Fine-Grained Data Security for Generative AI

Caber is delivering a radically new architecture for data security that identifies rather than classifies data to secure inputs to Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) systems, and monitor their output for sensitive data disclosure.

GenAI Security Challenges

Chunked Data

Data in GenAI and RAG systems is chopped up and aggregated with other data before being vectorized. This thwarts the use of object-centric DLP and traditional data security solutions.

Per-Customer Policies

Individual customer mandates on how their data may be used with AI make compliance-oriented classifications (PII, PHI, etc.) overly-broad for securing GenAI use cases.

Lack of Visibility

Frameworks like LangChain for building GenAI apps pull data from many sources on-demand but lack the ability to show the source, ownership, or permissions that belong to that data.

How Caber is Different

Deterministic Detection

Precise and accurate identification of data with false positive rates less than .001%

Fine-Grained Control

Connect data to existing ownership and permissions on data-at-rest, or enrich with metadata from customs sources.

Byte-level Data Tracing

Trace the individual API calls that move data bytes from source to your AI systems to determine where and how data security incidents occurred.

Remediation Options

Get application aware remediation options that account for non-incident data flows to avoid application breakage.

Agentless Deployment

Caber deploys with a single click automatically in cloud environments without impacting to GenAI applications

Caber Authorization of Data Use
Call graph shows exactly how an incident took place
Generate and Push Policies


Caber is backed by data and security industry veterans from Cisco, Akamai, Riverbed, Netskope, Meta, and Oracle.

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