The Impact of Data Analytics on Cybersecurity
Data Analytics and Cybersecurity are two different kinds of technology. But the adoption of data analytics cans leads to a stronger and secured cybersecurity. It plays a key role in cybersecurity by enabling firms to detect, prevent, and respond to cyber threats more effectively.
The Global Big Data Analytics Market was estimated at USD 37.34 billion in 2018 and expected to reach USD 105.08 billion by 2027 at a Compound Annual Growth Rate (CAGR) of 12.3 percent during the forecast period from 2019 to 2027. The main goal of several data analyst professionals is to incorporate advanced analytical techniques in cybersecurity for utilizing and breaking down huge volumes of data.
In this article, let’s understand the ways big data analytics is contributing to cybersecurity:
☛ Threat Detection and Prevention
Big data analytics techniques like machine learning (ML) and anomaly detection, can analyze huge amounts of data from several various sources such as network traffic, system logs, and user behavior to identify potential security threats in real-time. By recognizing patterns and deviations from normal behavior, it can help detect and prevent cyber-attacks before they cause significant damage.
☛ Behavioral Analysis
It can also be used to create profiles that are related to normal user behavior within an organization’s network. Any deviation from these established patterns usually indicates a security breach or unauthorized access. By continuously analyzing user behavior, data analytics can help identify insider threats and malicious activities.
☛ Threat Intelligence
Several professionals apply their data analyst skills to analyze and correlate threat intelligence feeds from many different sources such as open-source intelligence (OSINT), dark web monitoring, and industry-related threat reports. By identifying emerging threats and trends, organizations can proactively update their security controls and defenses to better protect against known and unknown threats.
☛ Security Operations Optimization
It can improve the efficiency of security operations by automating routine tasks, correlating security events across various multiple systems, and prioritizing alerts depending on their severity and potential impact. This gives security analysts the chance to focus their efforts on investigating and responding to the most critical threats, thereby reducing response times and minimizing the risk of data breaches.
☛ Risk Assessment and Compliance
The data analyst professionals also help organizations to better assess their cybersecurity risk posture by analyzing data related to vulnerabilities, patch levels, and compliance with regulatory requirements. By identifying potential gaps in security controls, organizations can prioritize remediation efforts and ensure compliance with industry standards and regulations.
☛ Detecting and Neutralizing Cyber Threats
Cyber threats are hidden in the shadows of the digital landscape, posing various risks to firms, businesses and individuals. With the increase in the sophistication of attacks, traditional security measures are no longer enough. By neutralizing it will be considered a game-changer that empowers cybersecurity teams to detect and mitigate threats, which might go unnoticed.
☛ Incident Response and Forensics
In the event of a security breach, data analytics can offer better assistance in incident response and forensic investigations. By analyzing historical data and logs, security analysts can reconstruct the timeline of events leading up to the breach, identify the root cause, and determine the extent of the damage. This information is essential for remediation efforts and applying measures to prevent similar incidents in the future.
☛ Log and Event Analysis
It also enables organizations to ingest, store, and analyze huge volumes of security-related logs and events generated by various systems and applications. By correlating and analyzing these logs in real-time, organizations can detect suspicious activities, which include unauthorized access attempts, brute force attacks, or data filtration, and respond promptly to mitigate the risks.
☛ Scalability and Performance
Many professionals have designed various applications as well as platforms by applying their data analyst skills to handle massive volumes of data and scale horizontally to meet the growing needs of cybersecurity operations. By leveraging distributed computing frameworks like Hadoop or Spark, many firms can process and analyze massive amounts of datasets in parallel, enabling faster detection, response, and decision-making in cybersecurity operations.
Conclusion
Overall, data analytics is a powerful tool in the cybersecurity arsenal, helping organizations stay ahead of evolving threats and effectively protect their sensitive data and critical assets. By harnessing its power organizations can strengthen their cybersecurity posture, mitigate risks, and protect sensitive data and critical assets from growing cyber threats.
