Overview
Bedrock is a cutting-edge data management and security platform built on patented data discovery and classification technology.
Unified Metadata Lake
The Data-Aware Foundation for Security and Governance
The Challenge:
Enterprise data resides in disconnected silos across numerous systems, making it difficult to gain a holistic understanding of data sensitivity, location, access, and usage. This lack of centralized context hinders effective risk assessment and policy enforcement.
The Bedrock Solution:
Bedrock builds and maintains a Metadata Lake, a graph knowledge base that serves as a central repository for enterprise data context. The Metadata Lake maps relationships between metadata elements—such as data classification, entitlements, lineage, usage patterns, and ownership—without ever storing customer data itself.
Why It Matters:
The Metadata Lake provides the interconnected context needed to accurately assess data risk, automate governance policies, and streamline operations like access reviews, policy validation, and incident response.
Graph API
Programmatic Access to Unified Data Context
The Challenge:
Security and data governance workflows often require manual data gathering and correlation across multiple tools, leading to inefficiencies and errors. Integrating data context into existing workflows can be complex and slow.
The Bedrock Solution:
Bedrock offers a Graph API that provides programmatic access to the rich, contextual information stored within the Metadata Lake. Any data point visible in the Bedrock Console can be queried via the API, enabling automation and integration with existing systems.
Why It Matters:
The Graph API allows enterprises to make other tools smarter—enriching SIEM alerts, automating SOAR playbooks, and enhancing catalog inventories with deep data context. It’s the connective tissue that makes data-aware decision-making automatic.
Full-Context Entitlement Analysis
Understand Who Can Really Access Sensitive Data
The Challenge:
Scanning massive, distributed data environments is costly and time-consuming. Legacy tools rely on brute-force scanning or incomplete sampling—both inefficient and unreliable.
The Bedrock Solution:
Bedrock performs Full-Context Entitlement Analysis, mapping the complete access chain for every identity. It resolves nested groups, integrates federated identity data (like Okta or Azure AD), and calculates “effective permissions” for both users and service accounts. Bedrock’s Impact Score ranks identities by the sensitivity and volume of data they can access.
Why It Matters:
Security teams can finally see who can actually reach critical data. This visibility supports least-privilege enforcement, meaningful access reviews, and reduced identity-based data exposure.
Adaptive Scanning
Efficient, Petabyte-Scale Data Scanning
The Challenge:
Scanning massive, distributed data environments is costly and time-consuming. Legacy tools rely on brute-force scanning or incomplete sampling—both inefficient and unreliable.
The Bedrock Solution:
Bedrock’s Adaptive Scanning groups similar data objects by analyzing file paths, structure, and metadata to select representative samples intelligently. This approach achieves comprehensive coverage in hours instead of weeks, delivering visibility across petabyte-scale estates without redundant compute cost.
Why It Matters:
Adaptive Scanning delivers 10–100x lower TCO than traditional scanning. It ensures continuous, cost-efficient visibility into enterprise data without performance or budget trade-offs.
Serverless Outpost Architecture
Agentless, Scalable, and Efficient In-Environment Scanning
The Challenge:
Traditional tools rely on persistent agents or proxies, creating overhead, performance impact, and ongoing management burden.
The Bedrock Solution:
Bedrock uses a Serverless Outpost Architecture, deployed via infrastructure-as-code in minutes. These outposts operate as ephemeral functions (e.g., AWS Lambda), performing discovery and classification directly within the customer’s environment. They scale up for work and scale down to zero when idle.
Why It Matters:
The Serverless Outpost model eliminates agents, minimizes cost, and increases security by keeping all scanning inside the customer’s boundary. It’s scalable, efficient, and maintenance-free.
Zero Data Access
Secure Analysis Without Data Exfiltration
The Challenge:
Many data security tools copy sensitive data into external clouds for analysis, creating new risks and compliance challenges.
The Bedrock Solution:
Bedrock adheres to a Zero Data Access principle. All scanning, classification, and analysis happen in the customer’s environment through Serverless Outposts. Only anonymized metadata—like file paths, sensitivity labels, or access patterns—is transmitted to Bedrock’s SaaS for visualization and policy management.
Why It Matters:
Zero Data Access preserves data sovereignty, simplifies compliance, and eliminates the need to trust a third party with your most sensitive data.
Natural Language Policy Engine
Translate Business Policies into Automated Controls
The Challenge:
GRC policies are written in human language, not code, making them hard to enforce consistently across complex data platforms. Translating them into technical rules is manual and error-prone.
The Bedrock Solution:
Bedrock’s Natural Language Policy Engine ingests policies directly from GRC documents. It interprets intent, identifies relevant data types, and maps them to the Metadata Lake’s query language. This enables enforcement of rules like “HR data must remain within HR systems” or “No PCI data in development environments.”
Why It Matters:
This bridges the gap between policy definition and implementation. Organizations can ensure business rules are accurately, automatically, and continuously enforced across their entire data landscape.
Metadata Lake Copilot
Accelerate Investigation and Resolution with AI
The Challenge:
Investigating data risk or compliance issues requires manually correlating information from multiple tools—a slow, fragmented process.
The Bedrock Solution:
The Metadata Lake Copilot provides a conversational AI interface to Bedrock’s graph knowledge base. Users can ask natural-language questions (“Which users have access to PHI data?”) and receive instant, context-rich responses including lineage, ownership, and exposure details.
Why It Matters:
The Copilot turns complex investigations into simple queries. It saves analysts hours of manual effort and accelerates both incident response and audit preparation.
Semantic Search
Find Data by Meaning, Not Just Keywords
The Challenge:
Enterprises use inconsistent terminology for the same data concepts (“employee record” vs. “staff info”), making discovery unreliable and manual.
The Bedrock Solution:
Bedrock’s Semantic Search extracts features during scanning to understand meaning, not just keywords. It indexes data semantically so a search for “passport” will also find “travel document.”
Why It Matters:
Semantic Search ensures policies and audits find all relevant data, regardless of inconsistent naming conventions, dramatically reducing manual mapping work.
Correlation-Based Lineage
Trace Data’s True Path Across Platforms and Formats
The Challenge:
Traditional lineage methods rely on logs or DDL parsing, which don’t cover unstructured data or inter-platform movement.
The Bedrock Solution:
Bedrock’s Correlation-Based Lineage uses patented fingerprinting to compare data content and metadata across systems. It infers lineage even when data changes format, moves across boundaries, or lacks event logs.
Why It Matters:
Correlation-Based Lineage reveals data’s true provenance and propagation across structured and unstructured environments. It’s foundational for understanding exposure, preventing leaks, and ensuring compliance.
AI-Driven Categorization & Classification
Automate Data Understanding and Sensitivity Tagging
The Challenge:
Manual data classification is slow, subjective, and unscalable. Simple pattern matching produces high false positives and misses contextual nuance.
The Bedrock Solution:
Bedrock’s AI-Driven Categorization uses fine-tuned models that analyze data semantics and structure to automatically group information into business categories (e.g., “Customer Records,” “Source Code”). These categories are then mapped to sensitivity levels defined by organizational policy.
Why It Matters:
AI-Driven Classification delivers precise, automated labeling at enterprise scale, dramatically reducing manual effort while improving accuracy and policy alignment.
Universal Tags
Enforce Policies Natively Across Platforms
The Challenge:
Maintaining consistent classifications across multiple data systems is operationally complex and prone to drift.
The Bedrock Solution:
Bedrock automatically applies Universal Tags—native labels that mirror Bedrock classifications—to data assets in their home systems (e.g., Purview labels, S3 object tags, Snowflake column tags).
Why It Matters:
Universal Tags extend Bedrock’s intelligence to the platforms where enforcement happens. This ensures consistent, policy-aligned behavior everywhere your data lives.
Bidirectional Integrations
Closing the Loop on Remediation Workflows
The Challenge:
Findings from one system often require manual follow-up in another, leading to slow, error-prone remediation and poor accountability.
The Bedrock Solution:
Bedrock integrates bidirectionally with SIEM, SOAR, ITSM, and data catalog tools. When an issue is marked as resolved in one platform, Bedrock automatically re-validates it in the source system to confirm the fix.
Why It Matters:
This creates a closed-loop remediation cycle. Teams spend less time tracking tickets and more time solving real problems—knowing that every resolution is verified.