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.

Authorization failure incident and associated API call-graph in Caber

• 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.
• Scalable Automated Deployment: Caber deploys automatically into your AWS account and requires no agents or code changes. Only two observation points in the application, log ingest and an API gateway plug-in, are required for it to work.

• 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.

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

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