For organizations to tackle data attacks, they need the assistance of data analytics to play the role of security against evolving risks. Organizations can build robust defense capabilities using data analytics, and safeguard extensive database from breaches.
Data analytics has become vital for organizations looking to shield their sensitive data, posing customers’ personal information, business assets, and core information.
The power of data analytics to process data from various sources, analyze and segregate has proven to be a game changer in cybersecurity.
With the increasing severity and frequency of cyber-attacks directed at data, data analytics in cybersecurity is being used at scale as a necessity. Today, data analytics is applied to improve the detection and response to cyber threats, as well as to enhance the overall security web in organizations.
CISOs consider data analytics in cybersecurity a double-edged sword and find it advantageous by looking at its use cases to fight threat actors efficiently. Here are the best use cases of data analytics that can help businesses build a defensive roof of security and leverage its power to safeguard the database efficiently.
Real-time Fraud Detection
Cyber-thieves are growing smarter at identifying innovative inroads to crack businesses and attacking data systems. This calls for action for businesses to take steps, using data analytics. This is a modern approach to cybersecurity that continuously monitors online system data logs and safeguards them against disasters and cyber assaults.
Data analytics enables quicker actions in cybersecurity and can also pre-empt erroneous behavior in the data system. It can be a part of an efficient cybersecurity strategy for the early detection of data threats so that CISOs can implement relevant countermeasures to the security risk.
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Promotes Network Security
Cyber-hacking is the most common threat against businesses. Here, data analytics plays a crucial tool in analyzing and anticipating cyber hacking and protects data by understanding the nature of the attacks.
It also mitigates the challenges of developing a mechanism for aggregating the security risk of all systems in a network. Essentially, it catches the vulnerability exploit areas after assessing threats. CISOs should consider data analytics as an intelligence-based approach to monitor networks across the organization. It is because by employing scalable parallel processing data analytics in cyber security, it can begin engaging deeper and cutting-edge inspection through log analysis. Also, visual data analytics can help offer a network security administrator extensive network visibility so that security measures are built in time.
Data Exfiltration
The application of data analytics in cybersecurity for data exfiltration efficiently detects new approaches being used for data leakage. It can be identified by standard data loss prevention (DLP) solutions. These solutions help to detect leakage by understanding the pattern of data automatically, for instance, specific data fields, customer information, files, or business-driven information. Detecting such illicit exfiltration is possible by HTTP POST traffic, a common method used to upload data.
So, the analytics process enables the detection of abnormal traffic movement from any system and helps in mitigating using appropriate steps. CISOs should address data analytics in cybersecurity as it is a self-learning system that detects data exfiltration efficiently.
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Data Clustering & Segmentation
Data analytics in cybersecurity has a significant potential to provide essential insights and detect unexpected threats. Since almost a lot of data remains unstructured in organizations, it makes threat actors easily enter business models by attacking databases intensely. Consequently, in such situations, data analytics in cybersecurity strengthens segment and cluster data approaches.
Data restoration, the recovery of a non-faulty database system, and data loss prevention are all attainable using data analytics weapons in providing security measures. This technique can evaluate and identify bottlenecks in the segmentation process. Security leaders can easily cluster and segment data by taking deep insights and building a secure roof using analytics techniques, thus safeguarding data in both static and dynamic demographics at scale.
Data Analytics: Laying the Road for Building Full-Proof Data Security
Data always remains under the radar of attacks by threat actors. Uncertainties with data breaches are always a challenging part of any business. Therefore, with the power of data analytics, businesses can have a promising security web across data due to the above use cases.
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