Which factors do you consider the most threatening for a business? Financial risks? Competitors? Disruptive technologies? Surely, these aspects are important but cybersecurity issues remain the most dangerous and devastating. Grasp the number: 1.76 billion personal records were leaked in January 2019 alone! Costs of hacker attacks count in billions of dollars while the global cost approaches several trillion. No enterprise can feel safe now, so DWH privacy matters.
We realize how essential data warehouse security is. Working with banks and insurance companies, our developers have to design flawless systems to protect business and customer-sensitive data. In this guide, we share the knowledge gathered over the years of experience. You will learn about privacy basics and challenges, ways to improve your data warehouse protection, including encryption methods and hardware-based approaches.
How to implement a DWH for bank
A data warehouse or DWH is a software tool that collects business information from several sources. Put simply, it’s a repository. It stores data, provides quick access and helps in the analysis. Obviously, it also must be safe. And here comes the main problem.
In a nutshell, DWH privacy is similar to this aspect in other systems. Protected apps should prevent unauthorized access and hacker attacks while employees should be able to get the required data when they need it. However, too strict access would interfere with users working with the information seamlessly. Moreover, security always affects performance.
Business owners should care about the protection of the company’s/users’ data before building databases. Pay attention to the ways you’re going to use the data. For instance, warehouses focused on selling data should feature separated access levels for each client. Simultaneously, bases for internal work should prioritize quick and error-free processes.
Let’s look at the current issues of data warehouse modeling and protection. Apart from the aforementioned importance of balancing between smooth access and security measures, there are a few other points:
In 2018, in the report published by Hiscox, researchers surveyed more than 4,000 companies from the USA, the UK, Spain, Netherlands, and Germany. The results revealed that 73% of companies aren’t ready for hacker attacks at all, i.e. they were so-called «cyber novices». To deal with the listed challenges and become at least a «cyber intermediate», businesses should start with the architecture of the planned system.
Just trust us: it’s much easier to build a robust and protected platform than to redesign it to get better DWH privacy, add new features or upgrade security layers later. Naturally, enterprises grow by acquiring new clients or partners. This process leads to new data sources, as well as new access levels. Without proper initial planning, you will have to add security measures and set access for all the new partners, spending extra resources.
Hence, let’s think about how to build a reliable database at the beginning. According to data warehouse modeling, there are four key activities to remember.
To start with, there’s a system of access layers. They can be set based on different criteria, e.g. data types, job functions, the company’s hierarchy or employees’ roles. When you design the warehouse, you should think about data people will access and then classify both information and end-users.
There are two data classification approaches:
And two user classification methods:
Choosing one way or combining several of them, managers can build a comprehensive yet scalable data warehouse architecture. Remember that new data/user types may appear over time and use universal classes.
Most often, data is compromised when an employee accesses it. Sometimes, hackers get quicker access to restricted areas when the packages are uploaded or downloaded. Also, workers can steal sensitive info directly. Say, in April 2019, more than 540 million Facebook private records were found on public Amazon cloud servers. It’s a bright example of poor security during data exchange between platforms.
To keep DWH privacy at a high level, answer questions related to different aspects of data movement:
Regardless of data type, remember to keep the same security standards. For instance, often, regular employees can make a query and get temporary tables with restricted info. It’s unacceptable.
Apart from the user and data security, we shouldn’t forget about tech stuff. Data warehouse modeling provides for designing and connecting reliable infrastructure. To make your network safe, plan how the data will flow across the organization, which ways you will use to send and receive info, and what type of encryption you will use (if any).
Our data science professionals worked with a lot of systems based on poor data warehouse architecture. One of the most common issues refers to poor scalability. Enterprises use advanced encryption methods but forget that large data packages require more processing power over time. That’s why it’s essential to plan the structure before creating the DWH.
Well, now, let’s move to the exact tips & tricks! Despite serious challenges and tons of concerns to foresee, it’s definitely possible to build a reliable, safe, and powerful data warehouse. Further, we list efficient time-proven approaches to maintain perfect security. On the most basic level, these options are divided into two types: hardware of physical measures and software-based ones. We will focus on both aspects.
Physical conditions and protection of your database may look less important than digital sides. However, they also form a crucial security level. All software decisions would be obsolete if a fraudulent employee could access the data warehouse physically and damage or steal valuable information. Hardware-focused solutions come down to three points:
While top-notch physical DWH privacy is often a must-have, we suggest managers calculate expenses carefully. It’s illogical to build a defense that costs several billion when the estimated losses from data leak is a few million. Still, large companies should invest in physical defense. 3 billion compromised Yahoo accounts resulted in $350 million damage, for example. Most likely, it’d be cheaper to prevent this attack.
The main battle between cybersecurity specialists and hackers takes place in the digital world. Hardware acts as a basement but the software is a key factor. Let’s look at the most useful safeguards that refer to data warehouse architecture, access points, and users:
Similarly to hardware protection, don’t forget to calculate expenses. If the potential damage is low, don’t invest in costly solutions – you just don’t need them. Consider reputational losses here, too. For instance, banks are interested in advanced security systems even if they don’t have a lot of sensitive data in their storage. Protected banks are more demanded by customers, obviously.
Numerous studies describe the idea of DWH privacy. According to the analysis, the experts often talk about encryption, audit, transformation, views, multi-platform connections, and general data warehouse modeling. The majority of studies focus on extendibility and independence models while the most popular approaches include encrypted queries, UML-, and XML-based security techniques.
We can predict that old approaches like Adapted Mandatory Access control will disappear as cybersecurity professionals will introduce more efficient options. Our developers are aware of the most innovative techniques and are ready to use them for your data warehouses. Feel free to contact us if you need a consultation, upgrade, or a completely new custom DWH. Don’t wait and protect your data today!
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