Oracle数据脱敏最佳实践

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"Oracle数据脱敏最佳实践" Oracle数据脱敏是一种关键的安全措施，它能够将敏感和有价值的信息替换为现实但非真实的值，确保在非生产环境中使用数据时符合监管要求，如萨班斯-奥克斯利法案(Sarbanes-Oxley)、支付卡行业数据安全标准(PCI DSS)、健康保险可移植性和责任法案(HIPAA)以及众多其他法律法规。通过数据脱敏，企业可以在不影响业务运营和开发测试的同时，保护关键信息不被泄露。在实施数据脱敏时，面临的主要挑战包括如何在非生产环境中安全地处理数据，同时保持数据的可用性和真实性。Oracle提供了一套全面的数据发现、评估和遮掩策略。 1. 数据发现：Oracle工具能够进行企业级的敏感数据全面搜索，帮助识别需要脱敏的数据源和字段。 2. 评估与选择算法：Oracle提供了广泛的预设脱敏算法，可以根据数据类型和业务需求选择最适合的遮掩方法。 3. 高级遮掩技术： - 确定性遮掩：这种方法会产生固定的结果，适用于需要保持数据一致性的场景。 - 包装应用数据遮掩模板：对于特定应用程序，如ERP或CRM系统，Oracle提供了定义好的遮掩模板，以确保遮掩后数据仍保留其逻辑结构。 4. 安全执行：Oracle保证高效率的遮掩执行，包括在克隆的非生产环境中进行遮掩，以防止敏感信息泄漏。 5. 原地遮掩：数据在源系统上直接脱敏，减少数据迁移的风险。 6. 遮掩性能测试：通过测试确保遮掩操作不影响系统的整体性能。 7. 整合测试：与应用质量管理解决方案集成，保证遮掩后数据的可用性，用于测试和验证。 8. 数据遮掩与子集集成：可以结合数据子集功能，根据需要只遮掩部分数据，降低处理量。 9. 支持异构数据库：Oracle数据脱敏解决方案支持多种数据库平台，提供跨环境的统一保护。 10. 客户案例研究：Oracle文档中通常会包含实际客户的应用案例，展示数据脱敏的最佳实践和成功经验。结论：Oracle数据脱敏最佳实践旨在帮助企业实现对敏感数据的有效保护，确保在满足法规遵从性的同时，还能维持非生产环境的正常运作。通过实施这些最佳实践，企业可以降低因数据泄露带来的网络安全风险，提升整体数据治理水平。

Data Masking Best Practice

safely used for development, testing, outsource partners and off-shore partners or other non

production purposes.

The Challenges of masking non-production environments

Organizations have taken these threats seriously and have set out to address these issues as

quickly as possible knowing the ramifications. However, the idea of simply removing sensitive

information from non-production environment seems to be simple, it can pose serious

challenges in various aspects.

Some of the immediate challenges are identifying sensitive information. What defines sensitive

information? Where does it reside? How is it referenced? Applications have become very

complex and integrated. Knowing where the sensitive information resides and what

applications are referencing this information becomes a daunting task. Culminated with the

ever evolving application, the challenge also becomes maintaining meta-data knowledge of the

application architecture though-out its lifecycle.

Once sensitive information has been identified, the process of masking while maintaining

application integrity becomes paramount. Simply changing the value will inadvertently break

the application that is being used to test, develop or upgrade. As an example masking a part of

a customer’s address, such as zip without consideration of city and state, may render the

application unusable. Hence developing or testing becomes if not impossible, unreliable.

Auditing is another challenge that is considered seriously. Knowing who changed what and

when becomes an important business control requirement to prove compliance with

regulations and laws. To implement these types of controls, the challenge becomes

separations of duties, role based permissions and the ability to report on these activities.

Databases are becoming very large and the frequency of requests for a secure non-production

environment to be available has drastically increased over the years. The reason for this

increase is for business to develop newer and better applications which services their

customers at a faster pace to stay competitive. A masking process needs to have acceptable

performance and reliability.

And finally having a flexible solution that can evolve with the application and extend to other

applications within an enterprise becomes an important challenge to address.

Data Masking Best Practice

As a result of these challenges, unfortunately organizations have tried to address these issues

with custom hand-crafted solutions or repurposed existing data manipulation tools within the

enterprise to solve this problem of sharing sensitive information with non-production users.

Take for example, the most common solution: database scripts. At first glance, an advantage

of the database scripts approach would appear that they specifically address the unique

privacy needs of a particular database that they were designed for. They may have even been

tuned by the DBA to run at their fastest

Let’s look at the issues with this approach.

1. Reusability: Because of the tight association between a script and the associated

database, these scripts would have to be re-written from scratch if applied to another

database. There are no common capabilities in a script that can be easily leveraged across

other databases.

2. Transparency: Since scripts tend to be monolithic programs, auditors have no

transparency into the masking procedures used in the scripts. The auditors would find it

extremely difficult to offer any recommendation on whether the masking process built into a

script is secure and offers the enterprise the appropriate degree of protection.

3. Maintainability: When these enterprise applications are upgraded, new tables and

columns containing sensitive data may be added as a part of the upgrade process. With a

script-based approach, the entire script has to be revisited and updated to accommodate new

tables and columns added as a part of an application patch or an upgrade.

Implementing Data Masking

With these enterprise challenges in mind, Oracle has development a comprehensive 4-step

approach to implementing data masking via Oracle Data Masking Pack called: Find, Assess,

Secure and Test (F.A.S.T). These steps are:

• Find: This phase involves identifying and cataloging sensitive or regulated data across

the entire enterprise. Typically carried out by business or security analysts, the goal of this

exercise is to come up with the comprehensive list of sensitive data elements specific to the

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