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Data Loss Prevention Global Market Report 2024 - Market Size, Trends, And Global Forecast 2024-2033 | Microsoft Corporation, International Business Machines Corporation (IBM), Cisco Systems Inc., Broadcom Inc., SAP SE, BAE Systems PLC, Thales Group, VMwar

Data Loss Prevention Market Size

Data Loss Prevention Market Size

As per the report, the data loss prevention market size has grown exponentially in recent years. It will grow from $2.3 billion in 2023 to $2.92 billion in 2024 at a compound annual growth rate (CAGR) of 27.0%. The data loss prevention market size is expected to see exponential growth in the next few years. It will grow to $7.6 billion in 2028 at a compound annual growth rate (CAGR) of 27.1%.

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Rising Data Breaches Fuel Demand For Data Loss Prevention Solutions
The surge in data breach cases is expected to propel the growth of the data loss prevention market going forward. A data breach is an incident in which sensitive, confidential, or protected information is accessed, disclosed, or compromised without authorization. Data loss prevention (DLP) is the practice that involves identifying and stopping data breaches, unauthorized data movement, or the unintended destruction of sensitive information by implementing strategies and technologies to detect, prevent and mitigate the loss or unauthorized access of sensitive data. For instance, in January 2022, according to the Identity Theft Resource Center, a US-based nonprofit organization, the total count of data breach cases accounted for 1,862 in 2021 and has risen by over 68% compared to 2020. Therefore, the surge in data breach cases is driving the growth of the data loss prevention market.

Emphasis On Generative Ai Utilization In Data Loss Prevention
Major companies operating in the data loss prevention market are adopting advanced security solutions powered by generative AI technologies to increase their profitability in the market. Generative AI technologies are artificial intelligence systems that can generate new content, such as text, images, video, audio, code, or synthetic data. Machine learning algorithms are used in these technologies to learn patterns and structures. For instance, in June 2023, Zscaler Inc., a US-based company that provides data loss prevention services, launched advanced security solutions integrated with generative AI tools. Zscaler uses AI/ML or generative AI in its enormous data pool to forecast breaches and advocate policies to improve threat detection, prevention and response.

The data loss prevention market covered in this report is segmented -

1) By Service: Managed Security Services; Training And Education; Consulting; System Integration And Installation; Threat And Risk Assessment
2) By Solution: Network Data Loss Prevention; Endpoint Data Loss Prevention; Datacenter Or Storage-Based Data Loss Prevention
3) By Deployment: On-Premises; Cloud-Based
4) By End-User Industry: Information Technology (IT) And Telecommunication; Banking, Financial Services And Insurance (BFSI); Government; Healthcare; Manufacturing; Retail And Logistics; Other End-User Industries

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Major players in the data loss prevention market are Microsoft Corporation, International Business Machines Corporation (IBM), Cisco Systems Inc., Broadcom Inc., SAP SE, BAE Systems PLC, Thales Group, VMware Inc., Fortinet, Citrix Systems Inc., Check Point Software Technologies Ltd., Trend Micro Inc., CrowdStrike Holdings Inc., Proofpoint Inc. , RSA Security LLC, BlackBerry Ltd., Forcepoint LLC, Idera Inc., Trustwave Holdings Inc., McAfee Inc., Sophos Ltd., CA Technologies Inc., Netskope Inc., CoSoSys Ltd., Code Green Networks Inc., Zecurion DLP, Avanan Inc., GTB Technologies Inc.

Contents of the report:
1. Executive Summary
2. Data Loss Prevention Market Report Structure
3. Data Loss Prevention Market Trends And Strategies
4. Data Loss Prevention Market - Macro Economic Scenario
5. Data Loss Prevention Market Size And Growth
…..
27. Data Loss Prevention Market Competitor Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis
30. Appendix

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