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Cloud Data Loss Prevention Market Segmented By solution type that is network DLP, endpoint DLP and storage DLP, also by deployment type that is public and private cloud used in IT and telecom, BFSI, healthcare, government and defence, manufacturing, retail and logistics, energy and utility.
In today’s world, huge amount of sensitive data such as customer financial information, patient health information, personal confidential documentation or intellectual property documents is moving to the cloud, but many enterprises lack policy controls for data stored in cloud services.
Data loss prevention is a process of protecting sensitive data which is at rest, in-motion and endpoint to reduce the chances of data theft or unauthorized exposure. Cloud data loss prevention solutions protect enterprises that have adopted cloud storage. Cloud DLP solution ensure that data is encrypted and sent to authorized cloud applications. This solution offers a built in support for scanning and classifying sensitive data in cloud storage.
Cloud DLP helps in detecting data policy violation, prevents data leakage and uses data discovery and classification to find and understand sensitive data.
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Factors such as increase focus of organizations to meet data regulations, compliance requirements for the data saved on public and private cloud and increasing instances of data breaches and cyber-attacks at various platforms are the factors driving the growth of cloud data loss prevention. Moreover, law enforcement agencies around the globe are promoting efficient and effective use of technology for protecting individual and organization data.
However, lack of qualified professionals for developing cloud data loss prevention solutions and deficient knowledge regarding cloud data loss prevention are some factors causing a hindrance in the growth of cloud data loss prevention.
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Get a Customized VersionCloud Data Loss Prevention can be segmented on the basis of solution type, deployment type, application industry vertical, end-user size and region wise. On the basis of solution type, it can be further segmented into network DLP, endpoint DLP and storage DLP.
On the basis of deployment type cloud DLP market is further segmented into public and private cloud. Application wise market is further segmented into encryption, storage, web protection, policy and standards and others. By industry vertical, this market can be sub-segmented into IT and telecom, BFSI, healthcare, government and defence, manufacturing, retail and logistics, energy and utility and others.
On the basis end user type, market is further segmented into small & medium enterprises and large enterprises. Region wise, cloud data loss prevention can be segmented into North America, Latin America, Asia Pacific, Japan, Eastern Europe, Western Europe and Middle East & Africa.
North America cloud data loss prevention holds the largest market share and is expected to dominate the market in the near future owing to large number of early technology adopters in this region.
Europe contributes a significant market share in cloud data loss prevention market due to rise in adoption of cloud services by small, medium and large enterprises in these regions. Asia-Pacific region is expected to show the highest growth rate as enterprise are adopting cloud data loss prevention to combat IT security threats and increased digitization in this region.
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CipherCloud, McAfee, Trustwave, Check Point Software Technologies Ltd, Symantec, Digital Guardian, Skyhigh Networks, Blue Coat Systems, Cisco Systems, Code Green Networks and Zecurion are some of the key players in Cloud Data Loss Prevention market.
The report is a compilation of first-hand information, qualitative and quantitative assessment by industry analysts, inputs from industry experts and industry participants across the value chain.
The report provides in-depth analysis of parent market trends, macro-economic indicators and governing factors along with market attractiveness as per segments. The report also maps the qualitative impact of various market factors on market segments and geographies.
