AI SECURITY / RAG
Govern the Data Feeding Your AI
RAG pipelines pull from the same sensitive stores your access policies are supposed to protect — but most embed, retrieve, and generate with no enforcement. Lattix keeps policy on the data as it flows into your models.
Retrieval-augmented generation turns your document stores, wikis, and databases into model context. That is exactly where sensitive data lives — and most RAG stacks ingest, embed, and retrieve it with none of the access controls that govern the source systems. The result: a model that will happily surface a record to a user who was never authorized to see it, with no audit trail and no way to revoke what has already been embedded.
- Embeddings strip source-system permissions, so retrieval ignores who is actually authorized.
- Sensitive records leak into prompts, responses, and vector stores with no enforcement.
- There is no lineage from a generated answer back to the governed source data.
- Once data is embedded, there is no way to revoke or re-scope it as policy changes.
- Security and legal block AI initiatives because they cannot prove the data is governed.
Enforce Policy at Retrieval
Lattix keeps each document and record bound to its access policy as it enters the pipeline. At retrieval time, policy is evaluated against the requesting identity and context, so the model only ever receives context the user is actually authorized to see. Authorization is no longer left behind in the source system.
Preserve Lineage End to End
Every piece of context that reaches the model carries provenance. You can trace a generated answer back to the exact governed sources that informed it — essential for review, redress, and regulatory defensibility of AI outputs.
Revoke and Re-Scope Embedded Data
Because access is enforced by policy rather than baked into the index, you can revoke or re-scope data even after it has been embedded. When classification or entitlements change, the pipeline reflects it on the next request instead of requiring a full re-index.
Audit Every AI Access
Each retrieval and decision is written to a tamper-evident ledger, giving security teams a verifiable record of what data informed which interaction — the evidence base regulators and customers increasingly demand of AI systems.
Prevent Data Leakage
Sensitive records never reach a user the policy would have denied — even through a model.
Unblock AI Initiatives
Give security and legal the enforcement and evidence they need to approve RAG on real data.
Full Answer Lineage
Trace any generated response back to the governed sources that produced it.
Policy-Aware Retrieval
Retrieval honors the same attribute-based access control as your source systems.
Revoke After Embedding
Re-scope or revoke access without rebuilding the vector store from scratch.
Model-Agnostic
Works across LLM providers and vector stores — enforcement lives on the data, not the model.
Helps You Align With
Lattix provides the technical controls and audit capabilities to help your organization meet the requirements of these frameworks.
Explore Further
How does Lattix secure a RAG pipeline?
Lattix keeps each document bound to its access policy as it enters the pipeline and evaluates that policy at retrieval time against the requesting identity. The model only receives context the user is authorized to see, every access is audited, and answers retain lineage back to their governed sources.
Can Lattix prevent sensitive data from leaking through an LLM?
Yes. By enforcing attribute-based access control at retrieval rather than relying on source-system permissions that embeddings strip away, Lattix ensures restricted records never reach an unauthorized user — even when surfaced through a model's response.
What happens to data that is already embedded when policy changes?
Because access is enforced by live policy evaluation rather than baked into the index, you can revoke or re-scope embedded data without rebuilding the vector store. The pipeline reflects the new policy on the next request.
Does Lattix work with any LLM or vector database?
Yes. Enforcement lives on the data, not the model, so Lattix is model- and vector-store-agnostic and integrates across LLM providers and retrieval stacks.
Put Governance on Your AI Data
Tell us about your RAG stack and data sources, and we'll show you how Lattix enforces access and lineage on everything your models retrieve.
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