Securing AI Systems with Locktera
Overview
AI systems process sensitive information across ingestion, indexing, training, fine-tuning, and inference. Traditional AI architectures rely on infrastructure permissions, storage controls, and application logic to restrict access to this data.
These controls cannot reliably prevent unauthorized access if storage systems, vector indexes, training pipelines, or deployment environments are compromised.
Locktera secures AI systems by encrypting documents, datasets, and models into immutable containers and enforcing access cryptographically at the time of decryption.
AI pipelines and services can only decrypt containers they are explicitly authorized to access. Authorization is granted to specific users, services, or pipeline identities through container access policies (DRM), evaluated at decryption time.
The Core Security Principle
Locktera moves enforcement from infrastructure to the data itself.
Instead of relying on:
• Storage permissions
• Index isolation
• Network segmentation
• Application-layer checks
Each protected asset is stored as a .tera container: encrypted, immutable content plus policy-enforced decryption. Locktera enforces access cryptographically when a document, dataset, or model is decrypted. If authorization fails, decryption is denied — regardless of where the container is stored or how it was distributed.
AI Lifecycle Coverage
Locktera protects sensitive assets throughout the AI lifecycle:
• Document ingestion and knowledge bases
• Retrieval-Augmented Generation (RAG) systems
• Training datasets
• Fine-tuning workflows
• Model weights and distribution
• Runtime inference pipelines
At every stage, access is evaluated cryptographically before data is exposed to the AI system.
Security Guarantees
Locktera ensures:
• Sensitive data remains encrypted at rest and in transit
• Unauthorized systems cannot decrypt protected documents, datasets, or models
• Index or storage compromise does not expose plaintext data
• Access policies are enforced independently of infrastructure
• Access may be revoked at any time
• All decryption events are audit logged
This prevents unauthorized use of sensitive data in AI responses, training jobs, or model deployments.
What This Enables
By enforcing access at the container level, Locktera enables:
• Secure enterprise AI assistants
• AI systems handling regulated data (healthcare, finance, legal)
• Secure customer knowledge bases
• Controlled AI model distribution
• Auditable AI data usage
Security becomes enforceable at the data object level — not dependent on trusted infrastructure.
Next Step
For a detailed implementation example, see:
