{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_enkrypt-ai","slug":"enkrypt-ai","name":"Enkrypt AI","type":"product","url":"https://www.enkryptai.com","page_url":"https://unfragile.ai/enkrypt-ai","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_enkrypt-ai__cap_0","uri":"capability://safety.moderation.real.time.compliance.risk.detection.and.scoring","name":"real-time compliance risk detection and scoring","description":"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.","intents":["Prevent accidental disclosure of PHI or PII in AI-generated responses before they reach employees","Automatically flag prompts that violate data residency or processing restrictions","Generate audit trails showing which compliance rules were triggered and when","Block or redact high-risk outputs without halting the entire AI interaction"],"best_for":["Healthcare organizations subject to HIPAA compliance requirements","Financial services firms managing PCI-DSS and regulatory reporting obligations","Legal departments handling privileged communications and client confidentiality"],"limitations":["Real-time scoring adds latency to response generation (specific overhead unknown from public docs)","Rule configuration requires domain expertise in compliance frameworks; misconfigured rules may create false positives or false negatives","Semantic analysis may struggle with context-dependent compliance violations (e.g., de-identified data that can be re-identified through inference)","No visibility into whether risk scoring uses deterministic rules, ML classifiers, or hybrid approaches"],"requires":["Access to compliance framework definitions (HIPAA, SOC 2, GDPR, etc.)","Integration with identity and access management system to map user roles to compliance contexts","Audit logging infrastructure to capture risk scores and policy violations"],"input_types":["text prompts","structured data (user metadata, data classification tags)","AI model outputs (text, code)"],"output_types":["risk scores (numeric or categorical)","compliance violation flags","redacted/filtered outputs","audit log entries"],"categories":["safety-moderation","compliance-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_1","uri":"capability://automation.workflow.data.residency.and.processing.location.enforcement","name":"data residency and processing location enforcement","description":"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.","intents":["Ensure customer data processed by AI never leaves the EU to comply with GDPR data residency requirements","Route sensitive financial data to on-premises or private cloud AI models rather than public cloud endpoints","Prevent training data from being used to improve models serving other customers or regions","Demonstrate to auditors that data processing locations match contractual and regulatory commitments"],"best_for":["Multinational enterprises with operations across GDPR, CCPA, and other jurisdictions with data localization mandates","Financial institutions managing cross-border customer data under regulatory restrictions","Healthcare providers operating in multiple countries with conflicting data residency rules"],"limitations":["Requires deployment of model inference infrastructure in multiple geographic regions, increasing operational complexity and cost","Routing logic must account for data classification metadata; misconfigured metadata can cause data to be processed in wrong regions","No mechanism described for handling data that must be processed in one region but returned to users in another (e.g., EU user querying US-resident data)","Unclear whether enforcement applies to intermediate processing (embeddings, tokenization) or only final inference"],"requires":["Multi-region deployment infrastructure (on-premises, private cloud, or regional cloud endpoints)","Data classification and tagging system to identify which data requires which geographic constraints","Network policies and firewall rules to enforce data routing decisions","Integration with identity provider to map users to allowed processing regions"],"input_types":["text prompts with geographic metadata","data classification tags","user location and jurisdiction context"],"output_types":["routing decisions (which region to process in)","enforcement logs showing where data was processed","compliance attestations for audit purposes"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_10","uri":"capability://tool.use.integration.multi.model.orchestration.with.compliance.aware.routing","name":"multi-model orchestration with compliance-aware routing","description":"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.","intents":["Route sensitive healthcare data to a HIPAA-approved model rather than a general-purpose model","Use a locally-deployed model for data that cannot leave the organization, and cloud models for less sensitive data","Automatically select models based on regulatory requirements (e.g., EU-only models for GDPR compliance)","Manage model selection across multiple providers (OpenAI, Anthropic, local models) with compliance constraints"],"best_for":["Enterprises using multiple AI model providers and needing to coordinate model selection","Organizations with heterogeneous data sensitivity levels requiring different models for different data types","Regulated industries requiring model selection based on compliance and regulatory approval status"],"limitations":["Model orchestration adds complexity to the system; routing decisions must be made in real-time with minimal latency","Compliance-aware routing requires accurate classification of data sensitivity and regulatory requirements; misconfiguration can route data to non-compliant models","No visibility into how model selection decisions are made or what factors are considered","Managing multiple models increases operational overhead and requires monitoring and updating multiple model versions"],"requires":["Integration with multiple AI model providers or local model deployment infrastructure","Model registry with metadata about each model (training data, regulatory approval, data residency, etc.)","Data classification system to identify data sensitivity and regulatory requirements","Model selection logic or policy engine to route requests to appropriate models"],"input_types":["user request with data and context","data classification and sensitivity metadata","compliance requirements and regulatory context"],"output_types":["routing decision (which model to use)","AI model response","routing audit trail"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_11","uri":"capability://data.processing.analysis.data.lineage.tracking.and.provenance.management","name":"data lineage tracking and provenance management","description":"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.","intents":["Trace the origin of data used in an AI decision to ensure it came from authorized sources","Identify all downstream systems that received data from an AI model output","Demonstrate data lineage to auditors to prove compliance with data governance policies","Investigate data quality issues by tracing transformations applied to data throughout the pipeline"],"best_for":["Regulated enterprises with strict data governance requirements","Organizations using AI for high-stakes decisions (credit approval, medical diagnosis) requiring audit trails","Data-driven organizations with complex data pipelines requiring visibility into data transformations"],"limitations":["Data lineage tracking adds overhead to the system; tracking every data transformation can impact performance","Lineage graphs can become very large and complex in organizations with many data sources and transformations","No visibility into how lineage is stored and queried; complex lineage queries may be slow","Lineage tracking requires integration with all data sources and transformations; incomplete integration creates gaps in lineage"],"requires":["Data lineage tracking infrastructure (graph database, metadata store, or specialized lineage platform)","Integration with all data sources and transformation systems","Metadata capture for all data transformations (what was transformed, how, when, by whom)","Lineage query and visualization tools for auditors and data governance teams"],"input_types":["data source metadata","transformation metadata (what was transformed, how, when)","AI model inputs and outputs"],"output_types":["data lineage graphs","provenance records","lineage audit trails"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_2","uri":"capability://safety.moderation.audit.trail.generation.and.forensic.logging","name":"audit trail generation and forensic logging","description":"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.","intents":["Generate audit reports showing which users accessed which AI capabilities and what data they processed","Investigate security incidents by replaying the exact sequence of prompts and responses that led to a data exposure","Demonstrate to regulators that all AI interactions were logged and monitored in accordance with compliance requirements","Identify patterns of misuse or policy violations across the organization"],"best_for":["Regulated industries (healthcare, finance, legal) subject to audit requirements and regulatory inspections","Organizations with mature security operations centers (SOCs) that can consume and analyze high-volume audit logs","Enterprises implementing zero-trust security models requiring comprehensive activity logging"],"limitations":["High-volume audit logging can generate massive storage costs and query latency for large organizations","Audit logs may contain sensitive information (prompts, responses, user data) requiring their own encryption and access controls","No visibility into retention policies or how long audit logs are kept before deletion","Unclear whether audit logs are queryable in real-time or only available for batch analysis"],"requires":["Secure, append-only storage backend (e.g., cloud object storage with versioning disabled, or on-premises WORM storage)","Encryption keys for audit log encryption and integrity verification","Integration with SIEM or log aggregation platform for analysis and alerting","Retention policies and archival strategy to manage storage costs"],"input_types":["all AI interaction metadata (prompts, responses, user identity, timestamps)","risk scores and policy violation flags","data classification tags"],"output_types":["structured audit log entries (JSON, CSV, or proprietary format)","audit reports and compliance attestations","forensic analysis outputs (e.g., timeline of events during an incident)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_3","uri":"capability://safety.moderation.sensitive.data.masking.and.redaction.in.real.time","name":"sensitive data masking and redaction in real-time","description":"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.","intents":["Prevent accidental exposure of patient names, medical record numbers, or diagnoses in AI-generated clinical summaries","Remove credit card numbers and account identifiers from customer service interactions before logging","Redact API keys and credentials from code snippets before they are stored in audit logs","Enable AI models to process sensitive data without training on or memorizing actual PII"],"best_for":["Healthcare organizations processing patient data and clinical notes","Financial services firms handling customer account information and transaction data","Customer service teams using AI to summarize interactions containing personal information"],"limitations":["Pattern-based redaction may miss context-dependent sensitive data (e.g., dates that become identifying when combined with other information)","Redaction can reduce the utility of AI responses if sensitive context is necessary for accurate analysis","No visibility into whether redaction uses rule-based patterns, ML models, or hybrid approaches","Redaction rules must be configured per organization; generic rules may not catch domain-specific sensitive data"],"requires":["Configurable redaction rule set (regex patterns, entity types, custom classifiers)","NER model or PII detection service (may be built-in or integrated from third-party)","Token mapping system to track which redacted values correspond to original data (if re-identification is needed)","Integration with data classification system to identify which fields require redaction"],"input_types":["text prompts containing PII/PHI","AI model outputs","structured data (CSV, JSON with sensitive fields)"],"output_types":["redacted text with sensitive values replaced by tokens or placeholders","redaction metadata (which patterns were matched and redacted)","token mapping (for re-identification if needed)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_4","uri":"capability://safety.moderation.role.based.access.control.rbac.with.compliance.aware.policies","name":"role-based access control (rbac) with compliance-aware policies","description":"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').","intents":["Prevent non-clinical staff from accessing AI models trained on patient data","Restrict financial AI tools to authorized traders and analysts, blocking access from other departments","Enforce segregation of duties by preventing a single user from accessing both data and AI models that could be used to exfiltrate that data","Audit which users accessed which AI capabilities and when"],"best_for":["Large enterprises with complex organizational structures and multiple departments","Regulated industries requiring segregation of duties and access controls","Organizations with federated identity management across multiple systems"],"limitations":["RBAC alone does not prevent authorized users from misusing access; requires additional monitoring and behavioral analysis","Policy configuration can become complex in large organizations with many roles and compliance contexts","No visibility into whether access control is evaluated at the request level or session level","Unclear how access policies interact with data residency and compliance risk detection"],"requires":["Integration with identity provider (LDAP, Active Directory, SAML, OAuth, OIDC)","Role definitions and access policy configuration","Audit logging to track access decisions and policy violations","Regular access reviews and recertification processes"],"input_types":["user identity and attributes","requested AI capability or data","compliance context (data classification, jurisdiction, etc.)"],"output_types":["access decision (allow/deny)","audit log entry","access denial reason (for user feedback)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_5","uri":"capability://automation.workflow.model.governance.and.version.control.for.compliance","name":"model governance and version control for compliance","description":"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).","intents":["Prove to regulators that AI models used for high-stakes decisions (credit approval, medical diagnosis) were validated and tested for bias","Rollback to a previous model version if a new version exhibits unexpected behavior or violates compliance requirements","Track which model version was used for each AI interaction, enabling forensic analysis if a decision is later challenged","Maintain audit trail of model training data, hyperparameters, and performance metrics for regulatory inspection"],"best_for":["Financial services firms using AI for credit decisions, fraud detection, or trading","Healthcare organizations using AI for diagnosis, treatment recommendations, or patient risk scoring","Legal firms using AI for contract analysis or due diligence"],"limitations":["Model governance adds operational overhead; requires processes for model validation, testing, and approval before deployment","No visibility into whether model performance metrics are automatically computed or require manual validation","Unclear how model governance integrates with continuous model improvement and retraining","Regulatory requirements for model governance vary by jurisdiction and use case; generic governance framework may not meet all requirements"],"requires":["Model registry or artifact repository to store model versions and metadata","Model validation and testing framework to assess model performance and bias","Integration with model training and deployment pipelines","Audit logging to track which model versions were used for which interactions"],"input_types":["model artifacts (weights, architecture, hyperparameters)","training data metadata and provenance","model performance metrics (accuracy, fairness, bias)"],"output_types":["model version records with metadata","model governance audit trails","compliance attestations for model performance"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_6","uri":"capability://tool.use.integration.integration.with.enterprise.identity.and.access.management.systems","name":"integration with enterprise identity and access management systems","description":"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.","intents":["Enable employees to access Enkrypt AI using their existing corporate credentials without creating new accounts","Automatically revoke AI access when an employee is terminated or changes departments","Retrieve user attributes (department, role, security clearance) from the identity system to enforce access policies","Integrate AI access logs with centralized identity and access management auditing"],"best_for":["Large enterprises with mature identity and access management infrastructure","Organizations with federated identity management across multiple systems and domains","Enterprises requiring single sign-on (SSO) and centralized access control"],"limitations":["Requires integration with specific identity providers; not all identity systems may be supported","Identity attribute retrieval adds latency to authentication and access control decisions","No visibility into whether identity attributes are cached or retrieved in real-time","Misconfigured identity federation can create security vulnerabilities (e.g., privilege escalation through attribute injection)"],"requires":["Supported identity provider (Active Directory, LDAP, Okta, Azure AD, SAML, OAuth)","Network connectivity to identity provider","Configuration of identity federation protocols (SAML, OAuth, OIDC)","Mapping of identity attributes to Enkrypt AI roles and access policies"],"input_types":["user credentials or identity tokens","identity provider configuration"],"output_types":["authenticated user identity","user attributes (roles, departments, clearances)","access control decisions based on identity attributes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_7","uri":"capability://safety.moderation.encrypted.data.processing.and.end.to.end.encryption","name":"encrypted data processing and end-to-end encryption","description":"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.","intents":["Process sensitive customer data through AI models without exposing plaintext data to the platform or its operators","Comply with regulations requiring data encryption at rest and in transit (e.g., HIPAA, PCI-DSS)","Prevent insider threats where platform operators or cloud providers could access plaintext data","Enable customers to audit and verify that their data was never exposed in plaintext"],"best_for":["Healthcare organizations processing patient data with strict confidentiality requirements","Financial services firms handling sensitive customer financial information","Organizations with zero-trust security models requiring encryption of all data"],"limitations":["Encrypted data processing significantly reduces model performance and increases latency (homomorphic encryption can add 100x+ overhead)","Encrypted processing limits the types of AI operations that can be performed (e.g., complex reasoning may not be feasible with encrypted data)","Key management becomes critical; loss of encryption keys results in permanent data loss","No visibility into which encryption techniques are used (homomorphic encryption, secure MPC, encrypted embeddings, etc.) or their performance impact"],"requires":["Encryption key management system (on-premises or cloud-based)","Client-side encryption libraries or SDKs","Support for specific encryption techniques (homomorphic encryption, secure MPC, etc.)","Secure key distribution and rotation mechanisms"],"input_types":["plaintext data on client side","encryption keys"],"output_types":["encrypted data in transit and at rest","encrypted AI model outputs","decrypted results (only on authorized client side)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_8","uri":"capability://automation.workflow.automated.compliance.reporting.and.attestation.generation","name":"automated compliance reporting and attestation generation","description":"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.","intents":["Generate SOC 2 Type II attestations showing that controls are operating effectively","Produce HIPAA compliance reports demonstrating that patient data is protected and access is logged","Create GDPR data processing agreements and demonstrate compliance with data subject rights","Provide auditors with automated evidence of policy enforcement and risk management"],"best_for":["Regulated enterprises undergoing regular compliance audits and certifications","Organizations with multiple compliance requirements (HIPAA, SOC 2, GDPR, PCI-DSS) requiring coordinated reporting","Enterprises with large audit teams that need to process high-volume compliance evidence"],"limitations":["Automated reports may not capture all nuances required by auditors; manual review and supplementation may still be necessary","Report templates must be customized per organization and jurisdiction; generic templates may not meet specific requirements","No visibility into whether reports are generated on-demand or require manual triggering","Compliance requirements change frequently; report templates must be updated to reflect new regulatory requirements"],"requires":["Audit logging infrastructure to capture compliance-relevant events","Report templates for specific compliance frameworks (SOC 2, HIPAA, GDPR, etc.)","Configuration of compliance requirements and control objectives","Integration with audit log storage and analysis systems"],"input_types":["audit logs and compliance events","risk scores and policy enforcement records","compliance framework definitions"],"output_types":["compliance reports (PDF, HTML, or structured formats)","attestations and certifications","evidence packages for auditors"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_enkrypt-ai__cap_9","uri":"capability://safety.moderation.behavioral.anomaly.detection.and.insider.threat.monitoring","name":"behavioral anomaly detection and insider threat monitoring","description":"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.","intents":["Detect when a user account is compromised and being used to access sensitive data or AI models","Identify employees attempting to exfiltrate data through AI systems (e.g., requesting large volumes of customer data)","Flag unusual access patterns (accessing data outside normal business hours, accessing data from unusual locations)","Prevent insider threats by detecting and alerting on suspicious behavior before data is exposed"],"best_for":["Large enterprises with mature security operations centers (SOCs) capable of responding to alerts","Organizations with high-value data or intellectual property requiring insider threat detection","Regulated industries (finance, healthcare, defense) with strict insider threat requirements"],"limitations":["Behavioral anomaly detection requires baseline data; new users or users with changing roles may generate false positives","False positive rate can be high, leading to alert fatigue and reduced effectiveness","No visibility into which anomaly detection techniques are used (statistical baselines, ML models, rule-based heuristics)","Behavioral detection may not catch sophisticated insider threats that mimic normal behavior patterns"],"requires":["Baseline user behavior data (access patterns, data requests, interaction frequency)","Anomaly detection model or rule set","Integration with SIEM or security alerting system","Incident response procedures for responding to anomaly alerts"],"input_types":["user interaction logs (access times, data requests, AI model usage)","user identity and attributes","baseline behavior profiles"],"output_types":["anomaly alerts","risk scores for user behavior","recommended actions (restrict access, investigate, etc.)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Access to compliance framework definitions (HIPAA, SOC 2, GDPR, etc.)","Integration with identity and access management system to map user roles to compliance contexts","Audit logging infrastructure to capture risk scores and policy violations","Multi-region deployment infrastructure (on-premises, private cloud, or regional cloud endpoints)","Data classification and tagging system to identify which data requires which geographic constraints","Network policies and firewall rules to enforce data routing decisions","Integration with identity provider to map users to allowed processing regions","Integration with multiple AI model providers or local model deployment infrastructure","Model registry with metadata about each model (training data, regulatory approval, data residency, etc.)","Data classification system to identify data sensitivity and regulatory requirements"],"failure_modes":["Real-time scoring adds latency to response generation (specific overhead unknown from public docs)","Rule configuration requires domain expertise in compliance frameworks; misconfigured rules may create false positives or false negatives","Semantic analysis may struggle with context-dependent compliance violations (e.g., de-identified data that can be re-identified through inference)","No visibility into whether risk scoring uses deterministic rules, ML classifiers, or hybrid approaches","Requires deployment of model inference infrastructure in multiple geographic regions, increasing operational complexity and cost","Routing logic must account for data classification metadata; misconfigured metadata can cause data to be processed in wrong regions","No mechanism described for handling data that must be processed in one region but returned to users in another (e.g., EU user querying US-resident data)","Unclear whether enforcement applies to intermediate processing (embeddings, tokenization) or only final inference","Model orchestration adds complexity to the system; routing decisions must be made in real-time with minimal latency","Compliance-aware routing requires accurate classification of data sensitivity and regulatory requirements; misconfiguration can route data to non-compliant models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.284Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=enkrypt-ai","compare_url":"https://unfragile.ai/compare?artifact=enkrypt-ai"}},"signature":"p1GkmiXdkSwtaL7W9cKlOYoD1LZBNb2Lb/TILt2Q1yhyLtP09JnHqEjq6Ftjg9aTRT15bikP6ESuEgSGH56ICw==","signedAt":"2026-06-21T07:51:11.744Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/enkrypt-ai","artifact":"https://unfragile.ai/enkrypt-ai","verify":"https://unfragile.ai/api/v1/verify?slug=enkrypt-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}