Trust, security, and compliance at the core

Technical security architecture: local AI models, on-premise hosting options, and zero data exfiltration.

Architecture

Local AI models and on-premise hosting

Local Model Deployment

All AI models run locally on your infrastructure or our dedicated servers. No data is sent to external AI APIs or cloud services. Models are containerized using Docker and can be deployed on-premise, in your private cloud, or on dedicated hardware we provision.

Models run in isolated containers with resource limits and network isolation
Inference happens entirely within your network boundary
No external API calls or data transmission to third-party services

On-Premise Hosting Options

We can deploy our entire stack on your premises or set up dedicated infrastructure in your data center. This ensures complete data sovereignty and eliminates any risk of data leaving your network.

Full stack deployment on your hardware or our dedicated servers
We can provision and manage infrastructure in your facility
All data processing occurs within your network perimeter
Air-gapped deployment options available for highest security requirements

Security Implementation

Technical security measures

Encryption

AES-256 encryption at rest for all databases and file storage. TLS 1.3 for all network communications. Encryption keys managed via HashiCorp Vault with key rotation policies. Database-level encryption with row-level security where applicable.

Access Controls

Role-based access control (RBAC) with principle of least privilege. Multi-factor authentication (MFA) required for all admin access. SSH key-based authentication for server access. All access attempts logged to SIEM with alerting on anomalies.

Network Security

Private networks with VPN access only. Firewall rules restrict outbound connections. Network segmentation isolates AI inference from data storage. Intrusion detection systems (IDS) monitor for unauthorized access attempts.

Risk Management

AI risk management and data privacy controls

AI Output Validation

All AI-generated outputs are logged with input prompts, model version, inference parameters, and timestamps. Human-in-the-loop validation workflow requires expert approval before any AI output is used in production. Automated quality checks flag outputs for review based on confidence thresholds and anomaly detection.

Complete audit trail of all AI interactions stored in immutable logs
Model versioning and rollback capabilities for production models
A/B testing framework for model comparison before deployment

Data Privacy Implementation

Data minimization: only required fields are collected and processed. Automatic data retention policies delete data after specified retention periods. Right to deletion implemented via API endpoints. Data anonymization for training datasets. No cross-client data access or model training on client data without explicit consent.

GDPR-compliant data processing agreements and privacy policies
Data subject access request (DSAR) automation for compliance
Pseudonymization and tokenization for sensitive data fields

Compliance & Auditing

SOC 2 Type II controls implemented. Regular penetration testing and vulnerability assessments. Compliance with financial regulations (SOX, PCI-DSS where applicable). Automated compliance checks in CI/CD pipeline. Immutable audit logs for all system changes and data access.

Automated compliance reporting and evidence collection
Third-party security audits and certifications
Incident response procedures with defined SLAs

Discuss your security and hosting requirements

Contact us to discuss on-premise deployment options, security architecture, and compliance requirements for your organization.

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