Chicago, Aug. 02, 2023 (GLOBE NEWSWIRE) -- The AI in cybersecurity Market by Offering (Hardware, Software, and Service), Deployment Type, Security Type, Technology (ML, NLP, and Context-Aware), Application (IAM, DLP, and UTM), End User and Geography - Global Forecast to 2028", Factors such as the growing adoption of IoT and increasing number of connected devices, increasing instances of cyber threat, rising concerns of data protection, and increasing vulnerability of Wi-Fi networks to security threats are driving the growth of the AI in cybersecurity industry during the forecast period.
[312 Pages Report] The artificial intelligence in cybersecurity market size is valued at USD 22.4 Billion in 2023 and is anticipated to be USD 60.6 Billion by 2028; growing at a CAGR of 21.9%.
Key Market Players in AI in cybersecurity Market
- NVIDIA Corporation (US),
- Intel Corporation (US),
- Xilinx Inc. (US),
- Samsung Electronics Co., Ltd (South Korea),
- Micron Technology, Inc. (US), I
- BM Corporation (US),
- Amazon Web Services, Inc. (US),
- Microsoft (US),
- Palo Alto Networks Inc. (US),
- Trellix (US),
Artificial intelligence in cybersecurity Market Dynamics
Driver: Increasing instances of cyber threats
The instances of cyberattacks are gradually increasing globally. Cybercriminals attack endpoints, networks, data, and other IT infrastructure that lead to a huge financial loss for individuals, enterprises, and governments. The primary motive of cybercriminals includes political rivalry, financial gain, harm reputation, international rivalry, and radical religious group interest. The majority of cyberattacks are for financial gains. WannaCry, Petya, NotPetya, and BadRabbit are among the significant ransomware that have affected enterprises and government organizations on a large scale.
According to the CISCO cybersecurity threat trends report 2021, 86% of the organizations of organizations had at least one user try to connect to a phishing site, 70% of organizations had users that were served malicious browser ads, 69% of organizations experienced some level of unsolicited crypto mining, and 50% of organizations encountered ransomware-related activity.
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Restraint: Inability of AI to stop zero-day and advanced threats
All algorithms or approaches used in AI, including machine learning, genetics algorithm, deep learning, or neural networks, are based on experiences from the past. This means that AI in cybersecurity is based on learning from past malware about what malware looks like and how it behaves. A zero-day threat exploits an unknown computer security vulnerability. An advanced persistent threat (APT) is a network attack in which an unauthorized person gains access to a network and stays undetected for a long period of time. APTs are armed with new methods to invoke application programming interfaces (APIs) and innovative techniques to access system resources, while a few APT behaviors might be similar to past events that AIs can recognize them, new APTs have no past events. Real protection against sophisticated, advanced threats must not rely on prior malware or prior attacks. The inability of AI to stop advanced threats is thus acting as a restraint in this market.
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Opportunity: Zero trust framework providing advanced security
Zero trust is a framework that considers that the security of complex networks is constantly under threat from both internal and external threats. A zero-trust security model validates and authorizes each connection made by a user to an application or software that connects to a data set via an application programming interface (API). It helps organize and strategize a thorough approach to counter cyber threats. According to the zero-trust principle, no one or any application should ever be presumed to be trustworthy. According to the principle of least-privileged access, which is a cornerstone of zero trust, trust should be established in accordance with the context (such as user identification and location, endpoint security posture, and the app or service being requested), with policy checks at each level. It utilizes deep learning which reveals document meaning and context to provide accurate, granular categories that reflect business criticality. Once categorized, deep learning can establish a security baseline for each category. That baseline encompasses how files are permissioned, shared, stored, and managed, and it reflects the policy decisions made by the people who know those files best – the owners and end-users
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Challenge: Shortcomings of AI
AI is sometimes just useful for detecting threats; it does not always remove threats. However, issues can occur even when AI is used to detect threats as AI and ML are often positioned to perform anomaly detection, which highlights unknowns while also highlighting additional unknowns that are unrelated to security. To avoid this, security teams must constantly train the existing model using training data that includes a strong feedback loop. However, it requires additional effort and cost in addition to looking into the finding itself. The idea that artificial intelligence and machine learning can take the place of human judgment is a misconception. Human decision-making is unmatched. For instance, creating detection criteria based on attack paths, new vulnerabilities, and emerging threat intelligence involves context, research, and creativity. Being aware of impending attacks via research, replicating them, determining where detectability can occur across the stack, and building detections and playbooks are all distinctly human tasks that AI can assist with but cannot fully perform on its own.
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