Security Analytics Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)

Market Overview

• The Security Analytics market was valued at USD 3.63 billion in 2020 and is expected to reach USD 6.68 billion in 2026 with a CAGR of 10.7%, during the forecast period (2021 - 2026). The rapid growth of the internet has resulted in an exponential increase in the type and frequency of cyber attacks. Although many well-known cybersecurity solutions are utilized to counteract these attacks, the generation of Big Data over computer networks is rapidly rendering these traditional solutions obsolete.
• The increased pervasiveness of data breaches and the higher volume of impacted records are resulting in far higher costs for organizations of all sizes. According to ISF, an independent and not-for-profit association of leading organizations from around the world, the number, magnitude, and costs of data breaches are all set to continue on their upward trajectories in 2019.
• While day-to-day cyber threats continue to increase at an exponential rate, CISOs (Chief Information Security Officer) are most concerned over the rise of targeted and advanced malware enabled attacks such as Advanced Persistent Threats (APTs).
• According to statistics from IBM and Ponemon Institute, the cost of a breach in the United States estimated at USD 7.35 million or nearly double the USD 3.62 million it cost globally. As a result, organizations are increasingly concentrating on threat hunting as a preventive measure.

Scope of the Report

Security analytics is the process of using data collection, aggregation, and analysis tools for security monitoring and threat detection. Security analytics data can be collected in several ways, including Network traffic. Endpoint and user behavior data.

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Key Market Trends

Network Security Analytics to Account for Significant Share

• Network security applications are the largest source of demand for security analytics solutions globally. Security analysis, unlike other more well-behaved problem domains, does not naturally lend itself to statistical analysis. In most of the cases, programmers do not have the basic data that enable them to fabricate an accurate analytical engine.
• Further, given the tendency of attackers to constantly adapt to changes, makes fabrication of network security analytics solutions an almost impossible task to the programmers. Also, In recent times, there have been many cases of network and web-app security failures that brought catastrophic damage to several large enterprises and governments globally.
• Further, as the traditional network security analytics solutions based on the Bayesian probability theory, that states it’s possible to predict with high accuracy the likelihood of something happening by capturing every element of a problem and calculates possible outcomes mathematically, are being replaced with machine learning algorithms, due to this network security analytics solutions are getting more reliable.
• Bayesian models have domain conceptual alignment and the ability to reason on incomplete data, while machine learning has the sheer power and ability to cope with massive quantities of data required by modern networks, that are spreading beyond traditional applications.

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