Honey Jar Data Connectivity Framework

Executive Summary

The STING platform introduces Honey Jars as intelligent data containers that can securely connect to various data sources. This document outlines how organizations can leverage Honey Jars to create a unified, secure data access layer while maintaining enterprise-grade security and compliance.

Core Concepts

Honey Jars - Intelligent Data Containers

Honey Jars are secure, portable data containers that:

  • Connect to external data sources (databases, file servers, APIs)
  • Apply security policies and access controls
  • Enable AI-powered analysis while maintaining data sovereignty
  • Package knowledge for sharing or monetization.

Hives - Administrative Control Centers

Hives provide centralized management where administrators can:

  • Configure data source connections
  • Manage user permissions and access controls
  • Monitor data usage and compliance
  • Set up data governance policies.

Worker Bees - Data Connectors (Working Name)

Specialized connectors that:

  • Establish secure connections to data sources
  • Handle authentication and encryption
  • Transform data into AI-ready formats
  • Maintain audit trails.

Customer-Friendly Explanation

The Beehive Analogy

Think of your organization’s data ecosystem as a beehive:

  1. The Hive (Administrative Console)

    • Where the “Queen Bee” (admin) manages everything
    • Controls which bees can access which flowers (data sources)
    • Monitors the health and security of the colony.

  2. Worker Bees (Data Connectors)

    • Fly out to collect nectar (data) from various flowers (sources)
    • Know exactly which flowers they’re allowed to visit
    • Bring back only what they’re authorized to collect.

  3. Honey Jars (Data Containers)

    • Store the processed nectar (data) securely
    • Can be sealed and shared with other hives (organizations)
    • Contain not just data, but the intelligence to use it.

  4. The Honey (Processed Knowledge)

    • Ready-to-use insights from your data
    • Can be consumed by AI models safely
    • Retains the essence without exposing raw data.

Technical Architecture

Honey Combs - Quick Connect Templates

Honey Combs revolutionize data connectivity by providing pre-configured templates that Worker Bees can use to quickly establish secure connections. They serve two primary purposes:

  1. Continuous Flow: Maintain live connections that continuously feed data into existing Honey Jars
  2. Jar Generation: Create new Honey Jars from database dumps, API exports, or file system snapshots

Key Features:

  • Reusable Configurations: Save and share connection templates across teams.
  • Built-in Scrubbing: Optional PII removal and data masking at ingestion.
  • One-Click Deploy: Transform complex integrations into simple selections.
  • Compliance Ready: Pre-configured for GDPR, CCPA, HIPAA compliance.

Example Workflow:

# 1. Select a Honey Comb from the library
honey_comb: "PostgreSQL Production DB"

# 2. Choose operation mode
mode: "generate_honey_jar"  # or "continuous_flow"

# 3. Configure scrubbing (optional)
scrubbing:
  enabled: true
  profile: "gdpr_compliant"
  
# 4. Deploy Worker Bee
result: "New Honey Jar created with sanitized production data"

Data Source Connectivity Framework

data_sources:
  databases:
    - type: postgresql
      connector: "bee-postgres"
      features:
        - connection_pooling
        - ssl_encryption
        - query_sanitization
        - row_level_security
    
    - type: mysql
      connector: "bee-mysql"
      features:
        - connection_pooling
        - ssl_encryption
        - query_sanitization
    
    - type: mongodb
      connector: "bee-mongo"
      features:
        - connection_pooling
        - tls_encryption
        - document_filtering
    
    - type: snowflake
      connector: "bee-snowflake"
      features:
        - warehouse_management
        - role_based_access
        - data_sharing
  
  file_systems:
    - type: s3
      connector: "bee-s3"
      features:
        - bucket_policies
        - encryption_at_rest
        - versioning
    
    - type: sharepoint
      connector: "bee-sharepoint"
      features:
        - oauth_integration
        - document_libraries
        - metadata_extraction
    
    - type: google_drive
      connector: "bee-gdrive"
      features:
        - oauth_integration
        - team_drives
        - permission_sync
  
  apis:
    - type: rest
      connector: "bee-rest"
      features:
        - oauth2_support
        - rate_limiting
        - response_caching
    
    - type: graphql
      connector: "bee-graphql"
      features:
        - query_optimization
        - schema_introspection
        - subscription_support

Connection Security Model

class HoneyJarConnector:
    """Base class for all data source connectors"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.vault_client = VaultClient()
        self.audit_logger = AuditLogger()
    
    def connect(self, credentials: Optional[Dict] = None):
        """Establish secure connection to data source"""
        # Retrieve credentials from Vault if not provided
        if not credentials:
            credentials = self.vault_client.get_credentials(
                self.config['credential_path']
            )
        
        # Log connection attempt
        self.audit_logger.log_connection_attempt(
            user=self.config['user'],
            source=self.config['source_name'],
            timestamp=datetime.utcnow()
        )
        
        # Establish encrypted connection
        return self._establish_secure_connection(credentials)
    
    def query(self, query: str, params: Dict = None):
        """Execute query with security controls"""
        # Validate query against security policies
        if not self._validate_query(query):
            raise SecurityException("Query violates security policy")
        
        # Apply row-level security if configured
        query = self._apply_security_filters(query)
        
        # Execute and return results
        return self._execute_query(query, params)

Identity Provider Integration

identity_providers:
  supported:
    - name: "Active Directory"
      protocol: "LDAP/SAML"
      features:
        - group_sync
        - attribute_mapping
        - mfa_support
    
    - name: "Okta"
      protocol: "SAML/OIDC"
      features:
        - sso
        - provisioning
        - lifecycle_management
    
    - name: "Azure AD"
      protocol: "OIDC"
      features:
        - conditional_access
        - b2b_collaboration
        - pim_integration
    
    - name: "Google Workspace"
      protocol: "OIDC"
      features:
        - oauth2
        - directory_sync
        - mobile_management

passkey_configuration:
  primary_method: "WebAuthn"
  fallback_methods:
    - "TOTP"
    - "SMS (deprecated)"
  features:
    - platform_authenticators
    - roaming_authenticators
    - attestation_verification
    - backup_eligibility

Security Architecture

Multi-Layer Security Model

  1. Connection Security

    • TLS 1.3 for all connections
    • Certificate pinning for critical sources
    • Mutual TLS for high-security environments.

  2. Authentication & Authorization

    • Passkeys as primary 2FA method
    • Integration with enterprise IdPs
    • Fine-grained permission model
    • Temporary credential generation.

  3. Data Security

    • Encryption at rest and in transit
    • Field-level encryption for sensitive data
    • Data masking and tokenization
    • Audit trails for all access.

  4. Compliance & Governance

    • Policy-based access control
    • Data classification enforcement
    • Retention policy automation
    • GDPR/CCPA compliance tools.

Use Cases

1. Financial Services

scenario: "Risk Analysis Honey Jar"
data_sources:
  - trading_database: "real-time market data"
  - customer_database: "transaction history"
  - external_api: "credit scores"
capabilities:
  - fraud_detection
  - risk_scoring
  - compliance_reporting
security:
  - pci_dss_compliance
  - data_masking
  - audit_trails

2. Healthcare

scenario: "Patient Care Honey Jar"
data_sources:
  - ehr_system: "patient records"
  - imaging_server: "medical images"
  - lab_system: "test results"
capabilities:
  - diagnosis_assistance
  - treatment_recommendations
  - population_health_analytics
security:
  - hipaa_compliance
  - phi_encryption
  - access_logging

scenario: "Case Research Honey Jar"
data_sources:
  - document_management: "case files"
  - legal_databases: "precedents"
  - email_server: "communications"
capabilities:
  - document_analysis
  - precedent_search
  - timeline_construction
security:
  - client_privilege
  - data_segregation
  - retention_policies

Customer Benefits

For IT Administrators

  • Centralized Control: Manage all data connections from one “Hive”.
  • Security Compliance: Built-in compliance for major standards.
  • Easy Integration: Pre-built connectors for common systems.
  • Audit Trail: Complete visibility into data access.

For Business Users

  • Self-Service Analytics: Access data without IT tickets.
  • Secure Collaboration: Share insights, not raw data.
  • AI-Powered Insights: Get answers in natural language.
  • Mobile Access: Passkey authentication from any device.

For Executives

  • Data Monetization: Package and sell industry insights.
  • Risk Reduction: Maintain control over sensitive data.
  • Competitive Advantage: AI capabilities without cloud exposure.
  • Cost Optimization: Reduce data duplication and storage.

Technical Requirements

Minimum Infrastructure

honey_jar_requirements:
  compute:
    cpu: "4 cores"
    memory: "16GB"
    storage: "100GB SSD"
  
  network:
    bandwidth: "100Mbps"
    latency: "<50ms to data sources"
    protocols: ["HTTPS", "PostgreSQL", "MongoDB"]
  
  security:
    vault: "HashiCorp Vault or equivalent"
    certificates: "Internal CA or public certs"
    firewall: "Application-aware rules"

production_deployment:
  load_balancer:
    type: "HAProxy or NGINX"
    features: ["SSL termination", "Health checks"]
  
  honey_jar_cluster:
    nodes: 3
    configuration: "Active-Active"
    features: ["Auto-failover", "Load distribution"]
  
  data_cache:
    type: "Redis Cluster"
    size: "32GB"
    features: ["Persistence", "Replication"]
  
  monitoring:
    metrics: "Prometheus + Grafana"
    logs: "ELK Stack"
    alerts: "PagerDuty integration"

Glossary of Bee Terms

  • Hive: Administrative control center.
  • Honey Jar: Secure data container with AI capabilities.
  • Worker Bee: Data connector/integration service.
  • Nectar: Raw data from external sources.
  • Honey: Processed, AI-ready knowledge.
  • Pollen: Metadata and data schemas.
  • Queen Bee: System administrator.
  • Drone: Read-only data consumer.
  • Honeycomb: Structured data storage within a Honey Jar.
  • Honey Comb: Pre-configured data source template for quick connectivity.
  • Comb Library: Repository of reusable connection configurations.
  • Scrubbing Engine: Privacy-preserving data processor.
  • Bee Dance: Data synchronization protocol.
  • Royal Jelly: Premium/privileged data access.

This framework provides a foundation for STING’s data connectivity capabilities while maintaining the bee-themed branding and focusing on enterprise security needs.

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