CDMC - BigID

CDMC Framework Overview: Scope and Challenges

The Cloud Data Management Capabilities (CDMC) Framework enables organizations across all industries to better manage sensitive data in the cloud.

The CDMC Framework establishes a set of controls and capabilities designed to help organizations manage and protect their cloud data, migrate securely to the cloud, and incorporate automation and technology for better data management. Organizations that handle sensitive, personal, or regulated data in cloud, multi‐cloud, and hybrid‐cloud environments should pay particular attention to the CDMC Framework.

The framework consists of six main components, 14 capabilities, and 37 sub-capabilities that provide in-depth guidance on how to protect sensitive data in a cloud/hybrid cloud environment.

Gouvernance des données & Accountability

Catalog, tag, and maintain data ownership roles for source data and newly generated cloud data with automation. Empower data stewards to validate and curate data; and find duplicate data for automated labeling, governance, and consolidation across all data sources and cloud targets.

Data Cataloging et Classification

Use advanced ML to find, tag, and catalog data and map sensitive data through automation. Catalog data and metadata can be exchanged with other data catalogs and across multi- and hybrid-cloud environments.

Données Accessibility & Usage

Quickly identify overexposed data and overprivileged user access to specific types of data by determining accessibility rights. Improve ethical decision-making and entitlement reviews based on the sensitivity and context of data. Mitigate risk by helping limit access to sensitive data.

Données Protection & Privacy

Protect data and maintain compliance. Automate data-driven privacy compliance, including privacy risk assessments (PIA), for new and emerging privacy and protection regulations. Secure personal, sensitive, and regulated data in the cloud. Transform data security with ML-based classification, file analysis, and apps to reduce risk.

Données Lifecycle Management

Ensure data quality through defining retention policies and illustrating workflows. Analyze and improve the quality of data in the cloud by leveraging dynamic profiling and setting data quality rules across all data. Automate and scale workflows that manage and enforce consistent data retention policies to retain or discard data in the cloud.

Data & Technical Architecture

Identify all instances of data stored in different data silos and identify the source or provenance of any data asset. Traverse through your data sources to trace the lineage flow. Validate data flows to confirm the lineage to produce evidence for data trust.

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