Prompts & inputs
Every query, instruction, and user input stays on-device. No prompt is ever sent to external servers.
Why Local AI · Security & Compliance
Complete control. Zero exposure.
When AI runs locally, your data never crosses a network boundary. No third-party servers. No API logs. No trust required. Just inference that stays where it belongs, on your infrastructure.
What stays on your machine.
With local inference, every piece of data involved in AI processing remains entirely within your control. Nothing is transmitted externally.
Model outputs
Generated text, completions, and responses are processed and stored locally, never logged remotely.
Embeddings & vectors
Semantic representations of your documents remain in your vector store, under your control.
Retrieved documents
RAG source documents and context chunks never leave your infrastructure during retrieval.
Conversation history
Multi-turn chat context and memory persist locally. No conversation data is shared externally.
Logs & telemetry
Usage metrics, debugging data, and operational logs stay within your observability stack.
Cloud AI risks vs. local AI.
Understanding the threat model helps you make informed architectural decisions for sensitive applications.
Cloud AI attack surface
- Data in transit: Prompts cross network boundaries, exposing them to interception risks
- Third-party access: Provider employees may access data for debugging or training
- Provider breaches: A single breach at the provider exposes all customer data
- API request logging: Providers typically log requests for billing and analytics
- Training data concerns: Your data may be used to improve provider models
Local AI protection
- Zero network exposure: Data never leaves your device or infrastructure
- No third-party trust: You don't depend on provider security practices
- Breach scope limited: A breach affects only your own systems, not a shared platform
- Full logging control: You decide what's logged, retained, and deleted
- Legal clarity: No external data processor relationships to manage
- No model training risk: Your data is never used to train third-party models
Compliance made simpler.
Local inference eliminates entire categories of compliance complexity by keeping data within your controlled environment.
GDPR
General Data Protection Regulation
GDPR requires lawful basis for processing personal data, data minimization, and respecting data subject rights. Local processing simplifies compliance by eliminating cross-border transfers and third-party processor agreements.
- No international data transfers to manage
- No Data Processing Agreements (DPAs) required with AI vendors
- Full control over data retention and deletion
- Simpler Article 30 records of processing
HIPAA
Health Insurance Portability and Accountability Act
HIPAA mandates technical safeguards for Protected Health Information (PHI). Using cloud AI with PHI typically requires Business Associate Agreements and careful vendor vetting.
- PHI never transmitted to external services
- No Business Associate Agreements needed for AI
- Audit trails remain within your HIPAA-compliant infrastructure
- Simplified breach notification scope
SOC 2
Service Organization Control 2
SOC 2 audits evaluate security, availability, processing integrity, confidentiality, and privacy controls. External AI services become part of your vendor risk assessment.
- No additional vendor risk assessments for AI
- Confidentiality controls stay within your perimeter
- Simpler control narratives around AI data flows
- Processing integrity under your direct control
Localization
Data localization requirements
Many jurisdictions and industries require data to remain within specific geographic boundaries. Cloud AI may route data through regions that violate these requirements.
- Data stays exactly where you deploy it
- Air-gapped deployment for classified environments
- Meets government and defense sector requirements
- Compatible with industry-specific mandates (finance, healthcare, public sector)
Important: Local AI simplifies compliance but doesn't guarantee it. You remain responsible for implementing appropriate security controls, access management, encryption, and organizational policies required by each framework. Consult qualified legal and compliance professionals for your specific situation.
Protect what matters most.
When you send data to cloud AI, you're trusting that provider with your most sensitive information. Local inference ensures trade secrets, proprietary algorithms, and confidential documents never leave your control.
Source code
Analyze, refactor, and document code without exposing proprietary logic.
Internal docs
Process contracts, strategies, and memos without third-party access.
Trade secrets
Keep formulas, processes, and competitive intelligence truly confidential.
Customer data
Build AI features on customer data while honoring confidentiality commitments.
When local is the right choice.
If any of these apply to your project, local inference should be your default architecture, not an afterthought.
Frequently asked questions.
Is local AI automatically secure?
No. Local inference eliminates external data exposure, but you're still responsible for securing the deployment environment. This includes access controls, encryption at rest, secure model storage, network segmentation, and proper authentication. Local AI reduces your attack surface, it doesn't eliminate the need for security best practices.
How do model updates work?
LM-Kit models are downloaded once and run entirely offline. Updates are pulled when you choose, not automatically pushed. For air-gapped environments, models can be transferred via secure media. You control the update schedule and can validate new versions in staging before production deployment.
What about incident response?
With local deployment, incident response is entirely within your control. There's no dependency on vendor communication or waiting for provider breach notifications. Your existing IR playbooks, monitoring, and forensics tools apply directly. You determine breach scope, notification timelines, and remediation steps.
Can I use cloud AI for some things and local for others?
Yes. Hybrid architectures are common: keep sensitive workloads (PHI, PII, trade secrets) entirely local with LM-Kit while using cloud AI for non-sensitive tasks. The decision belongs to you, not the framework.
What hardware do I need?
LM-Kit is optimized for efficient inference on standard hardware. Modern laptops can run capable models for development. Production workloads benefit from GPUs (NVIDIA, AMD, or Apple Silicon), but CPU-only deployment is fully supported. Check our documentation for specific model requirements and benchmarks.
Does LM-Kit phone home or collect telemetry?
LM-Kit does not require network connectivity for inference and does not transmit telemetry to external servers. License validation can work offline. If you implement your own telemetry using our OpenTelemetry integration, that data goes only to your observability stack.
Build privacy-first AI applications.
100% on-device. GDPR, HIPAA, and SOC 2 compliant by design. Start for free with our Community Edition.