Data Security Purpose and Issues

To some extent, all organizations today deal with data. Data is at work in both big and small companies, ranging from banking behemoths dealing in vast numbers of personal and economic data to a one-man operation maintaining the contact information of his clients on a cell phone.

Purpose

Data security's main goal is to safeguard the information that an institution collects, keeps, develops, receives, or sends.
Another important factor to examine is compliance. The information must be protected regardless of which tool, technology, or method is utilized to handle, save, or gather it.
Data breaches can lead to lawsuits and hefty fines, not to forget the damage to a company's reputation. The importance of protecting data from cyber threats is higher than it's ever been.
Suppose you have exposure to a person's accounting transactions or medical records. In that case, you must have the appropriate security policies and procedures to protect it, which can be done with the help of data security.

Issues

1. Vulnerability to the creation of fictitious data

Before diving into all of big data’s organization’s security difficulties, it’s important to address the issue of falsified data creation. Cybercriminals can fake data and ‘dump’ it into your datastore to purposefully impair the integrity of your big information analysis. For example, suppose your industrial organization relies on sensor data to detect failing manufacturing processes. In that case, hackers can hack your system and cause your sensors to display false findings, such as incorrect temperatures.

2. Untrustworthy mappers could be present.

After your huge data has been acquired, it is processed in parallel. The MapReduce model is one of the strategies employed here. When data is divided into many bulks, a mapper analyses them and assigns them to certain storage alternatives. If an external gains entry to your mappers’ software, they can alter current mapper settings or introduce ‘alien’ mappers. In this method, fraudsters can have mappers generate insufficient lists of key/value pairs, thereby ruining your data processing.

3. The mining of sensitive data is a possibility.

Perimeter-based protection is commonly used to protect large amounts of data, and it signifies that all ‘entry and exit points have been saved. What IT professionals perform within your system, however, is a mystery. Because of this loss of control inside your big data solution, your dishonest IT specialists or malevolent business competitors may be able to mine and sell unencrypted data for their own gain.

4. Problems with data provenance

Data integrity, or historical documents about your data, is confusing. We can only imagine what a massive collection of metadata it could be, given that its function is to record the source of information and any alterations conducted with it, In terms of quantity, big data isn’t tiny. Imagine that each data item it holds has precise data about its origins and how it has been impacted.

Wrapping Up

Data security is a hot topic that necessitates organizations enlisting the help of specialists when dealing with significant volumes of sensitive data. It’s crucial to remember that better data security doesn’t come immediately.

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