Data Breaches illustration

by Marc Dallas

Data breaches are a major cybersecurity threat. They affect organizations of all sizes and can lead to financial, legal, and reputational consequences.

To prevent data breaches, start by classifying sensitive data and limiting access with least-privilege. Encrypt data at rest and in transit, and enforce DLP to block unauthorized sharing. Anonymize or consistently mask non-production datasets used for testing and analytics. Combine these controls with monitoring and regular reviews to keep risks low.

This article provides a clear definition of data breaches, outlines the associated risks, shares the latest statistics, and presents preventive measures—with a particular focus on anonymization.

1. Understanding a data breach

A data breach is the unauthorized disclosure of sensitive information. It can be malicious or accidental, intentional or unintentional, internal or external. This includes:

  • Human error (sending a file to the wrong recipient)
  • Cyberattacks (phishing, malware, ransomware)
  • Insider behavior (malicious employee or human failure)

2. Types of data breaches

External breaches

Usually caused by cyberattacks, such as:

  • Phishing
  • Spyware
  • Ransomware and malware exploiting vulnerabilities

Internal breaches

Originating from within the organization:

  • Unauthorized access to sensitive data
  • Poor access rights management
  • Use of unsecured data in test or training environments

3. Recent statistics on cyberattacks

4. Average cost of a data breach

Losses include:

  • Business disruption
  • Loss of competitiveness
  • Consulting and remediation costs
  • Loss of customer trust and reputational damage

Data Anonymization Keys to a Successful Cross-functional Project

5. Measures to prevent data breaches

1. DLP (Data Loss Prevention)
Prevents sensitive data from leaving the organization.

2. Data classification
Protects data by assigning sensitivity levels and limiting access.

3. Least privilege policy
Ensures each user only accesses the data necessary for their job.

4. Encryption
Protects data by making it unusable without a key.

5. Anonymization
Replaces personal data with realistic, non-identifying equivalents. Discover DOT Anonymizer, Data Masking Tool.

6. Focus: Anonymization as a key prevention tool

What is anonymization?

According to the CNIL, anonymization involves applying a set of techniques that make it practically impossible to identify a person in an irreversible way.

Difference with pseudonymization

  • Pseudonymization: reversible, still subject to GDPR.
  • Anonymization: irreversible, excluded from the scope of GDPR.

Use cases

  • Software testing and development: provide coherent yet non-identifying data.
  • Training environments: simulate real scenarios without risk of leakage.
  • Outsourcing: deliver usable data without exposing personal information.
  • Business Intelligence: leverage data while preserving confidentiality.

Example: profile-based data access

  • An HR developer sees coherent, anonymized data.
  • An HR manager sees the actual data.

Anonymize your data with DOT Anonymizer

Conclusion

Data breaches are a daily reality with potentially severe consequences for businesses.

An effective strategy combines both technological and organizational measures. Anonymization, as a proactive solution, not only protects sensitive data but also relieves regulatory obligations—provided it is deeply embedded in all business processes.

Author

Asma Mabrouk

Marc Dallas

Business Line Manager Application Release Automation, ARCAD Software

Marc Dallas is Vice President of R&D at ARCAD Software. With over 25 years' experience in production operations and R&D leadership, Marc plays a key role in developing DevOps tools for IBM i, release management, and test data management.

FAQ

Start with data classification and least-privilege access, enforce MFA, encrypt data at rest and in transit, deploy DLP to block exfiltration, and anonymize non-production datasets used for testing and analytics.

Properly anonymized data is outside GDPR scope because individuals are no longer identifiable; pseudonymized data remains personal data and stays within GDPR.

Anonymization irreversibly removes the ability to identify individuals; pseudonymization replaces identifiers but remains reversible using separate information or keys.

Preserve schemas, distributions, and referential integrity with consistent masking; use format-preserving, tokenization, or differential techniques to keep realism while removing identifiers.

Data classification, least-privilege and role-based access, strong authentication, patching cadence, encryption, DLP, continuous monitoring, and secure handling of non-production data via anonymization or masking.

Use anonymization for analytics and testing when re-identification is not needed; choose pseudonymization when you must retain a way to re-link data under strict controls.