- Big Data complexity and collateral security issues
- Security audits and actualization of your Big Data strategy are initial, yet critical, steps in resolving Big Data and security concerns
Big Data analytics is a business heavy-hitter that helps organizations run targeted campaigns to acquire more customers and retain them, create smart strategies for potential risk identification, and find more opportunities for product innovations. But to reap all those benefits, you should deal with Big Data security concerns first.
This article outlines the common security concerns related to Big Data and provides you with results-oriented methods to protect your sensitive data.
Big Data complexity and collateral security issues
Because of the Big Data diversity, its security can be viewed from two sides — corporate data and customer data. Organizations should be aware of business data security as the leakage of proprietary information is equivalent to exposing your weak points to your competitors and handing them a competitive advantage on a silver platter.
Imagine a logistics provider that has prepared a special transportation offer for its clients over the Christmas period. What if information about it leaks out right before the holiday season? Knowing the targeted audience and specifics of the promotional offer, your competitors will have the opportunity to improve their advertising campaigns and make them more attractive than yours.
Though business data leakage is one of the large-scale issues of data security, it affects only the organization itself. But along with corporate data, companies also keep confidential information about their customers. In case this data falls into unreliable hands, the severity of the problem skyrockets as more than 90% of people state they want to control who can get information about them.
Leakage of customer data might have major repercussions for the company in terms of the compensation fee it’ll be liable for and losing the clientele to competitors that offer the same services but with higher security levels. The largest recorded U.S. data breach in the healthcare sector was in 2015 at Anthem Inc., a health insurance provider in the United States when criminal hackers stole personal data affecting 80 million individuals. After two years of litigation, Anthem paid $150 million to settle lawsuits over the incident.
What security measures can you take to protect your business and customers’ data considering the diversity of data structure and format, storage location, origin source, device type, etc.? We’ve listed seven common data security problems and their solutions.
Network perimeter insecurity
When information enters a company’s network, it goes through various security checks. But if your system fails to separate potentially destructive data even at the initial stage, this harmful data can get into the infrastructure and cause a lot of damage. Cyber-attacks are continuously evolving and becoming more sophisticated, so organizations should rethink their Big Data security management and adopt zero trust architecture solutions. These assume a dynamic digital identity-based perimeter and approach where any user or device, application, or system, outside and inside the network, is classed as untrusted by default. In this case, organizations’ resources are secured regardless of their location because access control moves from the perimeter to each device and user, and the network is divided into micro-segments to make it harder for hackers to attack.
Social Engineering Attacks
A classic example of this type of attack is known as phishing. This is when attackers send you messages that seem to be from a known, trusted source but, in reality, are malicious. If your employee unknowingly clicks on an untrustworthy link, hackers can access the corporate network.
Implement gateways to prevent scam emails that may contain spam, malware, or phishing attempts. Gateways are useful for identifying bad emails thanks to their antivirus, anti-spam, and anti-phishing functions. But what if the malicious link isn’t in the email but in an attached PDF file? That way, gateways won’t identify the suspicious email and quarantine it. To confront the current data security problems and threats, take advantage of the additional features the gateways can provide you with, such as a sandbox. It’s a safe isolated replica of your real environment where you can open potentially malicious letters without impacting a system or platform on which they run.
Data Cleansing Problems
Big Data is known for promising benefits such as improved decision-making, but to get valuable information, you should first separate the wheat from the chaff and deal with the dirty and messy data. Otherwise, you’ll get stuck in a vicious cycle of poor data and a garbage-in-garbage-out trap.
Here’s where an automated data cleansing process enters the battlefield to provide you with correct, complete, and properly formatted data from your warehouse stores. But if those tools are not configured correctly, data cleaning will result in inconsistent data and Big Data analytics security concerns won’t go anywhere. An algorithm can fail at the data classification stage — it may define sensitive data as normal and share it with a wide range of people.
Flawed Data Masking Measures
Organizations adopt data masking policies to distinguish data that identifies customers (characteristics by which one person differs from another, such as date of birth, name, age) from confidential information about them (data that also is connected to a customer persona, but is changeable, like home address, driver’s license number, bank account number). The purpose of data masking is to prevent cybercriminals from matching customers and their sensitive information. If data masking is done incorrectly, it can be reversed by hackers.
A key solution for secure data management is data encryption. But it takes time to implement effectively, so how to keep the data processing speed up (since velocity is one of the five core Vs of Big Data along with variety, volume, value, and veracity)? Use these techniques of data encryption at the last stage of data processing:
- Data scrambling. Input data characters are randomly reorganized and replaced in data storage.
- Data substitution. Fake data replaces real information, you can use random names from a phone book instead of real customer names.
In both cases, original data is still available at your warehouse or data lake. You use substituted or scrambled information to make decisions as you can’t use sensitive personal data for those purposes according to data protection regulations including GDPR and D-DPA, to name a few.
In addition, so that your security efforts don’t go down the drain, make sure that specific data masking algorithm settings are available only for data owners in the relevant departments and no one else.
Fake Data Generation
Cybercrime attacks can result in fake numbers that your dashboards will show as it happened with Amazon when the site’s algorithms (Amazon’s Choice, top sellers, products ratings) were manipulated with fake products’ reviews that overrated certain products and sellers artificially. If you can’t see the real picture you’ll end up getting the wrong insights and making flawed decisions.
Data that has gone past its sell-by date also becomes a fake that can badly affect your decision-making process. Which fake results can it show? Learn from the story of United Airlines. They lost $1 billion a year because of an inaccurate pricing model that was based on the passengers’ seating preferences that used to be relevant 10 years before.
Your ability to protect client and company data is diminished if you can’t identify fake or outdated data in your central repository. Take advantage of BI consulting to analyze the current state of your data systems, use Machine Learning (ML) models to find anomalies in your data, and apply a fraud detection approach. ML-powered fraud detection systems can improve detection accuracy rates by 90% and reduce fraud investigation time by 70%.
Unauthorized changes in metadata
Bearing in mind the gargantuan volume of Big Data, unauthorized metadata modifications make it challenging to manage changes in ‘data about other data’ and identify the relevant information afterward as you don’t know for sure which changes are trustworthy.
To exclude unauthorized access and its displeasing consequences, such as wrong data sets and untraceable data sources:
- implement obligatory authorization processes for employees
- use nulling out when data is replaced with NULL for an unauthorized user
Since even basic sensitive information such as a document author’s name, revision history, type of software, in the wrong hands may lead to a potential data breach, you’d better use one more measure to secure your metadata — sanitization. This is the process of removing sensitive information from the document. After sanitization, the file may be distributed to a broader audience.
Unauthorized changes in metadata can also make things awkward if you prepare an offer for the customer by copying a previous file. If the document doesn’t go through the sanitization, your hypothetical client will have access to the history of changes and find the corrections of the original budgets or the scope of work for your previous customer.
Therefore, don’t underestimate the importance of this activity. Use automated data sanitization tools to ensure that only the intended information can be accessed.
Have you heard about the Equifax breach that was caused by an employee’s error? An individual in the technology department had ignored security warnings, which led to exposing the personal data of nearly 146 million Americans.
Sometimes hackers and other external security threats come in second to human errors in terms of damage done to the company. A CNBC study states that accidental loss of a document or a device by an employee is the reason for 47% of data breaches in organizations. The level of accidental staff negligence also rises along with the increasing number of devices having access to the company’s and customers’ sensitive data. It is as easy as snapping your fingers to share a file with unauthorized parties, either accidentally or maliciously, if your staff can access data from personal devices and over unsecured networks.
The technical aspects of the solution that will help you detect security breaches in time include the implementation of multi-layer authentication and an inside threat detection approach to get notifications about security threats by your staff. As for the ‘people’ part, create an atmosphere where employees feel safe to report on a lost or stolen device and do it immediately.
Security audits and actualization of your Big Data strategy are initial, yet critical, steps in resolving Big Data and security concerns
Big Data technology is a world of opportunities. It helps you better grasp your customers’ needs, create detailed hypotheses for market testing, and find ways to improve the product. And yet, Big Data technology is only worth using if the solutions you have are properly secured. Start with a security audit to identify the areas of concern, for example, those referred to data storage or data cleaning, and don’t ignore them when developing your Big Data strategy. Not sure you can deal with all the Big Data security challenges by yourself? Our BI experts have you covered, get in touch to find out how.
The complexity of data in terms of its structure, source, storage location, format, device type, etc. is one of the key security concerns related to Big Data. Coupled with the diversity of processes that occur with Big Data — storing, cleaning, masking, etc. — controlling various data transformations is challenging.
Ensuring security and confidentiality of business data and customers’ sensitive information are the main security issues in Big Data. You have to improve your system’s cyberattack resilience level, configure automated data cleaning, data masking, and document sanitization tools, establish mandatory authorization for employees, and adjust continuous monitoring of the system’s state.