Data integration is the process of centralizing data from different sources into a unified dataset. Once data is integrated, it can be queried, modified, added, or eliminated by users from a single user interface.
Data integration offers many advantages. For one thing, it is a way of preventing data silos within an organization. Moreover, data integration also makes possible automation and interoperability and is a step forward toward big data analysis. Hence, data integration is chosen by organizations with many departments, but also by large institutions made of different organizations that share information between them.
Still, facing data integration requires planning and presents different operational challenges, such as data security. The risks of negligence during data integration include:
- Data loss
- Governance loss
- Confidential and sensitive data leakage
- Unauthorized disclosure or secondary use of data
- Confidentiality and privacy compromises
- System breaches
- Financial losses
As such, when facing data integration, data security cannot be left to chance. In this article, we will review the most important security elements to consider during data integration and propose practices to avoid the aforementioned issues.
Heterogeneous security policies
Nowadays, it has become easier to create, manipulate and share information. The amount of data and data sources have increased exponentially, and it seems that in the future this trend will only continue to grow. As a result of so many types of data and so many ways of collecting and storing it, data sources tend to be heterogeneous. Hence, data is organized, represented, and validated diversely.
In this way, heterogeneous data sources are also prone to have disparate security policies. Different organizations and departments may have different policies and security obligations toward their data. Therefore, as each data source has its own security policy, before and during integration each data source will need to comply with the security and privacy requirements established for it.
Moreover, the integration and data-sharing processes should also have their own policies and apply to the process of data collection, processing, and disclosure. Redact and enforce new global security policies to prevent unauthorized disclosure or use of the data during and after integration.
A single authority may be behind applying and managing these global policies as a mediator who analyzes queries and judges if they accept or reject them. There are also tools to detect security or policy issues in software before deployment.
In the case of data sources that contain sensitive information such as individuals’ personal information, policy should communicate the nature of the data and specify how to handle it.
As policy determines how data must be managed, a user failing to meet policy requirements must be denied access to said data. Enforcing access control can be achieved through methods such as the need-to-know principle, in which data is only available to those parties who need it to perform their key processes.
Users could also be subject to pass an authentication mechanism to access data. Business VPNs, used for ensuring the availability of a company’s resources from any location, can also serve as an authentication method to grant access to data. Hence, in data integration, VPN ensures data security by filtering who can access the system and, accordingly, its data. You can check a variety of the most secure VPN options here.
As it is mentioned in the policy section, a single authority or third party can also be in charge of managing access control to data. Third parties can act as an intermediary trusted by all parties and be in charge of access to data. This way, third parties will provide data to authorized users or teams exclusively. More information about Trusted Third Parties (TTP) can be found here.
Data integrity and trustworthiness
Mechanisms to guarantee the reliability and trustworthiness of data should be set along with the life cycle of data, which includes the data integration process. Trust can be established by asking for the integrity of data certification from the departments or organizations that provide it, as well as requesting reports about how updated it is.
Cloud storage and SaaS platforms
Cloud solutions and SaaS platforms are often chosen for centralizing data and data management. In the case of third-party providers, they often have their own security measures. Still, they need to be reviewed and compared with policies already established.
Check the security and privacy technologies and policies of third-party cloud providers in case you use an external service or the internal security and policy measures if it is an on-premise server. Besides, check if the cloud provider’s terms of service grants permission to use or sell your data or if it complies with data protection laws.
Lastly, in SaaS collaboration platforms, don’t forget to review what options are available to restrict users’ rights to access data and if external measures such as other third-party tools must be taken to protect data within the platform.
Data privacy can be enforced by employing a handful of techniques. To name a few:
- Data anonymization: Freeing data from any identification information.
- Data perturbation: Adding “noise” to the data in a way that only authorized users can decode it and read it successfully.
- Data measuring: Measuring the amount of data shared depending on the user, who can also be inquired about what information it requires and for what purpose.
- Data destruction: keeping privacy through destroying data after a certain period.
You can also use meta-data to keep a track of who is the data owner, how sensible the data is, who should be able to have access to it and from where, and if it needs encryption.