Data Masking For ISO 27001 2022 Compliance
- Kamalika Majumder | DevOps & ISO 27001 Consultant
- Aug 18
- 6 min read
Few years back, one of my clients had to migrate their Fintech infra from AWS to an on-premises datacenter. Reason: PII Data localisation and privacy for OJK Indonesia compliance.
During that process there was an active debate on whether or not to migrate logging and monitoring systems, which was a managed SaaS hosted on cloud outside Indonesia.
While the devs thought “why not, it's just logs and alerts”, the Security team had a different observation, “the logs contained various transaction related information which was regarded as sensitive PII data”.
Their verdict was simple: sanitise your logs and obfuscate all PII data, then you can keep in the cloud, else migrate on-premise.
The new ISO 27001:2022 has introduced 11 new controls, one of which says:
“Data masking shall be used in accordance with the organisation’s topic-specific policy on access control and other related topic-specific policies, and business requirements, taking applicable legislation into consideration.”
Today, where data breaches are not a matter of “if” but “when,” organisations must go beyond conventional data protection mechanisms. And good news is that traditional compliances have also started recognising that. One such crucial strategy is data masking — a technique that obscures sensitive information, making it unreadable to unauthorised users while maintaining the data's utility for testing, analytics, or training purposes.
Data masking has become more than just a best practice; it is a critical control in safeguarding Personally Identifiable Information (PII), financial records, and confidential business data.
As organisations increasingly pursue compliance with ISO/IEC 27001:2022, the international standard for Information Security Management Systems (ISMS), data masking plays a pivotal role in demonstrating a proactive approach to data security.
What is Data Masking?
Data masking, also known as data obfuscation or anonymisation, is the process of transforming sensitive data into a format that is unreadable or unusable by unauthorised personnel.
Unlike encryption, which requires a key to decrypt the information, masked data is permanently altered, preventing re-identification unless a specific mapping or transformation logic is retained and protected.
For example, in a masked database used for testing, real customer names and credit card details are replaced with dummy names and randomly generated card numbers that follow the same pattern.
Why did ISO 27001 include Data Masking?
The inclusion of Data Masking (A.8.11) acknowledges a crucial evolution in the way data is handled across systems and applications. With an increasing number of data breaches originating from non-production environments, test systems, and internal misuse, the standard now recognises the need for proactive measures to prevent inadvertent exposure of Personally Identifiable Information (PII) or other sensitive data.
This new control complements existing privacy frameworks such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and others, by enabling technical enforcement of data minimisation and privacy by design.
What Does This Mean for Organisations?
1. Reinforced Data Privacy Posture
With the addition of A.8.11, ISO 27001-certified organisations are expected to take extra measures to de-identify sensitive data wherever full data fidelity is unnecessary. This reduces risk and supports compliance with data protection regulations.
Organisations must now consider not only access controls and encryption but also whether data is even needed in its raw form across various use cases—particularly for development, testing, or analytics.
2. Reduced Exposure in Non-Production Environments
Developers, QA testers, and data scientists often work in non-production environments where traditional perimeter defences are weaker. Data masking reduces the risk of these environments becoming weak links in the data lifecycle. By defaulting to masked datasets for testing, organisations lower the likelihood of internal threats or accidental leaks.
3. Shift Toward Privacy Engineering and Governance
Implementing data masking is not just a technical task—it requires data governance policies, classification of sensitive data, and role-based access management. It reinforces the need for structured data mapping, classification frameworks, and the alignment of privacy and security teams.
4. Pressure to Update Toolchains and Processes
For many organisations, implementing data masking will expose gaps in tooling and process maturity. Legacy systems may not support automated masking or may store sensitive data in unstructured formats. Integrating masking tools with modern DevOps pipelines, databases, and analytics platforms is now a required part of maturing the ISMS.
Implementing Data Masking Effectively
To achieve effective data masking that aligns with ISO 27001 and general security goals, organisations should:
Identify Sensitive Data Assets: Classify data across systems, databases, and applications to determine what needs masking.
Define Masking Policies: Based on roles, access levels, and usage contexts.
Automate Masking Processes: Use specialised tools to integrate masking into CI/CD pipelines and data movement processes.
Audit and Monitor Usage: Log access to masked and unmasked data for audit trails.
Train Staff: Ensure developers, testers, and data engineers understand when and how to use masked data responsibly.
Role of Encryption for data masking:
While data masking permanently alters data to obscure its original form, encryption is often used in parallel with masking to strengthen protection — especially for data at rest and data in transit.
🔐 Data at Rest
In environments like databases or backup storage, encryption ensures that even if masked data is stored, any associated mappings, transformation rules, or reference tables (used for reversible masking in some systems) are encrypted. This prevents an attacker from reverse-engineering masked data by accessing backend configurations or tooling.
Example: A masked database copy used in testing is stored on an encrypted volume, ensuring both the data and metadata are secure.
ISO 27001:2022 supports this under Control A.8.23 – Information Security for Use of Cloud Services, emphasising encryption for stored data.
🔐 Data in Transit
When masked data is being transferred between systems (e.g., from a masked data store to a dev server), encryption ensures that the data is protected during transmission from eavesdropping or man-in-the-middle attacks.
Protocols: TLS, IPSec, and VPNs are commonly used to secure data in motion.
ISO 27001:2022 addresses this in Control A.8.24 – Secure System Engineering Principles, requiring secure communication channels for data flow.
🤝 How Encryption Complements Masking
Masking reduces exposure by transforming data into non-sensitive forms.
Encryption ensures that even if data is intercepted or stolen, it cannot be read or used without decryption keys.
Together, they provide defence in depth: masking mitigates business logic risks, while encryption protects against infrastructure-level threats.
Why is Data Masking Important in Security?
The importance of data masking stems from its ability to:
Prevent Data Breaches in Non-Production Environments: Development, testing, and training systems often mirror production environments but lack the same security controls. Masking ensures sensitive data is not exposed in these lower-tier environments.
Support Least Privilege and Zero Trust Models: By delivering masked data to unauthorised or lower-privileged users, organisations reduce the attack surface and comply with the “need-to-know” principle.
Minimise Insider Threats: Not all threats are external. Data masking limits internal access to real data, reducing risk from employees or contractors with excessive access.
Enable Secure Outsourcing and Vendor Collaboration: When third parties need access to systems, masking sensitive fields (such as customer names or account numbers) allows business continuity without violating data protection norms.
Meet Data Protection Regulations: Data masking supports compliance with global regulations like GDPR, HIPAA, and PCI DSS, all of which mandate the protection of sensitive or personally identifiable data.
Conclusion:
The introduction of Data Masking in ISO/IEC 27001:2022 represents a critical shift toward embedding data-centric security into the fabric of information security management. It signals to organisations that protecting data doesn’t stop at encryption or access control—it extends to how data is used, shared, and replicated internally.
Organisations that implement data masking thoughtfully will not only improve their ISO 27001 compliance but also build a privacy-aware culture, reduce insider threat risks, and enhance trust with stakeholders and customers. In a world where data is both an asset and a liability, masking is no longer optional—it’s foundational.
As threats evolve and regulatory demands become more stringent, data masking is a fundamental element in protecting privacy, maintaining trust, and achieving long-term compliance with global standards like ISO 27001.
Need to get ISO 27001 or SOC2 Compliant, but not sure where to start!
Thanks & Regards
