On-Premise vs Cloud AI Data Protection
Comparing deployment models for enterprise AI data protection — on-premise, air-gapped, cloud, hybrid, and embedded options.
Problem
Enterprises operate in different infrastructure environments. Defense and intelligence agencies require air-gapped deployments. Financial institutions may mandate on-premise data processing. Cloud-native organizations want SaaS-based solutions. Many enterprises operate hybrid environments where different data types require different deployment models.
AI data protection must adapt to the enterprise's infrastructure requirements, not force infrastructure changes.
Explanation
- On-premise deployment. The encapsulation engine runs entirely within the enterprise data center. Sensitive data never traverses any network boundary. The AI enablement data layer operates within existing security perimeters. Only encapsulated data is sent to external AI services when needed.
- Air-gapped deployment. For the most sensitive environments, LLM Capsule can operate within air-gapped networks. Documents are encapsulated locally, transferred to an AI-connected environment through controlled channels, processed, and results are transferred back for local restoration.
- Cloud deployment. Available on AWS Marketplace. The encapsulation layer runs within the enterprise's cloud account or VPC. Data remains within the enterprise's cloud boundary while AI services are accessed externally.
- Hybrid deployment. Different document types or sensitivity levels route through different deployment modes within a single LLM Capsule instance.
- Embedded integration. LLM Capsule can be embedded into existing enterprise applications and platforms, operating as an AI enablement data layer within the enterprise's own software stack.
Enterprise Example
Government Agency — Air-Gapped Deployment
A government agency needs AI to process classified briefing documents for automated summarization. The classified network has no external connectivity.
LLM Capsule encapsulates documents on the classified network. Encapsulated data is transferred to the AI-connected environment through an approved data diode. AI generates summaries. Results are transferred back and restored on the classified network, producing classified-ready summaries.
FAQ
Yes. LLM Capsule supports on-premise, air-gapped, cloud, hybrid, and embedded deployment models. The encapsulation engine runs entirely within your environment regardless of deployment type.
See how LLM Capsule works with your data
Bring your documents, deployment constraints, and evaluation criteria. We demonstrate on your actual workflows.
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