Bedrock Data offers an AI‑native Data Security Posture Management platform that automatically discovers, classifies, and maps sensitivity, lineage, and entitlement metadata across cloud, SaaS, and on‑premise environments. Its serverless Outpost architecture builds a graph‑based Metadata Lake and a natural‑language policy engine to enforce least‑privilege access and remediate violations, while ArgusAI extends governance to generative‑AI workloads.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to maintain continuous visibility and accurate classification of sensitive data across heterogeneous cloud, SaaS, and on‑premise environments. This fragmented view hampers enforcement of least‑privilege access, leads to compliance gaps, and creates hidden leakage vectors for AI and generative‑AI workloads.
Solution
Bedrock Data delivers an AI‑native Data Security Posture Management (DSPM) platform that autonomously discovers and classifies petabytes of data in hours. Its serverless Outpost architecture performs adaptive, metadata‑only scanning inside the customer’s perimeter, populating a graph‑based Metadata Lake that captures sensitivity, lineage, usage, and entitlement context. A natural‑language policy engine translates business rules into actionable controls, while universal tags are synchronized directly to data stores such as Microsoft Purview, Snowflake, and AWS. The platform enforces least‑privilege access by mapping entitlement chains to actual data exposure and automatically remediates violations. ArgusAI extends this foundation to AI workloads, tracing data flow through retrieval‑augmented generation (RAG) systems and preventing restricted content from being surfaced by autonomous agents.
Target Audience
Primary customers are enterprise security, compliance, and data‑governance teams managing multi‑cloud and on‑premise data estates, as well as AI/ML engineering groups that require data‑aware controls for generative‑AI applications.
Features
- Adaptive Scanning with serverless Outpost functions that index metadata at petabyte scale in hours, eliminating agents and preserving data residency.
- Unified Metadata Lake graph that aggregates >50 metadata types (sensitivity, lineage, usage, entitlements) for real‑time risk analysis.
- AI‑driven categorization and classification models that understand semantic context to assign precise sensitivity labels.
- Universal Tags automatically propagated to native platforms (Purview, Snowflake, S3, etc.) enabling native policy enforcement.
- Full‑Context Entitlement Analysis that resolves nested groups and service accounts to compute effective permissions and impact scores.
- Natural Language Policy Engine that ingests GRC documents and generates enforceable policies without manual rule coding.
- ArgusAI data‑aware governance layer that monitors AI model consumption, provides real‑time data flow tracing, and audits RAG interactions.
- Graph API and bidirectional integrations for seamless enrichment of SIEM, SOAR, CNAPP, and data catalog tools.