Enkrypt AI

Monitors AI model outputs and user interactions against configurable compliance rule sets (HIPAA, SOC 2, GDPR, etc.) in real-time, assigning risk scores to prompts and responses before they reach end users. Implements a policy-as-code engine that evaluates content against regulatory frameworks without requiring manual review workflows, using pattern matching and semantic analysis to flag potential violations before data exposure occurs.

Enforces geographic and jurisdictional constraints on where AI model inference, training data, and intermediate processing occurs, preventing data from crossing regulatory boundaries. Uses request routing logic and data classification metadata to ensure prompts and responses stay within specified regions (EU, US, Asia-Pacific, etc.) and comply with data localization requirements like GDPR Article 44 and China's data sovereignty laws.

Manages multiple AI models (from different providers or internal models) and routes requests to the appropriate model based on compliance requirements, data sensitivity, and performance characteristics. Implements a model selection engine that considers factors like model training data provenance, regulatory approval status, and data residency requirements to choose the best model for each request while maintaining compliance.

Tracks the origin, transformations, and usage of data throughout the AI pipeline, maintaining a complete lineage record showing where data came from, how it was processed, and where it was used. Implements provenance tracking that enables organizations to answer questions like 'which source data was used to generate this AI output?' and 'which downstream systems consumed this data?', supporting compliance audits and data governance.

Captures comprehensive logs of all AI interactions including prompts, responses, risk scores, policy violations, user identity, timestamps, and data classification, storing them in immutable audit logs designed for regulatory inspection and forensic analysis. Implements structured logging with tamper-evident mechanisms (e.g., cryptographic hashing or append-only storage) to ensure audit records cannot be retroactively modified, enabling organizations to prove compliance during audits or incident investigations.

Automatically detects and masks or redacts sensitive data patterns (PII, PHI, credentials, financial account numbers, etc.) in both user prompts and AI-generated responses before they are processed or returned. Uses pattern matching, NER (named entity recognition), and configurable redaction rules to replace sensitive values with tokens or placeholders, allowing AI models to operate on de-identified data while preserving utility for downstream analysis.

Enforces fine-grained access control over AI capabilities and data based on user roles, departments, and compliance contexts, preventing unauthorized users from accessing sensitive AI features or data. Integrates with identity providers (LDAP, Active Directory, SAML, OAuth) to map user identities to roles, then evaluates access policies that may include compliance-specific constraints (e.g., 'only finance department can use AI on financial data', 'only doctors can access clinical AI models').

Tracks and manages AI model versions, training data provenance, and model performance metrics to ensure compliance with regulatory requirements for model governance. Maintains immutable records of which model versions were used for which interactions, enabling organizations to audit model behavior and demonstrate that models meet regulatory standards (e.g., fairness, accuracy, bias detection).

Connects to existing enterprise identity providers (Active Directory, LDAP, Okta, Azure AD, SAML, OAuth) to authenticate users and retrieve identity attributes (roles, departments, security clearances) without requiring separate credential management. Uses standard identity federation protocols to enable single sign-on (SSO) and ensure that access control decisions are based on authoritative identity data from the organization's identity system.

Processes data in encrypted form throughout the AI inference pipeline, using techniques like homomorphic encryption, secure multi-party computation, or encrypted embeddings to prevent the platform from accessing plaintext data. Implements end-to-end encryption where data is encrypted on the client side, transmitted securely, processed in encrypted form, and decrypted only by authorized recipients, ensuring that Enkrypt AI infrastructure never has access to unencrypted sensitive data.

Generates automated compliance reports and regulatory attestations (e.g., SOC 2 Type II reports, HIPAA compliance attestations, GDPR data processing agreements) based on audit logs, risk scores, and policy enforcement records. Uses templates and configurable report generators to produce evidence that the platform meets specific regulatory requirements, reducing manual effort in compliance documentation and audit preparation.

Monitors user interactions with AI systems for anomalous behavior patterns (unusual access times, unusual data requests, bulk data downloads, policy violations) and flags potential insider threats or compromised accounts. Uses statistical baselines, machine learning models, or rule-based heuristics to detect deviations from normal user behavior and trigger alerts or access restrictions.