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