored and processed only in designated geographic regions while tokens move freely across borders, enabling GDPR and data localization compliance.

Tokenizes individual fields within structured records (SSN, PAN, date of birth), rather than entire documents, enabling fine-grained data minimization while preserving record structure for downstream processing.

Generates tokens that structurally match the original data (same length, character type, or pattern), allowing tokenized values to pass downstream format validation without exposing real data.

Replaces cardholder data (PANs), with tokens before they reach systems in scope, reducing or eliminating PCI DSS CDE scope for storage, processing, and transmission.

Issues different tokens for the same underlying value depending on the requestor or context, preventing adversaries from correlating tokenized values across systems even with access to multiple stores.

Exposes tokenize, detokenize, and token-operable compute functions through a developer API, allowing applications to work with sensitive fields without ever receiving or storing raw values.

Stores original sensitive values in a secure vault with token-to-value mappings, providing a persistent token store with access controls, audit logging, and policy-governed detokenization events.

Maps data lineage and provenance across AI training and inference pipelines, tracing how PII, PHI, and IP move into models and external services.

Assesses the identities and service accounts that AI models, pipelines, and agents use, flagging over-permissioned non-human identities and access paths that violate least privilege. Reports identity risk as a posture finding, distinct from enforcing access policies at the model API at runtime.

Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.

Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.