Dvina provides enterprise-grade privacy features including local LLM deployment, on-premise infrastructure options, and comprehensive audit capabilities for organizations with complex compliance requirements.
Individual users need strong privacy protections. But enterprises face an entirely different level of complexity: regulatory compliance, internal governance policies, audit requirements, and the challenge of managing hundreds or thousands of users with varying access needs.
Standard privacy features aren't enough when you're managing sensitive client data, proprietary business intelligence, regulated information, or confidential strategic plans across large teams.
Dvina's enterprise privacy features provide the deployment flexibility, local model options, and audit capabilities that organizations need to meet their most demanding security and compliance requirements.
The Enterprise Privacy Challenge
Organizations face unique privacy and security challenges that individual users don't:
Data sovereignty and control
Regulated industries or sensitive operations may require that data never leaves organizational infrastructure or that AI processing happens entirely on-premise with locally hosted models.
Regulatory compliance across jurisdictions
Global organizations must comply with GDPR in Europe, CCPA in California, PIPEDA in Canada, LGPD in Brazil, and industry-specific regulations like HIPAA, SOX, or PCI-DSS, often simultaneously.
Audit and accountability requirements
Regulators, auditors, and internal compliance teams need detailed logs showing who accessed what data, when, and why. "Trust us" isn't acceptable; organizations need proof.
Custom policy enforcement
Every organization has unique needs: data retention schedules, approval workflows, encryption requirements, or acceptable use policies that must be technically enforced, not just documented.
Model transparency and control
Organizations need to know exactly which AI models are processing their data, with the option to use specific models optimized for their industry or compliance requirements.
Third-party risk management
When using cloud services, organizations need assurance that their data is isolated from other customers and that the provider's security practices meet their standards.
Local LLM Deployment
For organizations requiring complete control over AI processing, Dvina supports deployment with locally hosted language models:
Supported Local Models
Open-source large language models
- GPT-OSS 120B: Powerful open-source model for general-purpose tasks
- GPT-OSS 20B: Lighter weight model for resource-constrained environments
- DeepSeek: Efficient, high-performance model optimized for enterprise use
- Gemma 3: Google's open-source model family for various scales
Industry-specific specialized models
- GemmaMed: Healthcare-optimized model trained on medical literature and terminology
- Custom fine-tuned models for legal, financial, or other specialized domains
Why Local LLM Deployment Matters
Complete data sovereignty
AI processing happens entirely within your infrastructure. Sensitive data never leaves your network, even for model inference.
Regulatory compliance
Industries with strict data handling requirements (healthcare, finance, government) can use AI while ensuring data never transmits to external services.
Air-gapped environments
Deploy Dvina with local models in completely isolated networks with no internet connectivity, meeting defense, intelligence, or critical infrastructure security requirements.
Customization and fine-tuning
Use models specifically trained or fine-tuned for your industry's terminology, workflows, and use cases.
Cost predictability
No per-token API costs; inference runs on your hardware with fixed infrastructure expenses.
Performance optimization
Deploy models on hardware optimized for your specific workloads, from high-throughput GPU clusters to edge devices.
Use Cases for Local LLM Deployment
Healthcare organizations
Deploy GemmaMed on-premise to analyze patient records, clinical notes, and research data without HIPAA concerns about data leaving the facility. Models trained on medical terminology provide better accuracy for healthcare-specific tasks.
Financial services
Banks and investment firms can run AI analysis on proprietary trading strategies, customer financial data, and market research using local models, ensuring sensitive financial information never transmits externally.
Government and defense
Classified or sensitive government data can be processed using air-gapped Dvina deployments with local models, meeting stringent security clearance requirements.
Legal firms
Law firms handling privileged attorney-client communications can use local AI models to analyze case files, contracts, and legal research without risking confidentiality breaches.
Research institutions
Universities and research labs working with proprietary or pre-publication data can leverage AI assistance while maintaining complete control over intellectual property.
Manufacturing and IP protection
Companies with trade secrets, proprietary formulas, or confidential designs can use local AI models to analyze engineering documents and research data without external exposure.
On-Premise and Private Cloud Deployment
For organizations with the strictest security and compliance requirements, Dvina offers deployment options beyond standard cloud hosting:
On-Premise Deployment
Install and run Dvina entirely on your own infrastructure, within your own data centers. Data never leaves your physical control.
When on-premise makes sense
- Highly regulated industries: Healthcare (HIPAA), finance (SOX, PCI-DSS), government contractors
- Data sovereignty requirements: Countries or sectors requiring data to remain within specific borders
- Zero trust in external providers: Organizations with policies against any cloud usage
- Custom security infrastructure: Need to integrate with existing security tools and networks
- Air-gapped environments: Systems that must be completely isolated from the internet
- Local LLM requirements: Processing must happen on-premise with locally hosted models
Private Cloud Deployment
Dedicated Dvina instance running in a private cloud environment (AWS VPC, Azure Private Cloud, or your preferred provider) with no shared infrastructure.
Benefits over multi-tenant cloud
- Physical isolation: Your data never shares servers with other customers
- Custom network configuration: Define your own network topology, firewall rules, and access controls
- Dedicated resources: No "noisy neighbor" issues or resource contention
- Custom security controls: Implement organization-specific security tools and monitoring
- Flexible model deployment: Choose between cloud-based models or deploy local LLMs in your private cloud
Hybrid Deployment
Combine on-premise deployment for the most sensitive data with cloud deployment for general use, with secure integration between environments.
Local LLM + Hybrid Cloud Example
- Critical patient data processed with GemmaMed on on-premise infrastructure
- General administrative tasks use cloud-based models
- Secure API integration between environments with data classification policies
Implementation Process
- Requirements assessment: Dvina works with your team to understand infrastructure, model requirements, security, and compliance needs
- Architecture design: Custom deployment architecture tailored to your environment, including model selection and hardware requirements
- Hardware planning: For local LLM deployment, specify GPU/CPU requirements based on chosen models and expected workload
- Security review: Joint security assessment and threat modeling
- Deployment and testing: Staged rollout with comprehensive testing of both platform and models
- Model optimization: Fine-tune local models for your specific use cases and performance requirements
- Training and handoff: Knowledge transfer to your IT, security, and data science teams
- Ongoing support: Dedicated enterprise support with SLAs, including model updates and optimization
Custom Data Policies and Governance
Every organization has unique requirements that off-the-shelf policies can't address:
Custom Retention Policies
Define exactly how long different types of data are retained before automatic deletion.
Examples
- Legal discovery: Retain all communications for 7 years
- GDPR compliance: Delete customer data 30 days after account closure
- Financial records: Keep transaction data for 10 years, then archive
- HR records: Retain employee data for 5 years post-employment
- Healthcare: Maintain patient records per HIPAA requirements (typically 6 years)
Approval Workflows
Require management approval before certain actions or data access.
Examples
- Senior executives must approve access to strategic planning documents
- Legal team review required before sharing client information externally
- Multi-person approval for data deletion or export
- Compliance officer sign-off on policy changes
- Medical director approval for certain patient data queries
Data Classification and Handling
Automatically classify data and enforce handling rules based on sensitivity.
Examples
- "Confidential" data requires encryption and access logging
- "Public" information can be shared freely
- "Regulated" data triggers compliance checks and audit trails (HIPAA, PCI-DSS, etc.)
- "Trade Secret" materials require multi-factor authentication
- "PHI" (Protected Health Information) automatically routes to local LLM processing
Geographic and Jurisdictional Controls
Enforce data residency and access rules based on location.
Examples
- EU citizen data must remain in EU data centers
- US government data cannot be accessed from outside the country
- Patient data processed only with on-premise GemmaMed model
- Different encryption standards by jurisdiction
- Chinese market data stays within China-based infrastructure with local models
Model Selection Policies
Control which AI models process different types of data.
Examples
- PHI data must use on-premise GemmaMed only
- Financial analysis uses specific fine-tuned models
- General queries can use any available model
- Confidential documents route to local GPT-OSS deployment
- Public data can leverage cloud-based models for performance
Custom Encryption Requirements
Implement organization-specific encryption policies.
Examples
- Use your own encryption keys (BYOK - Bring Your Own Key)
- Different encryption algorithms for different data classifications
- Hardware security module (HSM) integration
- Quantum-resistant encryption for long-term sensitive data
- Field-level encryption for specific data types
[Audit logs, SSO, DLP sections remain the same as before]
Model Management and Updates
Local Model Lifecycle Management
Model versioning
Track which model versions are deployed, test new versions before production rollout, and maintain rollback capabilities.
Update management
Receive notifications of new model releases, security patches, and performance improvements. Control update timing to fit maintenance windows.
Performance monitoring
Monitor model inference performance, accuracy metrics, and resource utilization to optimize deployments.
Custom fine-tuning
Work with Dvina's team to fine-tune open-source models on your proprietary data for domain-specific improvements.
A/B testing
Deploy multiple model versions simultaneously and route traffic for comparison testing before full rollout.
Real-World Enterprise Use Cases
Hospital Networks and Healthcare Systems
Large hospital networks handling millions of patient records can deploy on-premise Dvina with GemmaMed models to ensure PHI never leaves their data centers. This approach enables HIPAA-compliant AI-powered clinical decision support, research analysis, and administrative automation while maintaining complete data sovereignty.
Global Financial Institutions
Multinational banks operating across multiple jurisdictions can leverage hybrid architecture: on-premise local LLMs (DeepSeek) in each region for sensitive financial data analysis, combined with cloud deployment for general business operations. This structure allows compliance with regional data residency requirements while maintaining operational flexibility.
Governments and Public Sector
Federal, state, and local government agencies managing sensitive citizen data can deploy on-premise Dvina with local LLMs to ensure compliance with government data protection requirements. Municipal services, tax agencies, social services departments, and regulatory bodies can leverage AI for document processing, citizen service automation, and policy analysis while maintaining complete control over sensitive government and citizen information.
Defense Contractors and Government Agencies
Organizations handling classified information can implement air-gapped Dvina deployments with local GPT-OSS models for secure document analysis. Zero internet connectivity eliminates data leakage risks while custom-trained models can be optimized for military and technical terminology.
International Law Firms
Large law firms with multiple offices can use on-premise deployment with local LLMs to ensure attorney-client privileged communications are processed entirely within firm infrastructure, protecting confidential legal strategies from any external exposure while enabling AI-assisted legal research and document analysis.
Pharmaceutical and Biotech Research
Companies developing new drugs can deploy private cloud Dvina with GemmaMed models fine-tuned on their proprietary research databases. This protects pre-publication drug development data and clinical trial results while enabling AI-assisted research and analysis.
Manufacturing Companies with Trade Secrets
Manufacturers protecting proprietary designs and processes can implement on-premise deployment with local DeepSeek models in secure, air-gapped data centers. Engineers can leverage AI to analyze proprietary designs and research data without external exposure, while DLP integration prevents accidental data leakage.
Financial Services Firms
Investment firms and asset managers analyzing proprietary trading strategies can use local LLM deployments to ensure market research, portfolio analysis, and trading algorithms remain completely confidential while benefiting from AI-powered insights.
Academic Research Institutions
Universities conducting sensitive research or working with pre-publication data can deploy Dvina with local models to maintain intellectual property protection while enabling AI-assisted data analysis, literature review, and research collaboration.
[Rest of the content remains similar with updated references to local LLM capabilities]
Getting Started with Enterprise Privacy Features
Assessment Phase
- Requirements gathering: Work with Dvina team to document security, compliance, model requirements, and functional needs
- Model selection: Evaluate which local LLMs best fit your use cases, compliance requirements, and infrastructure
- Architecture review: Evaluate deployment options (on-premise, private cloud, hybrid) and integration points
- Hardware planning: Specify GPU/CPU requirements for local model deployment
- Proof of concept: Limited pilot with representative use cases, data, and chosen models
Implementation Phase
- Environment setup: Deploy infrastructure (cloud, private cloud, or on-premise) and install chosen local models
- Model testing and optimization: Validate model performance, accuracy, and resource utilization
- Configuration: Implement custom data policies, model routing rules, SSO integration
- Integration: Connect to existing enterprise systems (identity, SIEM, DLP, etc.)
- Security hardening: Apply organization-specific security controls
- Testing and validation: Comprehensive security, functionality, and model performance testing
Launch Phase
- User training: Train administrators, power users, and end users on platform and model-specific capabilities
- Phased rollout: Staged deployment to user groups with monitoring
- Model monitoring: Track performance, accuracy, and usage patterns
- Ongoing optimization: Regular reviews and adjustments based on usage patterns and model performance
Pricing
Enterprise privacy features are available through custom enterprise agreements. Contact our enterprise sales team for a tailored quote based on your:
- Number of users
- Deployment model (cloud, private cloud, on-premise)
- Local LLM requirements (models, scale, hardware)
- Required features and integrations
- Support tier
- Compliance requirements
The Bottom Line
Enterprise privacy isn't just about strong encryption and secure hosting. It's about deployment flexibility, local model control, comprehensive audit capabilities, and integration with your existing security infrastructure.
Dvina's enterprise privacy features provide the tools organizations need to protect sensitive data, process information with local AI models, meet complex compliance requirements, and maintain the visibility and control that auditors, regulators, and security teams demand.
Enterprise-grade privacy. Local model control. Enterprise-level accountability.