Gartner Research

How to Successfully Design and Implement a Data-Centric Security Architecture


Security and risk management technical professionals must implement data-centric security architecture to mitigate threats and compliance issues affecting data. DCSA controls discover, classify, protect and monitor data throughout its life cycle, whether on-premises or in the cloud.

Published: 22 July 2019

ID: G00390767

Analyst(s): Joerg Fritsch Mike Wonham

Table Of Contents

Problem Statement

The Gartner Approach

The Guidance Framework

  • Prework
    • Data-Centric Controls Include Data-Level Controls and Infrastructure-Level Controls
  • Data-Centric Control Families
  • Data Silos
  • DCSA vs. Data Security Governance
  • Step 1: Data Mapping
    • Tooling Up for Data Mapping
    • Playbook for Great Data Mapping
  • Step 2: Data Discovery and Classification
    • Tooling Up for Data Discovery and Classification
    • Playbook for Great Data Discovery and Classification
  • Step 3: Data Flow Modeling
    • Data Zones
    • Arcs
    • Nodes
    • Tooling Up for Data Flow Modeling
    • Playbook for Great Data Flow Modeling
  • Step 4: Data Control Examination
    • Control Placement: Enforcement Points
  • Step 5: Product Examination
    • Matching Control Requirements With Controls in the Data Flow Security Graph
    • Product Capability Matrix
    • Product Rationalization
    • Playbook for a Great Product Examination
    • Selected DCSA Vendors and Their Capabilities
  • Follow-Up
    • Conduct a Feasibility Study
    • Automate and Optimize

Risks and Pitfalls

  • Related Guidance

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