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data security

6 min read

Practical Framework for Enterprise Data Security

Secure your data infrastructure. Learn essential strategies to protect sensitive info, prevent breaches, and ensure compliance in this comprehensive guide.

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Sara Codarlupo

Marketing Specialist @Gigantics

Data security is an organization’s ability to control what data exists, where it resides, who accesses it, how it is used, and how it is deleted—all while maintaining operational continuity and meeting regulatory obligations. In enterprise environments with multiple platforms, teams, and vendors, risk is rarely concentrated in a single system; it emerges in data flows, dataset replication, and permissions that accumulate over time.



This article presents an applicable approach to designing and operating data security with verifiable criteria: governance, technical control, third-party management, operational traceability, and metrics for prioritization.




What Is Data Security?



Data security brings together policies, processes, and technical controls that protect the confidentiality, integrity, and availability of information throughout its lifecycle. It includes:


  • Prevention of unauthorized access.

  • Reduction of sensitive information exposure.

  • Control of movements between environments and platforms.

  • Detection and response to incidents.

  • Operational logging for auditing and compliance.



When security is limited to perimeter or infrastructure controls, data-centric risks persist: excessive access, uncontrolled replicated datasets, or transfers to third parties without equivalent technical conditions.




Why It Matters in Multi-Environment and Multi-Vendor Organizations



Data is an operational asset: operations, analytics, product, risk, compliance, and customer service depend on its availability. At the same time, it concentrates exposure: PII, financial information, operational secrets, and intellectual property.


In internal evaluations, risk increases predictably with complexity:


  • More systems containing data.

  • More integrations.

  • More non-production environments (analytics, UAT, support, sandboxes).

  • More identities with access.

  • More connected providers.


Data security reduces this exposure without blocking operations: by controlling access, limiting replication, applying treatment policies where appropriate, and maintaining an operational record.



Main Risk Vectors



Unauthorized Access and Excessive Privileges



Broad permissions, inherited access, shared credentials, or the absence of periodic reviews create exposure and make incident containment difficult.



Data Replication Outside of Production



Exposure grows when datasets are copied to sandboxes, analytics, UAT, support, integrations, or provider environments. Control is not just about "where the data is," but which dataset is circulating, with what treatment, and for how long.



Third Parties and ICT Supply Chain



When data is consumed on external platforms (cloud, managed services, consulting, tooling), the risk shifts. Without technical exit conditions, traceability, and expiration, the organization loses operational control.



Configuration Errors and Accidental Exposure



Public buckets, snapshots, exports, logs containing sensitive data, copies in collaborative tools, or poor segmentation.



AI and LLM Usage Risks



The use of assistants and models can introduce exposure via prompts, connectors, training datasets, operational memories, or indirect access. This vector is addressed in more detail in LLM security.



For a practical view of recurring failure patterns, see our overview of data security challenges and the controls teams use to reduce risk.




Data Security by Design as an Implementation Approach



Controls work when they are incorporated from the architecture and delivery stages, not when added at the end. A security-by-design approach typically includes:


  1. Data classification and domain-based requirements.
  2. Minimum access controls and segregation.
  3. Transformation of sensitive information for non-production use when applicable.
  4. Logging of decisions and exceptions.
  5. Automatable tests and validations (integrity, leaks, permissions).

This reduces reliance on manual reviews and avoids compensating with broad permissions or unnecessary replication.




Data Security Operational Framework


Phase Key Activities Inputs / Considerations Results / Metrics
1. Discovery & Mapping Inventory repositories and data flows; identify exports and replication between environments; locate access to critical datasets (internal and third-party). Available catalog and metadata, data architecture, integrations, consuming tools, vendor inventory. Prioritized inventory, flow maps, high-exposure destinations, access baseline (users, roles, permissions).
2. Classification & Policies Define sensitivity taxonomy, access policies (RBAC/ABAC), treatment rules per category (masking, pseudonymization, anonymization, or randomization), and retention/expiration criteria. Regulatory obligations, risk appetite, domain needs, integrity criteria for usage (formats, relationships, consistency). Versioned policies, treatment matrix, exception criteria, initial KPIs (classification coverage, % of datasets under policy).
3. Technical Controls Apply encryption and key management, segregation and least privilege, data treatment where applicable, automated validations (referential integrity, leaks), and per-execution logging. Platforms (databases, warehouse/lake, SaaS), identity model, logging standards, audit requirements, domain patterns. Exposure reduction, effective least privilege, executed validations, operational traceability per execution.
4. Third Parties & Extended Environments Classify external destinations, define egress conditions (what is shared and under what treatment), establish validity/expiration, verifiable revocation, and vendor-linked traceability. ICT vendor inventory, service purposes, locations/regions, contractual controls, IAM and logging integration. Operational record of shared datasets, applied expiration, reduction of exceptions, dataset-to-vendor-to-validity reconstruction capability.
5. Operation & Continuous Improvement Monitor access and exports, detect anomalies, execute containment and revocation procedures, review permissions/exceptions, and adjust policies using KPIs. Logs (IAM, data platforms, pipelines), SOC alerts, audit findings, incidents and lessons learned, changes in architecture and vendors. Revocation/withdrawal MTTR, reduction of excessive permissions, replication control, sustained compliance with consistent records.



Compliance and Regulatory Requirements



Data security compliance requires demonstrating operational control and policy consistency throughout the data lifecycle.


  • NIS2: In Spain and Europe, it increases expectations for risk management, incident response, and third-party governance, directly impacting how datasets and access are managed on internal and external platforms.

  • ENS (National Security Framework): For public administrations and related organizations, it reinforces requirements for access control, traceability, and system management.

  • DORA: In the financial and insurance sectors, this regulation adds a focus on digital operational resilience and raises the bar for ICT third parties and external platforms.




DSP vs. DSPM: Clarification for Architecture Decisions



When selecting technology, two layers that solve different problems are often mixed:


  1. Capabilities focused on operational data control: Reducing exposure by applying policies, limiting propagation, and managing expiration.
  2. Capabilities focused on posture and monitoring: Discovering where data is, detecting risky configurations, and prioritizing findings.

This distinction is relevant because it shapes the architecture:


  • A data control-centric approach acts on the dataset lifecycle: it defines treatment rules, manages exceptions, applies expiration, and maintains traceability per execution, especially involving non-production environments or third-party ICT.

  • A posture approach helps gain visibility and prioritization: inventory, exposure, permissions, and risk signals, making it useful for guiding remediation and auditing.



In multi-team, multi-platform organizations, both complement each other: posture identifies exposure surfaces, while data control systematically reduces risk in the flows where datasets are created, distributed, and consumed. This distinction is explored further in DSP vs. DSPM.




Metrics to Prioritize, Measure, and Sustain the Program



A data security program is managed with metrics linked to risk and operation:


  • Percentage of domains classified and with active policies.

  • Number of destinations with sensitive data (internal and third-party).

  • Mean Time to Revoke (MTTR) access and withdraw datasets.

  • Active exceptions and their average duration.

  • Coverage of mandatory treatment in non-production and external destinations.

  • Findings related to excessive permissions and remediation time.




How Gigantics Fits into a Data Security Program



Gigantics integrates as an operational control layer to deliver datasets with consistent policies to internal and third-party environments, reducing sensitive information exposure without losing the properties necessary for use.


  • Versioned policies for masking, pseudonymization, anonymization, or randomization by data domain.

  • Integrity for use: Preservation of formats, consistency between fields and relationships, including referential integrity where applicable.

  • Traceability per execution: A record of which dataset was delivered, to which environment or third party, which rules were applied, and its validity/expiration date.


Control Sensitive Data in Any Environment

Standardize transformation policies and per-execution logging to reduce exposure, manage third parties, and sustain audits without slowing down your teams.

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FAQ: Data Security



1. What are the 5 pillars of data security?



Confidentiality, integrity, availability, authenticity, and accountability. Together, they ensure data is accurate, protected, accessible, and traceable across its lifecycle.



2. What are the main methods used to secure data?



Encryption, access controls, data masking, anonymization, tokenization, and continuous monitoring are core techniques to reduce risk and ensure compliance.



3. How can you ensure your data is secure?



By classifying data, applying encryption and least-privilege access, automating policies in pipelines, monitoring anomalies, and preparing strong incident response plans.



4. What is the safest method to store data?



Encrypted, access-controlled storage with redundant backups and secure lifecycle management provides the highest protection and resilience against breaches.



5. What are the major threats to data security today?



Ransomware, insider misuse, cloud misconfigurations, third-party risks, and AI-powered attacks are the most pressing threats to enterprise data security.



6. What is the role of data security in regulatory compliance?



It enforces GDPR, HIPAA, and NIS2 requirements by applying encryption, access control, auditing, and breach notification measures to safeguard sensitive data.



7. How does data security support business resilience?



Strong security avoids downtime, fines, and reputational damage, while enabling trust with customers and partners, turning compliance into competitive advantage.