Data masking has become an essential data privacy and compliance technique in recent years. As cyber threats grow and regulations like GDPR and CCPA emphasize data protection, companies need smarter ways to secure sensitive customer data while still deriving business value from it.
This comprehensive guide provides everything you need to know about data masking in plain English – from what it is to techniques, tools, use cases and best practices.
What is Data Masking?
Data masking, also known as data obfuscation or data anonymization, refers to the process of hiding original sensitive data with fictional but realistic looking data. The goal is to ensure real data sets can be used for testing/analytics purposes without exposing confidential personal information.
For instance, a customer‘s name ‘Alice Meyer‘ may be masked as ‘Simone Bourne‘. The data remains structurally similar for application testing but the actual identity is protected.
So in essence, data masking limits data exposure to unauthorized users while maintaining its usability for analytics, machine learning and software testing.
Why is Data Masking Important?
With rising security breaches and stringent regulations around consumer data, it has become critical for organizations across industries to implement robust data protection controls.
Some key drivers making data masking ubiquitous include:
Preventing Data Breaches
- Over 41 billion records have been exposed through data breaches since 2013, according to Privacy Rights Clearinghouse. Masking reduces this risk by removing sensitive details that attackers usually target.
Enabling Test/Dev Environments
- Developers need to work on systems resembling production infrastructure. Data masking provides them access to data that looks real without exposing confidential customer details.
Analytics and Model Testing
- Data scientists develop models using actual datasets which may contain personal information. Masking techniques allow analytics on production data without violating privacy.
Regulatory Compliance
- Regulations like GDPR in the EU mandate reasonable data protection. Data masking is explicitly recommended as a compliance mechanism under these regs. Fines for non-compliance can be upto 4% of global revenue!
Clearly, data masking delivers the twin benefits of strengthening data security as well as enabling business productivity through test data and analytics – making it pivotal for modern data-driven organizations.
Next, let‘s understand how data masking actually works.
How Does Data Masking Work?
The data masking process involves mainly two steps:
1. Identifying Sensitive Data
Firstly, businesses need to discover and classify regulated data like personally identifiable information (PII), protected health information (PHI), financial data etc. across their systems.
Modern data masking tools use automated sensitive data discovery to find 100s of data types across structured as well as unstructured data sources.
2. Masking Sensitive Fields
Once sensitive information is identified, the next step is to apply masking algorithms to replace actual data with fictitious but realistic data.
For instance, names can be replaced by fake names from an existing database to retain format. DOBs can be shifted by a few days to retain date structure.
Let‘s see this in an example:
The masked data maintains privacy while still being suitable for downstream analytics, testing etc.
Multiple data masking techniques are available depending on your objectives which we‘ll cover next.
Data Masking Techniques
There are several techniques used for masking data to balance usability and security as per your needs:
Substitution
Replaces sensitive data with substitute fictional data from available datasets. Helps retain format and structure of fields.
Example: Replace real names and addresses with randomly chosen fake values
Shuffling
Swaps data values across records to break any link between data fields while keeping values realistic.
Example: Shuffle names across customer records
Number Variance
Applies mathematical variance while retaining format. Useful for fields like age, salary etc.
Example: Age values shifted by -3 to +3 years
Date Variance
Shifts date fields up or down randomly. Maintains date format.
Example: DOB values modified by +/- few days
Encryption
Encrypts data so only authorized users with keys can view underlying data.
Example: Encrypt last 4 digits of SSN using AES standards
Character Scrambling
Jumbles characters randomly to prevent reverse engineering but maintain text structure
Example: Phone numbers with digits scrambled
As visible, various options are available to mask data as per your needs in balance usability and security. Multiple techniques can be combined for smarter data masking.
Next, let‘s see how data masking differs from synthetic data generation.
Data Masking vs Synthetic Data
While data masking focuses on hiding original sensitive data, synthetic data generation creates completely artificial data statistically resembling real data.
Key Differences:
- Data Volume: Masking modifies real data while synthetic data is artificially generated
- Privacy Level: Synthetic data guarantees zero real data but masking has a minimal re-identification risk
- Compute Resources: Synthetic data uses more compute resources to generate data at scale
- Retention of Relationships: Data masking retains relational integrity in datasets better compared to synthetic data
In a nutshell:
- Data masking modifies real data to enable privacy
- Synthetic data creates artificial data from statistical models
So when should you use what?
When to use data masking
- Need to use production datasets with privacy enhancements enabled
- Want to retain relationships in existing data
- Have limited compute resources
When to use synthetic data
- Need fully artificial test data environment
- No risk of PII exposure is acceptable
- Do not need to retain raw data integrity
With this context of what data masking is and how it can help, let us now see what kind of data requires masking.
Types of Data that Require Masking
While all personal customer data requires privacy, regulations specially demand protection for certain kinds of confidential data including:
1. Personally Identifiable Information (PII)
PII could uniquely identify an individual either directly (like name) or indirectly (age, location etc.) Some examples of PII fields that may require masking include:
- Full name
- Home address
- Email address
- Credit card number
- Government IDs
- Photos/Videos showing individuals
2. Protected Health Information (PHI)
PHI comprises medical data that can be traced back to a person. Health records are specially safeguarded. For example:
- Treatment history
- Test results
- Prescriptions
- Insurance details
- Physical/mental condition
3. Personal Financial Information
With rising identity frauds, sensitive financial information is also crucial for masking including:
- Bank account numbers
- Annual income
- Investment portfolio
- Credit scores
- Debit/Credit card numbers
4. Company Confidential Data
Apart from third party personal data, companies also need to mask their own proprietary data before sharing externally:
- Trade secrets
- Product designs/formulations
- Patent details
- Source code
- Discount schemes
- Customer lists
With clarity on the kinds of data that need masking, let us know see how one of the most stringent regulations, GDPR, views it.
How GDPR Promotes Data Masking
The European Union‘s General Data Protection Regulation (GDPR) has strongly advocated the usage of data masking as an approved method for enabling data privacy.
GDPR explicitly uses terminologies around pseudonymization and encryption, both primary methods for data masking:
- Article 25 recommends using pseudonymization, a form of masking, to protect privacy
- Article 32 mandates using encryption or pseudonymization as safeguards
- Article 40 encourages anonymization via techniques like data masking
By using data masking techniques, companies can reap multiple benefits:
- Reduce GDPR compliance risk significantly
- Process customer data securely after pseudonymization
- Build effective software testing and analytics environments
Hence data masking forms a pivotal GDPR compliance mechanism for regulating use of private EU citizen data.
With this context on data masking, let us look at some real-world examples where data masking delivered value.
Data Masking Case Studies
1. Samsung Protects User Data to Enable Analytics
The consumer electronics giant analyzes usage data from millions of devices like mobile phones and TVs to gain insights. However, the raw data contained identifiable customer details.
By leveraging Dataguise’s masking solution, Samsung automatically discovered and masked PII data across their Hadoop environment without interrupting planned analytics initiatives – best of both worlds!
Outcomes
✔ Compliance with privacy regulations assured
✔ Analytics use cases like product development powered by data continued seamlessly
2. Bank Masks Financial Data to Modernize IT Systems
A leading bank with over $200 Bn in assets needed to upgrade their IT systems which required replicating customer data across dev, test and simulation environments.
They used Delphix Dynamic Data Platform to automatically mask and create 23 TB of masked data in just 5 days! This enabled accelerating modernization projects while also staying compliant.
Outcomes
✔ 85-90% faster access to masked data for projects
✔ Significantly reduced risk of unauthorized data exposure
There are 100s of such examples across industries like healthcare, retail, insurance showing how data masking fuels innovation via analytics while also enhancing security and compliance – clearly a win-win!
These real-world masking successes lead us to an important related question – what are some data masking best practices?
Data Masking Best Practices
While data masking offers multiple advantages, you need a thoughtful approach to maximize benefits and minimize risks. Here are 8 top data masking best practices:
1. Discover all sensitive data
Comprehensively scan distributed environments to identify regulated and confidential data across platforms like Hadoop, relational databases etc. Automated tools make this faster and more accurate.
2. Assess downstream usage
Understand how masked data needs to flow downstream for usage – analytics, testing etc. This governs choice of optimal masking techniques.
3. Balance usability vs security
Match masking methods to business objectives. Overmasking can negatively impact utility while undermasking leaves risk.
4. Automate masking processes
Manual masking huge datasets is infeasible and risky. Use automated workflows for scalable and consistent data masking.
5. Dynamically mask data on demand
For some use cases like analytics, dynamically mask data at runtime per user. This separates masking processes from actual usage.
6. Maintain centralized control
Manage masking centrally even across distributed data to enable unified visibility, control and auditing.
7. Audit masking activities
Proper access controls, logging and audits are crucial to monitor masking systems and assure they operate as intended.
8. Test masked data adequacy
Empirically test masked datasets by trying to re-identify individuals. Fix masking issues thereby strengthening privacy protection.
Adhering to these leading practices will enable harnessing the power of data masking effectively and safely.
Now that you understand all about its techniques, use cases, regulations and best practices, let‘s look at the Gartner Magic Quadrant for Data Masking tools.
Gartner Magic Quadrant for Data Masking
Gartner systematically evaluates all major data masking vendors using criteria around capabilities and business impact.
Leaders: Delphix, Informatica, Mentis
Challengers: IBM, Oracle, Solix
Visionaries: Imperva Camouflage
Niche Players: CA Test Data Manager, Dataguise, VMware
While Informatica and Delphix lead in both vision and execution as per 2022 Gartner MQ, you need to consider factors like scalability, automation, centralization etc. as per your environment.
For instance, Delphix offers a dynamic data platform built from scratch for large scale dynamic data programs – which sets it apart!
Data Masking Market Outlook
As per MarketsandMarkets, the global data masking market is projected to grow from $786 million in 2022 to $2,049 million by 2026 at a CAGR of 21%.
Key factors fueling high growth expected in coming years:
- Expanding regulations necessitating data security protections
- Increasing adoption of cloud across sectors leading to larger attack surfaces
- Rising importance of analytics, AI/ML driving need for test data environments
- Innovation in data masking powering automation and ease of use
Companies not investing in data masking risk facing serious cyber threats, fines as well as inability to leverage data – making it imperative!
Final Thoughts
To summarize, data masking or data obfuscation provides pseudonymized but realistic test datasets that preserve utility while enhancing compliance and security – clearly a pivotal technique today!
With game changing regulations like GDPR already here and emerging ones like CPRA on the anvil, robust data lifecycle controls like masking are a must have investment!
Leading analyst firms also forecast the masking market booming to $2B+ given surging cybercrime as well as innovation in smarter approaches.
While native capabilities in databases can help, dedicated agile masking platforms to centrally manage masking workflows are recommended for enterprise needs today given complex landscape. As the sample and research highlighted, masking is truly coming off age in enabling business applications securely while putting customer trust first!
