![]() |
½ÃÀ庸°í¼
»óǰÄÚµå
1812554
µð¼Á¼Ç ±â¼ú ½ÃÀå : ÄÄÆ÷³ÍÆ®º°, µð¼Á¼Ç ½ºÅú°, Áö¿ªº°Deception Technology Market, By Component, By Deception Stack, By Geography |
µð¼Á¼Ç ±â¼ú ½ÃÀåÀÇ 2025³â ½ÃÀå ±Ô¸ð´Â 29¾ï 5,000¸¸ ´Þ·¯·Î ÃßÁ¤µÇ¸ç, 2032³â¿¡´Â 72¾ï ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹ÃøµÇ¸ç, 2025-2032³âÀÇ ¿¬Æò±Õ ¼ºÀå·ü(CAGR)Àº 13.6%·Î ¼ºÀåÇÒ Àü¸ÁÀÔ´Ï´Ù.
¸®Æ÷Æ® ¹üÀ§ | ¸®Æ÷Æ® »ó¼¼ | ||
---|---|---|---|
±âÁØ¿¬µµ | 2024³â | 2025³â ½ÃÀå ±Ô¸ð | 29¾ï 5,000¸¸ ´Þ·¯ |
½ÇÀû µ¥ÀÌÅÍ | 2020-2024³â | ¿¹Ãø ±â°£ | 2025-2032³â |
¿¹Ãø ±â°£ : 2025-2032³â CAGR : | 13.60% | 2032³â °¡Ä¡ ¿¹Ãø | 72¾ï ´Þ·¯ |
µð¼Á¼Ç ±â¼úÀº Á¶Á÷ÀÇ ³×Æ®¿öÅ© ÀÎÇÁ¶ó¿¡ Çö½ÇÀûÀÎ ¹Ì³¢, ÇÔÁ¤, °¡Â¥ ȯ°æÀ» ¸¸µé¾î ½Ç½Ã°£À¸·Î ¾ÇÀÇÀûÀΠȰµ¿À» °¨Áö, ÆíÇâ, ºÐ¼®ÇÏ´Â ±â¼úÀÔ´Ï´Ù. ÀÌ º¸¾È Á¢±Ù ¹æ½ÄÀº »çÀ̹ö º¸¾È»óȲÀ» ±âÁ¸ÀÇ °æ°è ±â¹Ý ¹æ¾î¿¡¼ À§ÇùÀ» Á¶±â¿¡ ¹ß°ßÇÒ ¼ö ÀÖ´Â ÀÎÅÚ¸®Àü½º ±â¹Ý Àü·«À¸·Î ÀüȯÇÕ´Ï´Ù. ÀÌ ±â¼úÀº Çã´ÏÆÌ, Çã´Ï³Ý, µð¼Á¼Ç ÅäÅ«À» IT ȯ°æ Àü¹Ý¿¡ °ÉÃÄ Àü·«ÀûÀ¸·Î »ç¿ëÇÔÀ¸·Î½á ±ÍÁßÇÑ ÀÚ»êÀ» Âø°¢ÇÏ°Ô Çϰí, °ø°ÝÀÚ¸¦ À¯ÀÎÇÏ¿© ±× Á¸Àç, Àü¼ú, Àǵµ¸¦ µå·¯³»°Ô ÇÕ´Ï´Ù. Á¶Á÷ÀÌ Á¦·Îµ¥ÀÌ °ø°Ý, ³»ºÎ À§Çù, Ãø¸é À̵¿ °ø°Ý µî º¹ÀâÇÑ »çÀ̹ö À§Çù¿¡ Á÷¸éÇϰí ÀÖ´Â °¡¿îµ¥, µð¼Á¼Ç ±â¼úÀº °ø°ÝÀÚÀÇ ÇൿÀ» °¡½ÃÈÇÏ´Â µ¿½Ã¿¡ ±âÁ¸ º¸¾È ¼Ö·ç¼ÇÀ» ±«·ÓÈ÷´Â ¿ÀŽÀ» ÃÖ¼ÒÈÇÕ´Ï´Ù. ÀÌ ½ÃÀå¿¡´Â On-Premise, Ŭ¶ó¿ìµå ±â¹Ý, ÇÏÀ̺긮µå µî ´Ù¾çÇÑ µµÀÔ ¸ðµ¨ÀÌ ÀÖÀ¸¸ç, ÀºÇà, ÇコÄɾî, Á¤ºÎ±â°ü, Áß¿ä ÀÎÇÁ¶ó ºÎ¹® µî ´Ù¾çÇÑ ¾÷Á¾¿¡ ´ëÀÀÇϰí ÀÖ½À´Ï´Ù.
»çÀ̹ö À§ÇùÀÌ º¹ÀâÇØÁö°í, ±âÁ¸ º¸¾È ´ëÃ¥À¸·Î´Â °íµµÈµÈ Áö¼ÓÀû À§Çù°ú Á¦·Îµ¥ÀÌ °ø°Ý¿¡ ´ëÇÑ ´ëÀÀÀÌ ¹ÌÈíÇØÁö¸é¼ ½ÃÀåÀÌ È®´ëµÇ°í ÀÖ½À´Ï´Ù. ƯÈ÷ »çÀ̹ö ¹üÁËÀÚµéÀÌ AI¸¦ Ȱ¿ëÇÑ °ø°Ý°ú ½Ã±×´Ïó ±â¹Ý °¨Áö ½Ã½ºÅÛÀ» ¿ìȸÇÏ´Â »çȸ°øÇÐÀû ±â¼úÀ» äÅÃÇÔ¿¡ µû¶ó Á¶Á÷Àº ½É°¢ÇÑ ÇÇÇØ°¡ ¹ß»ýÇϱâ Àü¿¡ ¾ÇÀÇÀûÀΠȰµ¿À» ½Äº°ÇÒ ¼ö ÀÖ´Â »çÀü ¿¹¹æÀû À§Çù °¨Áö ±â´ÉÀÇ Çʿ伺À» Á¡Á¡ ´õ ¸¹ÀÌ ÀνÄÇϰí ÀÖ½À´Ï´Ù. Ŭ¶ó¿ìµå ÄÄÇ»ÆÃ, IoT ±â±â, ¿ø°Ý ±Ù¹« ȯ°æÀÇ µµÀÔÀÌ Áõ°¡ÇÔ¿¡ µû¶ó °ø°Ý ´ë»óÀÌ Å©°Ô È®´ëµÇ¸é¼ ºÐ»ê ³×Æ®¿öÅ© ¾ÆÅ°ÅØÃ³ Àü¹Ý¿¡ ´ëÇÑ Á¾ÇÕÀûÀÎ °¡½Ã¼ºÀ» Á¦°øÇÏ´Â µð¼Á¼Ç ±â¹Ý º¸¾È ¼Ö·ç¼Ç¿¡ ´ëÇÑ ¼ö¿ä°¡ ±ÞÁõÇϰí ÀÖ½À´Ï´Ù. ±×·¯³ª ½ÃÀå¿¡´Â ³ôÀº µµÀÔ ºñ¿ë, µµÀÔ ¹× °ü¸®ÀÇ º¹À⼺, µð¼Á¼Ç ȯ°æÀ» È¿°úÀûÀ¸·Î ¼³Á¤Çϰí À¯ÁöÇϱâ À§ÇÑ »çÀ̹ö º¸¾È Àü¹® Áö½ÄÀÇ Çʿ伺 µîÀÇ ¾ïÁ¦¿äÀÎÀÌ ÀÖ½À´Ï´Ù. ¶ÇÇÑ ³×Æ®¿öÅ© ¼º´É¿¡ ¹ÌÄ¥ ¼ö ÀÖ´Â ÀáÀçÀûÀÎ ¿µÇâ°ú Á¤´çÇÑ »ç¿ëÀÚ°¡ ½Ç¼ö·Î µð¼Á¼Ç Æ®·¦À» ÀÛµ¿½Ãų ¼ö ÀÖ´Â À§Çè¿¡ ´ëÇÑ ¿ì·Á·Î ÀÎÇØ ÀϺΠÁ¶Á÷Àº ÁÖÀúÇϰí ÀÖ½À´Ï´Ù. ÇÏÁö¸¸ µð¼Á¼Ç Ç÷§ÆûÀÇ ÀΰøÁö´É ¹× ¸Ó½Å·¯´× ±â´ÉÀÇ È°¿ëÀÌ Áõ°¡ÇÔ¿¡ µû¶ó °ø°Ý ÆÐÅÏÀÇ º¯È¿¡ µû¶ó ÁøÈÇÒ ¼ö ÀÖ´Â º¸´Ù °íµµÈµÇ°í ÀûÀÀ·Â ÀÖ´Â À§Çù °¨Áö ¸ÞÄ¿´ÏÁòÀ» ±¸ÇöÇÒ ¼ö ÀÖ´Â ¸¹Àº ±âȸ°¡ »ý°Ü³ª°í ÀÖ½À´Ï´Ù.
º» Á¶»çÀÇ ÁÖ¿ä Æ¯Â¡
Deception Technology Market is estimated to be valued at USD 2.95 Bn in 2025 and is expected to reach USD 7.20 Bn by 2032, growing at a compound annual growth rate (CAGR) of 13.6% from 2025 to 2032.
Report Coverage | Report Details | ||
---|---|---|---|
Base Year: | 2024 | Market Size in 2025: | USD 2.95 Bn |
Historical Data for: | 2020 To 2024 | Forecast Period: | 2025 To 2032 |
Forecast Period 2025 to 2032 CAGR: | 13.60% | 2032 Value Projection: | USD 7.20 Bn |
Deception technology creates realistic decoys, traps, and false environments in an organization's network infrastructure to detect, deflect, and analyze malicious activities in real-time. This security approach moves the cybersecurity landscape from traditional perimeter-based defense to an intelligence-driven strategy that provides early threat detection capabilities. The technology uses honeypots, honeynets, and deception tokens strategically throughout IT environments to create an illusion of valuable assets, thereby luring attackers into revealing their presence, tactics, and intentions. As organizations face complex cyber threats including zero-day exploits, insider threats, and lateral movement attacks, deception technology offers visibility into attacker behavior while minimizing false positives that plague conventional security solutions. The market includes different deployment models including on-premises, cloud-based, and hybrid solutions, serving different industry verticals such as banking, healthcare, government, and critical infrastructure sectors.
The market is seeing growth because of the rising complexity of cyber threats and the inadequacy of traditional security measures to combat advanced persistent threats and zero-day exploits. Organizations are increasingly seeing the need for proactive threat detection capabilities that can identify malicious activities before significant damage occurs, particularly as cybercriminals employ AI-powered attacks and social engineering techniques that evade signature-based detection systems. The growing adoption of cloud computing, IoT devices, and remote work environments has expanded attack surfaces a lot, creating an urgent demand for deception-based security solutions that provide comprehensive visibility across distributed network architectures. However, the market sees some restraints including high implementation costs, complexity in deployment and management, and the requirement for specialized cybersecurity expertise to effectively configure and maintain deception environments. Also, concerns about potential impact on network performance and the risk of legitimate users accidentally triggering deception traps create hesitation among some organizations. Nevertheless, many opportunities are created from the increasing use of artificial intelligence and machine learning capabilities in deception platforms, making possible more sophisticated and adaptive threat detection mechanisms that can evolve with changing attack patterns.
Key Features of the Study