When a company sends files to clients, partners, or analysts and leaves contact data in them, it faces a high risk of data breaches. If files are shared on public repositories without access restrictions, exploitation is almost guaranteed. Let’s look closer at ways to mitigate these risks.
Why Clean File Data
Overall, sharing uncleaned files carries severe breach risks, especially when the customer contact data belongs to a financial, medical, or security company. Such an incident can lead to the following types of consequences:
- Reputational damage. News that a company has leaked its clients’ contacts can severely shrink its customer base. A Vectra survey reports that 66% of customers prefer to end their relationship with a compromised company.
- Legal liability. Transferring raw data directly violates the law in many jurisdictions. The EU imposes heavy fines for GDPR infractions. In the USA, the most severe consequence can come from FTC interventions.
- Trade secret leakage. Building client bases takes significant time and requires huge financial investment. An uncleaned file can give competitors free access to a ready-made database, allowing them to poach the company’s customers.
- Vulnerabilities because of the human factor. Every organization must account for force majeure events, employee negligence, and intentional malicious actions. In such cases, properly cleaned data ensures that the file remains useless in strangers’ hands.
These factors lead to massive losses. IBM estimated that the average data breach costs a company $5M in 2026. IT giants like Facebook, Amazon, and X repeatedly pay tens of millions in fines for data leaks. Even a small American business owner with only ten regular clients can face a fine of about $3,000 to $7,000 per compromised person.
Difference Between Data Stripping and Masking
Stripping and masking are the primary data cleaning methods. They share the same goal: protect data by hiding it, but they use different principles. Compare them in the table:
| Feature | Stripping | Masking |
| Definition | Complete data removal from the file without a trace | Partial or complete data replacement while keeping the database size and structure |
| Approach | Remove contact data entirely | Preserve data for analysis but keep the information hidden |
| Document structure | Altered; columns and rows are often deleted | Unchanged |
| Processing reversibility | Deleted data is lost | Masked data stays in the file and sometimes can be recovered using a token or key |
| Security level | Maximum possible | High |
| Data value | Reduced; some data is missing | Stable |
| Processing program security | Lower for standard macros and formulas | Stable |
| Database size | Decreases | Stable |
In most cases, a company combines both, depending on the situation, version, and recipient. The goal is to balance data security for the company and data utility for its partners.
Stripping-Only Contact Data Types
To achieve the highest level of security, a company must delete anything the client or partner doesn’t need. However, there are several contact data categories that pose a direct threat to company security and must be removed in any case:
- Direct identifiers of company managers attached to certain clients, particularly email addresses and phone numbers.
- Precise coordinates and addresses of company offices and facilities serving certain customers.
- Messenger IDs, virtual phone numbers, and IP addresses of customer service managers.
Thoroughly cleaning these three types significantly reduces the risk of social-engineering and targeted phishing data leaks.
What Contact Data Types Can Be Masked?
Some sensitive contact details must not leave company servers, but they are structurally or semantically necessary for accurate automated data processing. In these cases, companies apply masking. It is mostly required for the following data types in a client contact database:
- Names and logins – replacement with pseudonyms.
- Phone numbers, emails, personal account pages – partial replacement with special or random characters.
- Geographical data – replacement of exact addresses or IPs with regions.
After such changes, the data loses its practical utility for attackers. At the same time, the data structure remains unchanged and processing algorithms can use it without additional risk.
Masking Techniques
The choice of a specific masking technique depends on whether you need to restore the original data in the future and on the restoration techniques available. The most practical ones include:
- Scrambling. The system randomly shuffles values within the same database columns, breaking the semantic links between different values within one row. Such databases become useless for fraudsters but can still suit analysts in some cases.
- Partial character masking. This irreversible technique is frequently used for contact details. They are replaced with some predetermined characters (like *) or random symbols, but the system keeps the same row format and length.
- Pseudonymization. The optimal solution for software testing and statistical analytics. The system replaces the actual identifiers with fake values while keeping the same basic features as the original cells.
- Tokenization. This technique is commonly used for bank cards, but it also applies to VIP or secret contacts. The system replaces the data with a generated token and stores the “token-data” connection in a secure vault.
- Hashing. The data is encoded into irreversible unique strings using special algorithms like SHA-256. Analytical systems can compare hashes and use them to search for unmasked data.
At least one of these techniques works for the absolute majority of cases. However, compliance with the company’s analytical algorithms must remain the main selection factor.
Automated Data Cleaning Tools and Additional Security Solutions
The market offers numerous solutions with varying complexity. You can apply them depending on the database’s size and type. Most automated tools belong to one of the following types:
- Stored procedures and UDFs. They are embedded directly in databases. They automatically process data during regular operations like exporting.
- Regular expressions and formulas. These are small predefined text patterns, most often in SQL, used in the code for processing databases. They allow data to be masked or deleted while saving a current version or creating copies for third-party users.
- AI-powered data detectors. These flexible models are embedded into advanced databases or integrated separately. They scan contact data independently, apply or suggest specific actions based on predefined patterns, and automatically adapt to changes in the database structure.
- Integrated software suites. Data masking and test data management systems handle databases of any volume. They can automatically generate a masked copy with the structure preserved for third parties.
For added security, companies often implement data cleaning solutions alongside other services and applications. For example, a virtual temporary number can receive a verification code on an exchange platform so an analyst can send a database file. This way, a real company contact doesn’t reach the exchange’s own database.
Widespread Data Cleaning Errors
Data pipeline developers often make the same crucial mistakes. These errors can lead to personal data compromise, analytical model disruption, and severe vulnerabilities that can be exploited by attackers:
- Weak masking. Overly simple replacement algorithms make data easier to decode, especially when attackers can cross-reference it with other data. Use only advanced hashing or another masking method that makes reverse-decoding impossible.
- Overlooking hidden data layers. Cleaning scripts often ignore technical information, such as system logs, history files, and backups, which can also hint at some types of contact information. To fix this, integrate cleaning solutions directly into the pipeline and make them the default for regular users.
- Disrupting structural integrity. Deleting identifiers that maintain the database semantics breaks analytical scripts. The solution is to mask the identifiers or to use the specialized TDM that preserves the database structure.
- Ignoring automation. Manual processing can work for a database with a few dozen rows, but even there the risk of human error is high. Failure is almost guaranteed for thousands and tens of thousands of rows. Moreover, this approach requires dozens of times more working hours than automated tools. If dedicated software is unavailable, you have to at least use built-in database functions.
Avoiding these mistakes makes the data-cleaning process more predictable and reliable.