Data-Centric Security: Protecting What Matters Most

In my previous post, we looked at the importance of focusing on access control and perimeter security. However, protecting the perimeter is no longer enough. As enterprises increasingly rely on cloud storage, hybrid infrastructure, mobile devices, and third-party integrations, data moves freely across environments, far beyond traditional network boundaries. This has made data-centric security a strategic imperative.

Rather than focusing solely on where data resides or travels, data-centric security is about protecting the data itself—wherever it goes, however it’s used.

Why Data-Centric Security Matters

Modern threats, from ransomware and insider threats to misconfigurations and regulatory scrutiny, demand that security controls be tightly aligned with the sensitivity, context, and usage of data.

Key drivers of data-centric security include:

• Regulatory compliance (e.g., GDPR, CCPA, POPIA, HIPAA)

• Increased insider and supply chain threats

• Remote and hybrid workforce models

• Rising data breach costs and reputational risks

In short, protecting data at the point of creation and throughout its lifecycle is no longer optional—it’s foundational.

Encrypting Data at Rest and in Transit

Encryption is a core pillar of data-centric protection, ensuring that even if data is intercepted or stolen, it remains unreadable without proper authorization.

Data at Rest

• Includes stored files, databases, backups, or cloud storage.

• Encrypted using methods like AES-256 to prevent unauthorized access if the storage medium is compromised.

• Common implementations: Full disk encryption, database encryption, and cloud-native encryption services (e.g., AWS KMS, Azure Key Vault).

Data in Transit

• Refers to data moving across networks (e.g., emails, API traffic, file transfers).

• Secured using protocols like TLS (Transport Layer Security), VPN tunnels, and end-to-end encryption.

• Prevents eavesdropping, MITM (Man-in-the-Middle) attacks, and interception.

Best practice: Manage and rotate encryption keys securely. Consider hardware security modules (HSMs) for critical systems.

Classifying and Monitoring Access to Sensitive Data

Knowing what data you have, where it resides, and who accesses it is crucial to enforcing protection.

Data Classification

• Organizes data based on sensitivity and criticality (e.g., public, internal, confidential, regulated).

• Enables tailored security policies—higher protection for high-risk data.

• Helps fulfill compliance mandates around handling and retention.

Access Monitoring

• Tracks who accesses what data, when, from where, and how.

• Supports anomaly detection and real-time alerts for suspicious behavior.

• Enables auditability and forensic investigation in case of breaches.

Tools and techniques:

• Data discovery and classification engines (e.g., Microsoft Purview, Varonis)

• Access auditing and identity correlation via SIEM and UEBA

• Policies tied to data labels in platforms like Microsoft 365, Google Workspace

Using Data Loss Prevention (DLP) Tools

Data Loss Prevention (DLP) technology prevents sensitive information from leaving an organization’s boundary—accidentally or maliciously.

Core Capabilities:

• Detects and blocks sensitive data (e.g., PII, financial records, IP) from being shared via email, cloud apps, or removable media.

• Applies content inspection, pattern matching, and context-aware rules.

• Integrates with endpoints, email gateways, cloud platforms, and SaaS.

Types of DLP:

• Network DLP: Monitors data in transit over corporate networks.

• Endpoint DLP: Prevents data exfiltration via devices (USB, print, screen captures).

• Cloud DLP: Applies controls to data stored in or shared through cloud apps (e.g., Google Drive, Dropbox, Office 365).

Best practices:

• Start with identifying and tagging sensitive data.

• Implement DLP in “audit mode” before enforcing policies.

• Align DLP with user training and insider threat mitigation programs.

Extending Data-Centric Security: Additional Considerations

• Tokenization & Masking: Replace sensitive fields (e.g., credit card numbers) with surrogates for use in lower-trust environments.

• Rights Management (IRM/DRM): Control how data is accessed, shared, or printed even after it leaves your environment.

• Data Residency and Sovereignty Controls: Ensure data storage aligns with local laws and industry regulations.

• Zero Trust Data Security: Combine identity, device, and behavior-based signals to dynamically restrict access.

Shift the Focus to the Data Itself

Data-centric security marks a fundamental shift: rather than defending the environment, we protect the data directly. This model supports agile, scalable security strategies in a world where data is everywhere—and attackers are too.

By embedding protections such as encryption, classification, access monitoring, and DLP into the fabric of data handling, organizations can ensure compliance, resilience, and trust, regardless of where their data flows.