Data security market trends
In the post-COVID-19 scenario, the global data security market size is projected to grow from $ 17.5 billion in 2020 to $ 35.3 billion by 2026, growing at a compound annual growth rate (CAGR) of 12.4. The growth of the global market growth can be credited to the rising levels of awareness and increase in investments towards data security solutions by organizations across various industries.
The impact of COVID-19 on data security market
The COVID-19 pandemic caused various governmental and regulatory authorities to enforce stringent social distancing protocols. This led to a sharp uptick in remote work habits, both in public and private organizations. Since then, an increasing number of business operations have become digitized and evolved into integrated work models. They are now a fundamental component of most future-facing business continuity plans. This includes the spread of the bring-your-own-device (BYOD) and work from home (WFH) culture, which has motivated companies to aggressively adopt cutting-edge digital technologies such as cloud which demands the adoption of new data and cyber security measures. In this regard, endpoint and virtual private network (VPN) security protocols make all the difference as they can determine whether you thrive or survive. Additionally, robust cyber-hygiene policies and enforcing cybersecurity best practices are imperative to business continuity for all global, technology-driven businesses.
In the post-COVID-19 scenario, the global data security market size is projected to grow from $ 17.5 billion in 2020 to $ 35.3 billion by 2026, growing at a compound annual growth rate (CAGR) of 12.4.
Data security market dynamics
Driver: Rising cyber-attacks leading to a demand for scalable data security solutions
Targeted cyberattacks have witnessed a sharp rise in recent years. Malicious vectors are used to infiltrate the targets’ network infrastructure whilst maintaining anonymity. In such cases, cybercriminals can select targets such as endpoints, networks, data, cloud or a combination of areas with great precision anywhere across the IT infrastructure to execute their goals. The primary motive behind targeted attacks is to steal critical business and consumer data. As a result, business-critical operations in organizations suffer disruptions. Companies lose intellectual property, incur financial losses and lose sensitive customer data. Targeted cyber-attacks affect organizations and their domestic and global customers. Personally identifiable information (PII) such as names, telephone contacts, addresses, drivers’ license details and social security numbers are stolen by attackers, resulting in security breaches and identity thefts.
Restraints: Low data security budget and high installation cost
Next-generation firewalls (NGFWs) and advanced threat protection (ATP) kits can be cost-intensive solutions. Emerging start-ups are not always able to make substantial budgetary allocations toward purchasing them. They simply do not have the resources to implement next-generation firewalls (NGFWs) and advanced threat protection (ATP) solutions. Small firms in developing economies also struggle with the lack of investments and limited funding. Their funds are diverted to meeting operational challenges and planning business continuity. In the absence of the required IT security infrastructure, these forms are slow adopters of new technologies and enterprise security solutions.
Opportunity: Adoption of AI- and ML-based applications to increase demand for data security solutions
Artificial intelligence (AI) is a rising trend, as it is used to automate processes and provide cognitive insights for actionable decision-making. Machine learning (ML), as a subset of AI, enables machines to learn from data and algorithms to accurately predict outcomes and improve their predictions by continuously learning from their users.
Enterprises are continuously accumulating data through their internal and external operations, Hence, ensuring data security has become a key business requirement. This is where AI and ML can enhance data security solutions by helping analysts accelerate their responses to potential breaches. These technologies also support scalable monitoring and detection solutions. They scan systems to detect abnormal behaviors and determine if the non-conformities qualify as potential threats.
Limitations: Discovering sensitive data at scale during data ingestion
Managing unstructured data is a major challenge in upholding data security. It differs from structured data in fundamental ways. While structured data resides in easily secured IT environments, unstructured data is far more nebulous. It can expose sensitive information through a variety of mediums such as documents, files, images, and videos that are easy-to-share across networks and social media platforms. This unstructured data also holds great value, if managed properly. It is the raw material that can be processed to generate meaningful insights that help grow businesses and achieve competitive advantage. A huge volume of such data is generated each day in various forms, from different sources. Sensitive data discovery is integral to creating and maintaining an effective data security plan. The rapid adoption of cloud and increase in remote workforce numbers has resulted in a “bring your own environment” scenario. Organizations are no longer only concerned with the sensitive data accumulated mainly on-premises. However, most IT systems have numerous paths for data transmission and even more for potential data storage. This complicates the vulnerability landscape. Therefore, sensitive data, both structured and unstructured, must be identified and analyzed across all sources. Some of the challenges are discussed below:
By deployment, the global cloud services market share was dominated by the public cloud segment and is expected to maintain its dominance:
Data can be secured using a variety of solutions, for both on-premises and cloud orientations, based on the specific needs of the businesses. The on-premises deployment approach allows organizations to exert greater control over their data security solutions such as next-generation firewalls and next-generation intrusion prevention system. On the other hand, the cloud deployment approach benefits enterprises with cloud-based solutions that enable greater speed, scalability and enhanced security effectiveness. The demand for cloud-based data security is critical as SMEs and global enterprises increasingly adopt cloud infrastructure and cloud-based applications for their business operations.
By organization size, the large enterprises segment is expected to hold a larger market:
The growth of the global data security market is being driven by large enterprises as they reshape their security policies and architecture. Not only are these players incorporating data at scale but also require security for their critical cloud-based assets. These organizations adopt most of their data security to safeguard networks, endpoints, data centers, devices, users and applications from unauthorized usage and malicious ransomware attacks.
North America projected to account for the largest market share (2020 to 2026):
The North American region has the presence of several prominent market players delivering advanced solutions to all the industry verticals in the regions. Furthermore, there is a significant increase in the deployment of data security solutions as factors such as geographical presence, global partnerships, strategic business investments and significant R&D activities continue to rise. Major global players such as IBM, Oracle, HPE, Microsoft and Thales, along with several start-ups in this region are offering enhanced data security software solutions and services that cater to the needs of customers and are poised to grow the North American market going forward.