Digitalization has changed the way business operations take place. Enterprises create and store vast volumes of data to research, manipulate, and extrapolate insights for better management and operational decisions. All this has increased the need for better data governance. In keeping with such growing data needs, computing environments have grown complex and now include the cloud, data centers, and multiple edge devices like sensors for the Internet of Things (IoT), robots, and remote servers.
In a bid to keep up with growing data security needs, companies also explore data integration to create a unified approach to their data. This warrants additional data security. With such complex data environments, security is a constant challenge. Additionally, there is an increase in customer awareness and demand for data privacy. To ensure that due importance is given to data privacy, governments worldwide have enacted numerous privacy laws and instituted regulations for compliance that companies have to adhere to.
What is data security? Data security is the set of procedures created to protect any digital data from unauthorized access and its misuse, all through its life cycle. This security covers not just the digital form of the data but also its physical security, covering related hardware, software, associated storage devices, and access. It also covers all related regulations and protocols set by the organization for the management of data.
The ideal data security procedures are those that not only protects an organization from an external threat to their data, but also from internal threats arising from sabotage or human errors, which incidentally are the highest causes of any data breaches today. Data security involves several tools and related technologies that work on giving an enterprise clear visibility of their critical data, its storage, and its use. These tools involve protocols related to encryption, masking of data, and redaction where needed. They also have provisions to generate automated reports to ease the process of auditing and to ensure compliance with regulatory stipulations.
Data Security Concerns During Data Integration
Data integration can leave systems vulnerable to cyber-attacks. Here is a look at some of the main concerns:
The Security Vulnerabilities with Traditional Data Integration: To reduce the complexity of data risk during the integration processes, it is necessary to have an effective role and policy-centric access control that oversees everything around the data – from governance to preservation and auditing. However, it is common for most organizations to end up with a large collection of database silos that have access controls independent of one another, making uniform tracking and protection of data nearly impossible.
Such silos are also riddled with ETL tools that have outdated code, points of integration, and unmanaged access controls leading to increased vulnerability. ETL processes are generally complex and use relational databases in their structure. They are implemented to help a system function and to ‘clean’ data to enable business process standardization. But, this cleaning of data often does not stand up to a quality check and removes data that could be essential. Data necessary for a compliance analyst may seem non-essential to a data analyst and will end up being removed. This may lead to key data going missing during the integration process, increasing the risk.
Over-burdened Application Developers: Securing data across multiple silos, and at every layer is a difficult task. In the case of multiple siloes, this securing is not done centrally or at the layer level. Instead, developers are burdened with the task of securing data on an application-level each time a new layer is created and added on. However, with the regulation governing data privacy and security becoming stringent and involving more accountability, the burden of data security is higher. The main concern here is how the development and security teams are often disconnected from each other’s work. This disconnect is further exacerbated with companies moving towards DevOps and agile technology development. Security growth has not been able to keep pace, time-wise, considering security reviews take several months to pass through necessary certifications and accreditations. A study by Gartner shows that almost 90 percent of organizations that take up DevOps put a pin on the thoughts around security.
Unknown Insider Risk Threats: In most cases, enterprises focus on threats at endpoints, the application perimeter, and any risks around general network security. This reduces intrusion attempts into the company’s infrastructure. This is especially crucial because organizations see thousands of such attempts being made daily. However, the concern is that a huge threat exists from factors within the company. Verizon released a study that states that the number of security breaches as a result of internal threats has increased by 25 percent in recent years.
Most organizations have a non-comprehensive approach to security controls and this often leaves them more vulnerable. What makes the insider threat more complex is the number of vendors that modern enterprises work with. Making a clear distinction between who is considered an insider and who is an outsider can be difficult. At times, seemingly harmless decisions on data management can prove to be massive risks to its security. For example, enterprises that utilize data lakes provide a gold mine of data to any user with broad access. Completely locking down a data system is not the solution in such cases as it prevents essential data sharing. The challenge lies in ensuring security even during data sharing.
Security measures have to be such that crucial segments of data are easily accessible by the right people, whether in or outside the organization, via the appropriate controls.
Data Security in Integration Projects
Data integration is bringing together data from multiple sources to create a single view. It starts right at the beginning and is inclusive of actions such as data cleaning, ETL mapping, and the transformation of data. This allows analytical tools to create more effective data that is actionable. There is no single approach to data integration. But a common thread across any approach is multiple data sources and one master server through which several people access data. The multi-layered aspects of data integration make it vulnerable to breaches. Security for data integration takes the customized approach and has to be done from case to case. While some may choose to go with data encryption, others may prefer masking or tokenization.
Encryption is where data is converted to another code and becomes password-protected for those looking for access. Tokenization works as an identifier. All valuable data is replaced with a token that can be mapped back to it. What remains visible is data that has no value. Data masking is a basic form of safety where sensitive data is hidden in plain sight, with modified content. It may simply be replaced with other data or characters. This approach cannot stave off advanced attacks.
Data security during data integration can be handled in many ways; however, most do not use any encryption of data. Some can be as simple as .csv or XML files being e-mailed or sent through an FTP server for download or placement on other systems. These files are also saved on removable storage forms such as USBs and memory cards. But the security risk continues to remain high and so does the chance of human error. One way to mitigate this danger is with the use of SSL encryption when data is moved between two systems and data is not staged with an FTP server or other media. In such cases, data is shared between databases only with approached APIs.
These are the functional approaches to data security during data integration. However, every business needs to anticipate the sophistication of cyber-attacks and be vigilant and careful. Here are some tips that will help.
Tips to Ensure Data Security During Integration
Risking data integrity is detrimental to the functioning of any organization. It necessitates implementing several strategic measures to mitigate threats. A single approach is never feasible. Here is a look at some effective means to reduce threats to data security.
Encourage a Culture of Integrity: A culture of integrity in the workspace brings down the risk associated with data security significantly. When a work environment helps an employee flourish and stay motivated, they are inclined to take their responsibilities more seriously. This immediately reduces the chances of error. They also feel responsible enough to report on those who may be slacking off and taking shortcuts.
Establish Quality Control Protocols: Instituting a system of quality control – a mix of responsible people and protocols can ensure that employees are compliant with the right manner in which to use data. For example, data stewards can be made responsible for data provenance with their data resources. IT personnel can be responsible for monitoring security systems related to data integrity.
Regularly Build Audit Trails: Having a clear audit trail is a simple way to ensure the mitigation of data integrity risks. These trails are a great way to see how data is affected at various stages of its lifecycle, including its origin, its many transformations, and its usage. All these specifics play a huge role in compliance with regulatory requirements.
Create Critical Data Process Maps: Creating process maps for critical data is a great way for enterprises to ensure better control over all their data assets. Creating these process maps to include how and where data is accessed, by whom, and how it is used before the data is put into use gives management better control. Such maps help with compliance too.
Remove all Known Vulnerabilities: It makes practical sense to remove all vulnerabilities to reduce any data integrity risks. This will require hiring the services of experts who can establish what specific security threats and vulnerabilities your data may be facing and who will suggest the right measures to counter them. Security patches are commonly used to handle this method.
Establish Software Development Lifecycles: Creating and following specific development lifecycles for software development is a key method to governing data through its course in an organization. These lifecycles are essential to understanding what governance processes are required to manage your data, and which are in keeping with regulatory requirements. This method allows a clear understanding of how data is deployed and creates the foundation for a sustainable ecosystem.
Regularly Validate Computer Systems: An organization can have several protocols in place governing how to plan and map data and its usage. But this has to be matched with procedures to ensure that both IT infrastructure and employees are also functioning per these processes. For example, consider that the IT teams have to constantly map sources to targets based on metadata previously mapped on specific factors. Only when computers that are part of this setup are constantly tested and validated, will the efficacy of the process be established.
Apply Error Detection Software: There is a wide range of software available for the detection of errors and anomalies. Implementing these can help with the monitoring and isolation of any factors that lead to errors and also show ways in which to prevent them from re-occurring. This helps in managing data integrity risks.
Secure Salesforce Integration Solutions
There are several approaches to security for data integration. Salesforce has been a leading name in the sector and specializes in provisions for secure data integration solutions.
Salesforce integration tools allow an organization to share data collected in Salesforce with their other systems. This enables people within an organization and those not on the Salesforce network to access relevant customer information when they require it. It also provides answers to all questions that the marketing and sales personnel may have and provides them with responses instantly to help them make better decisions based on incisive data. Such integration is essential to the growth of a company.
Considering Salesforce is a CRM system, its data integration tools require a complementary system like that of an ERP (or enterprise resource planning) to enable the sharing of data about customer accounts, associated inventory, and business protocols.
For example, by using Salesforce CRM systems along with an ERP solution like Microsoft Dynamics 365, data handled by both these systems ensures that companies can enhance the quality of their customer services and improve their overall satisfaction with the company. It also helps in the streamlining of all business processes.
Several such Salesforce data integration tools are effective. Some of them are:
- Rapidi – It has the benefit of not requiring any programming to enable integration. It is easily maintained and comes with ongoing support. It also works with timestamp mirror technology which especially enables ideal synchronizing with Microsoft Dynamics.
- Commercient – This comes with as many as 65 pre-programmed integrations and is inclusive of accounting software.
- Boomi – This is a system ideally used by customers who work with multiple systems and databases. However, it requires intense development and is primarily for larger businesses and enterprises.
- Jitterbit – This is a broad integration platform with several API integrations aimed at multiple systems.
- MuleSoft – This is a high-end API integration platform that is used by enterprises and comes with complex integrations. It enables the integration of multiple apps and supports the creation of specialized and complex solutions.
- Orbis Software – A SAP-centric tool that is aimed at integrating SAP and Microsoft Dynamics. This one is commonly used by SMEs.
- TIBCO – A generic integration tool that allows multiple platforms to connect. Some template-based support is available but deep business processes don’t come under its purview.
Data security is critical to data integration. It has to be done in a way that does not impede data sharing while protecting it at every stage. Salesforce comes with a variety of integration tools that assure the highest levels of security during data integration. Exploring what works best for your organization and its data protocols, with a clear understanding of your data requirements, is the first step.
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