Creating your own anonymization tool: good idea or strategic mistake?

Developing your own anonymizer may seem appealing... but in most situations, this is a miscalculation. This choice involves hidden costs, rare expertise, risks of non-compliance, heavy maintenance, and schedule slippage.

The pressure surrounding data protection has never been greater. Between regulatory requirements, the rise of cyber threats, the proliferation of test environments, and the growth of generative AI requiring anonymized datasets, organizations are faced with a dilemma: should they invest in an established anonymization solution or develop their own tool in-house?

On paper, the "homemade" option seems attractive: greater control, more flexibility, a tool perfectly suited to the specificities of the business... But what happens once the project is launched? Is it a strategic choice... or a costly trap that many regret?

This article provides a comprehensive overview to help you decide.

Many organizations start by considering the in-house option. And that's understandable. The arguments put forward are often the same:

A tool developed in-house seems to allow for the precise integration of business constraints, transformation rules, and specific cases unique to the company.

Many people imagine anonymization as simply masking data: replacing names with asterisks, truncating addresses, or generating fictitious values. An SQL script, an ETL, or a piece of Python code... and you're done.

An internal tool suggests that it will be possible to modify or extend the rules easily, without depending on a supplier.

Building it yourself is seen as a way to avoid software investment. "We already have the developers, it will cost us almost nothing." On paper, it's hard to argue with this logic. In reality... things quickly get complicated.

Creating a robust anonymizer is a much bigger undertaking than it seems. Teams that embark on this project usually discover these obstacles after several months, sometimes too late to turn back.

Here are the main challenges that are often overlooked:

Before anonymizing, you need to identify what needs to be anonymized. Databases, files, logs, APIs, SaaS... Automatic inventory and classification require advanced data expertise. However, most internal projects are limited to static lists, which are insufficient in a context of constantly changing data.

An anonymizer must:

• guarantee referential integrity (e.g., same customer → same anonymized identifier on all systems)
• preserve formats and statistical distributions
• avoid collisions, duplicates, and inconsistencies

This is one of the most complex aspects to design and maintain.

Data is not only found in relational databases. A modern anonymizer must manage:

• Relational databases such as Oracle, PostgreSQL, Sybase, SAP HANA, DB2, etc.
• NoSQL databases like MongoDB, Cassandra, MarkLogic etc.
• Data warehouses like Google BigQuery
• Platforms / SaaS platforms as z/OS and Starburst
• Flat files like CSV, XML, JSON etc.Each format requires libraries, adapters, rules, and tests.

Anonymization is rarely isolated; it is part of integration pipelines, environment refresh automation, and DevOps tools.

A handcrafted script is not enough

The GDPR requires anonymization to be irreversible. How can we guarantee, with supporting evidence, that re-identification is impossible? This requires robust, audited, documented algorithms... which are rarely designed correctly in-house.

Interim conclusion: what seemed like a project lasting a few weeks quickly becomes a long, complex, and costly program, often underestimated by a factor of 5 to 10.

Here is a summary of the key differences between in-house development and adopting a specialized solution such as DOT Anonymizer:

A concrete example: some companies that have chosen DOT Anonymizer have reduced anonymization processing times from more than two days to around two hours. => Read the Success Story

The initial development cost is only the tip of the iceberg.

Between development, testing, documentation, integration, audits, maintenance, training, and adaptation to new rules... the bill skyrockets.

Teams change, architectures evolve, technologies are renewed. An internal tool that is not a priority often ends up obsolete after 2–3 years.

Every hour spent building an anonymizer is an hour not invested in projects that directly benefit the business. The key question then becomes: Is this really the core business of the company's IT department?

Conversely, adopting a tool-based solution a specialist vendor can bring immediate benefits.

The rules, engines, connectors, and algorithms are ready. No R&D delays, no reinventing the wheel.

Serious solutions include irreversible anonymization models, logs, audits, and a regulatory framework that is compliant by default.

You benefit not only from the tool, but also from the feedback of other customers. Regulatory and technological changes are managed as new versions are released.

Example: DOT Anonymizer comes with a studio, API, engines, connectors, and is supported by specialized teams.

There are cases where in-house development may be appropriate:

• Anonymization needs are extremely specific and fall outside market standards.
• The company has strong in-house capabilities in data engineering, cryptography, DevOps, and security.
• The context is non-regulatory or the risk exposure is low.

In most other cases, investing in a dedicated solution is a more reasoned choice

Developing your own anonymizer may seem appealing... but in most situations, this is a miscalculation. This choice involves hidden costs, rare expertise, risks of non-compliance, heavy maintenance, and schedule slippage.

Successful anonymization is a full project. To echo Gartner’s "Market Guide for Data Masking and Synthetic Data" “Representative DM vendors generally provide support for the full life cycle of a DM implementation, as well as enterprise manageability features”. In a context where speed, compliance, and risk control are key, specialized solutions generally offer the best compromise: they are quick to implement, comprehensive, secure, and scalable.

As technology evolves, the risk of re-identification increases. Regulators expect ongoing vigilance. Reputable vendor solutions combine expertise, advanced techniques, and regular updates, making them more reliable in terms of database consistency. Bottom line: safeguard that your anonymization is ‘future-proof’, irreversible and compliant in the long term!