In the modern era of big data and advanced analytics, organizations collect and store more sensitive information. From identifiable information (PII) like names, addresses, and social security numbers. It leads to protected health or financial data, ensuring the security of this information is a top priority. But, organizations also need to use this data for essential business functions. It includes testing new applications, developing AI models, conducting analytics, and more.
This introduces a critical challenge - how can organizations get the most value from their data. Especially, while also protecting sensitive information? Enter data masking.
Data masking hides real data. It swaps in fake but realistic data instead. This lets groups share more data safely inside and outside the company.
As data and risks grow, smart data masking strategies are key to protecting data. In fact, hacked personal data causes the most costs from data breaches. Just in 2021, the average breach cost a shocking $4.24 million (BMC Blogs). With so much at stake, better data masking practices are vital.
Comprehensive Identification Of Sensitive Data
The first step in any data masking plan is finding all sensitive data. Groups need to locate where personal, financial, health, and other private data lives in their huge data systems.
Comprehensive data discovery uses automated tools and manual reviews. On the tech side, data classification tools can scan databases to detect, label, and catalog sensitive data based on patterns, formats, and metadata. These give a wide view of sensitive data across the group's data.
Manual checking complements the tech tools. Security teams do deep inspections to find details the tech may miss. Traditional data profiling also exposes the extent of sensitive data.
Together, automated classification and human audits fully map all sensitive data locations accurately. This lays the groundwork for picking the right data masking approaches across the group.
Selection of Data Masking Techniques
With sensitive data mapped out, security leaders can choose the best data masking techniques for their data types and uses. Data masking options include:
- Substitution – Swaps real data for fake but believable data randomly generated or from predefined lists and rules. Often used for names, addresses, card numbers.
- Shuffling – Jumbles up data by randomly reordering records or chunks of data. Helps dissociate people from records without distorting analysis.
- Generation – Makes up plausible fake data using algorithms instead of real data. Maintains consistency across systems.
- Encryption – Encodes data so only approved users can see the real values. Retains analytical value.
- Hashing – Converts data into fixed-length results using math formulas. Provides security but supports analysis.
- Redaction – Permanently omits sensitive data via blacking-out, deletion, or NULL values. Best for external sharing but impacts analysis.
Groups weigh the good and bad of each method based on how sensitive the data is, how it will be used, legal issues, and security rules. While static, rules-based masking was common, dynamic masking that changes per user is now popular.
Best Practices for Data Masking Implementation
Implementing effective data masking presents many challenges for organizations. Data environments are complex, regulations are stringent, and business needs are pressing. By Implementing data masking best Practices which help to reach their full potential when implemented with precision and aligned with established best practices, they were
- Mask data directly in live sources rather than in copies. This ensures downstream systems only get masked data.
- Maintain consistency across systems. Use robust algorithms and prevent logic gaps.
- Limit access to masking tools. Carefully control who can view and change masking settings.
- Mask sensitive data early in the data lifecycle, ideally right in source systems if possible.
- Build masking into DevOps release cycles for continuous data pipelines.
- Mask in small batches to minimize business disruption. Prioritize high-risk data first.
- Document all masking details - when, where, and how it was done.
- Regularly audit masking to confirm it works and complies with rules. Check new data flows.
With strong techniques and disciplined processes, groups can get great value from data while protecting privacy.
Review of Top Data Masking Tools
Now that we've covered best practices, let's look at leading commercial and open source tools to enable them:
Leading Commercial Data Masking Solutions
- Informatica - Centralized, scalable masking. Great for complex data consistency.
- Delphix - Specializes in DevOps and cloud data masking. Links with CI/CD workflows.
- Mentis - Prioritizes usability for enterprise needs. User-friendly workflows and automation.
- IBM InfoSphere - Leverages extensive privacy expertise. Broad data source connections.
- Solix - Emphasizes big data scale across data lakes, Hadoop, and the cloud.
Prominent Open Source Data Masking Options
- PostgreSQL Anonymizer - Data masking functions built into PostgreSQL database.
- ARX - Java library suitable for large masking projects. Community open source project.
- Masquerade - Focused CSV data masking tool supporting many data types, locales, and outputs.
Each solution balances capabilities, performance, usability, and total cost based on needs and systems.
Case Study: Big Retailer Masks Data For Analytics
A top retailer needed sales transaction data for analytics without exposing customer details. Using Informatica masking integrated with its data quality tools, they tailored sophisticated masking to each data element. Personal info like names and addresses used substitution while purchase amounts used shuffled masking. This let them analyze broad internal data instead of a limited real data sample.
Complying with Data Protection Regulations
Data masking isn't just good security - it's also required to comply with expanding privacy regulations like:
- GDPR - The EU law mandates masking to protect data. GDPR pseudonymization is done through masking.
- CCPA - California's privacy law also codifies masking obligations for resident data security.
- HIPAA - Healthcare groups must adhere to masking rules under HIPAA to avoid fines and reputation damage.
- PCI DSS - All companies handling credit cards have to mask cardholder data as required.
Masking provides the technical controls and audit trails mandated by these and other emerging laws. Ongoing governance maintains compliance as regulations grow.
Maintaining Data Utility After Masking
Many worry masking will hurt data quality, analysis, and utility for downstream use cases. But mature masking approaches preserve maximum utility and accuracy.
- Algorithmic masking to keep statistical distributions and validity
- Maintaining relationships, logic, and integrity between elements
- Preserving utility for required downstream needs before masking
- Limiting full redaction to retain some utility
- Deterministic masking using trusted seeds for repeatable results
- Having data stewards measure utility before and after masking
With careful planning and proper techniques, data utility and analytical truth can largely be retained, even for masked sensitive data. The goal is the right balance between security and functionality.
Monitoring And Updating Data Masking
Data masking requires ongoing oversight and updates to handle new data flows, emerging threats, and legal shifts. Groups should monitor:
- New data needing classification and potential masking
- Changing use patterns that impact utility requirements
- Masking gaps exposing data due to poor implementations
- New regulations requiring more data protections
- Advances in data science that could re-identify masked data
This means improving masking approaches, boosting classification, and adopting adaptive masking to tailor protection dynamically to risks. Only through continuous enhancement can masking provide lasting security and compliance.
Data masking has become essential for getting value from data while protecting privacy. As leaders build data strategies focused on security, solid masking techniques thoughtfully implemented will be the foundation.
This means using data discovery to find where sensitive data resides, selecting advanced masking methods tailored to each data type, hardening data systems based on proven best practices, and actively governing masking to uphold compliance.
With more at stake than ever before, organizations can leverage data masking and
supporting tools to drive innovation from data while fulfilling their duty to customers and stakeholders. A new age of privacy protection starts now through these data masking best practices.
What Are Common Data Masking Mistakes And How Can We Avoid Them?
Common pitfalls and fixes:
- Overmasking - Masking more data than needed reduces utility. Base masking on measured risk and uses.
- Undermasking - Not enough masking leaves data vulnerable. Regularly review regulations and data flows.
- Poor consistency - Inconsistent masking distorts analytics. Use algorithms to maintain integrity across systems.
- No audit trails - Lack of documentation hurts compliance and controls. Record all masking details and changes.
How Does Data Masking Impact Data And Analytics?
Thoughtful masking minimizes impact through algorithmic approaches, preserved logic, and limited full redaction. Only masking the bare minimum data elements retains overall utility. Data stewards can measure utility before and after masking and fine-tune as needed. Some stats and visuals may vary slightly but overall directional consistency enables good decision making.
Can Data Masking Be Reversed, And If So, In What Cases?
Data masking can technically be reversed under highly controlled conditions for legitimate purposes like law enforcement requests or audits. This requires strict access controls and storing masking logic/keys separathttps://onlinenewsbuzz.com/worlds-quickest-vpn/ely from masked data. We must carefully weigh data utility versus security risks. In practice, most groups treat masking as permanent.