COURSE · OPS · B017

Security and Privacy for Local AI

Learn security and privacy for local ai through RunLocalAI's practical lens: security, privacy, api and auth, hardware fit, runtime settings, verification habits and local-vs-cloud tradeoffs.

16 chapters6hOperator trackBy Fredoline Eruo
PREREQUISITES
  • B001
  • B003

Course B017: Security and Privacy for Local AI

Local AI deployments handle sensitive data, execute arbitrary code generation, and often lack the security controls found in cloud services. Without deliberate hardening, these systems become attack surfaces for data exfiltration, prompt injection, and compliance violations. This course walks through threat modeling, authentication, network hardening, and privacy regulations—giving operators concrete steps to defend their deployments.

You will know how to:

  • Identify and mitigate prompt injection, data leakage, and supply chain risks
  • Implement API authentication and key rotation in local AI services
  • Configure network security and TLS for local API endpoints
  • Meet GDPR and CCPA requirements for AI data processing
  • Build incident response procedures and audit logging
CHAPTERS
  1. 01Why Security MattersLocal AI security succeeds through defaults: deny-by-default, minimal exposure, and frequent updates. Most compromises happen because operators exposed services that should have stayed internal.10 min
  2. 02Threat Modeling for AIThreat modeling is not a one-time exercise. Revisit it when adding new capabilities, integrating new data sources, or changing deployment architecture. A model that was internal-only becomes external-facing when you add a web UI.15 min
  3. 03Prompt Injection AttacksNo single defense stops prompt injection reliably. Layer multiple controls: input validation, context isolation, output filtering, and user education. Attackers adapt faster than defenders, so assume some attacks will succeed and focus on limiting blast radius.20 min
  4. 04API AuthenticationChoose authentication based on exposure level. Internal-only services can use API keys. Services exposed across a network need TLS plus key auth. High-security environments warrant mTLS. Never expose unauthenticated AI APIs on anything other than localhost.20 min
  5. 05API Key ManagementAPI keys are credentials. Treat them like passwords: generate randomly, rotate frequently, store securely, and revoke immediately when compromised. A key that lives for years is a key that will eventually be stolen.15 min
  6. 06Data IsolationData isolation is architecture, not configuration. Build isolation into your deployment design from the start. Retrofitting isolation into a flat architecture requires refactoring.20 min
  7. 07Network SecurityNetwork security is about controlling reachability. If an AI service doesn't need to initiate outbound connections, block them. If management interfaces don't need to be accessible from workstations, restrict them to admin subnets.20 min
  8. 08Firewall ConfigurationFirewall rules without monitoring are incomplete. Log dropped packets to identify blocked attacks and verify rules work as intended. Review logs weekly to catch drift from intended configuration.20 min
  9. 09HTTPS for Local APIsTLS is not optional for AI APIs handling sensitive data. Even on internal networks, encryption prevents eavesdropping from compromised systems on the same subnet. Use valid certificates (internal CA or public) to avoid client trust issues.20 min
  10. 10Model ProvenanceModel provenance is chain-of-custody for AI systems. Know where every model comes from, verify its integrity before loading, and document the chain. A compromised model can undermine all other security controls.15 min
  11. 11Supply Chain RisksSupply chain security is trust management. Every library you import, every API you call, every model you load is a trust decision. Minimize dependencies, verify sources, and assume any component can be compromised.20 min
  12. 12GDPR ComplianceGDPR compliance is not a one-time setup. It requires ongoing controls: automated data deletion, documented legal bases, privacy impact assessments, and breach notification procedures. Build these into your AI architecture from the start.15 min
  13. 13CCPA ConsiderationsCCPA applies based on who your users are, not where your servers are. If you serve California residents, you likely need CCPA controls. The operational burden is manageable: document what you collect, provide deletion mechanisms, and update notices.15 min
  14. 14Audit LoggingAudit logs only provide value if you review them. Configure alerts for anomalous patterns: authentication failures, unusual API usage, or data access outside normal hours. Logs that nobody reads are wasted storage.15 min
  15. 15Incident ResponseIncident response succeeds through preparation. Run quarterly exercises against your playbooks. Identify gaps in detection speed, communication paths, and containment procedures before real incidents occur.20 min
  16. 16Security Checklist ProjectA security checklist only provides value if you execute it consistently. Automate checks where possible, schedule reviews for manual checks, and track findings to completion. Treat the checklist as a living document—update it when you discover new risks or implement new controls.25 min
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