The Artificial Intelligence (AI) Security Testing and Evaluation Platform market is experiencing rapid growth, driven by the increasing adoption of AI in various sectors and the concomitant need to ensure the security of these AI-powered systems. The market's expansion is fueled by several key factors. Firstly, the escalating sophistication of cyber threats targeting AI systems necessitates robust testing and evaluation methodologies. Secondly, regulatory compliance mandates for data security and AI ethics are driving the demand for comprehensive security assessments. Thirdly, the growing awareness among organizations regarding the potential vulnerabilities of AI models and their impact on critical infrastructure is pushing organizations to proactively invest in AI security testing solutions. We estimate the 2025 market size to be around $1.5 billion, considering the significant investments made in AI security and the high CAGR observed in related sectors. This figure is projected to grow substantially over the forecast period (2025-2033), with a CAGR of approximately 25%, reflecting continuous innovation in AI and cybersecurity technologies. The market is segmented by deployment (cloud, on-premise), by organization size (SMEs, large enterprises), and by application (network security, data security, application security). While cloud-based solutions dominate, on-premise deployments remain significant, particularly among organizations with stringent data security requirements. Large enterprises are the primary adopters, but increasing awareness among SMEs is driving their participation in the market. Key players like Microsoft, Synopsys, and CrowdStrike are actively investing in research and development, expanding their product portfolios, and forging strategic partnerships to consolidate their market positions. However, challenges such as the high cost of implementation, the scarcity of skilled professionals, and the complexity of integrating AI security testing into existing workflows are potential restraints. Nevertheless, the market is expected to overcome these challenges, driven by increasing technological advancements and growing demand for secure AI systems across all industries.

AI Vulnerability Scanning Market Outlook

The global AI Vulnerability Scanning market size was valued at USD 2.5 billion in 2023 and is expected to reach USD 7.8 billion by 2032, growing at a CAGR of 13.2% during the forecast period. The growing reliance on digital infrastructure and the increasing sophistication of cyber threats are significant factors driving this market's growth. As organizations continue to digitize their operations, the need to secure these systems against vulnerabilities becomes paramount, fostering the demand for advanced AI-based vulnerability scanning solutions.

One of the primary growth factors in the AI Vulnerability Scanning market is the exponential rise in the quantity and complexity of cyber threats. Traditional methods of identifying and mitigating vulnerabilities are often inadequate against modern, sophisticated attacks. AI technologies, with their capability to analyze vast amounts of data and identify patterns, provide a more robust and efficient means of vulnerability scanning. This has led to increased adoption of AI-based solutions across various sectors, further propelling market growth.

Another significant driver is the stringent regulatory environment regarding data protection and cybersecurity. Governments and regulatory bodies worldwide have implemented strict guidelines to safeguard sensitive information. Organizations are compelled to comply with these regulations, necessitating the adoption of advanced vulnerability scanning tools. AI-based solutions offer automated, real-time scanning capabilities, ensuring continuous compliance and thereby driving market demand.

The integration of AI with other emerging technologies such as Internet of Things (IoT) and edge computing is also contributing to market growth. As IoT devices proliferate, they present new avenues for cyberattacks. AI vulnerability scanning tools can provide continuous monitoring and quick detection of vulnerabilities in such complex environments. This integration is particularly beneficial for sectors like healthcare and manufacturing, where IoT adoption is high, thereby driving the demand for AI-based scanning solutions.

In the healthcare sector, the importance of Healthcare Vulnerability Scanning cannot be overstated. As healthcare organizations increasingly rely on digital systems to manage patient data and medical devices, the risk of cyber threats becomes more pronounced. Healthcare Vulnerability Scanning tools, powered by AI, provide essential protection by continuously monitoring systems for potential security gaps. This proactive approach not only helps in safeguarding sensitive patient information but also ensures compliance with stringent healthcare regulations such as HIPAA. The integration of AI in vulnerability scanning is particularly beneficial in identifying complex threats that traditional methods might miss, thereby enhancing the overall security posture of healthcare institutions.

Regionally, North America holds the largest market share due to its advanced technological infrastructure and high adoption rates of AI. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digitization of economies in this region, along with increasing government initiatives to enhance cybersecurity, is driving the adoption of AI vulnerability scanning solutions. Europe also presents significant opportunities due to stringent data protection laws and a high focus on cybersecurity.

Component Analysis

When analyzing the AI Vulnerability Scanning market by components, it is essential to examine both software and services. The software segment is anticipated to hold a larger market share due to the increasing adoption of AI-driven tools for automated vulnerability scanning. These software solutions provide real-time insights and predictive analytics, enabling organizations to proactively identify and address potential security threats. Continuous advancements in AI algorithms and machine learning models further enhance the capabilities of these software solutions, making them indispensable in the modern cybersecurity landscape.

On the other hand, the services segment is also experiencing significant growth, driven by the need for specialized expertise in deploying and managing AI-based vulnerability scanning solutions. Services such as consulting, implementation, and managed services are essential for organizations

Cybernative.ai Code Vulnerability and Security Dataset

  Dataset Description

The Cybernative.ai Code Vulnerability and Security Dataset is a dataset of synthetic Data Programming by Demonstration (DPO) pairs, focusing on the intricate relationship between secure and insecure code across a variety of programming languages. This dataset is meticulously crafted to serve as a pivotal resource for researchers, cybersecurity professionals, and AI developers who are keen on… See the full description on the dataset page: https://huggingface.co/datasets/CyberNative/Code_Vulnerability_Security_DPO.

In this study, we address potential source code vulnerabilities in some of the most commonly used IoT frameworks. We introduce IoTvulCode- a novel framework consisting of a dataset-generating tool, and ML-enabled methods for the detection of source code vulnerabilities and weaknesses as well as the initial release of an IoT vulnerability dataset. Our framework contributes to improving the existing coding practices, leading to a more secure IoT infrastructure. Additionally, IoTvulCode provides a solid basis for the IoT research community to explore the topic further.

Gene-gene chromatin interactions (GGIs) bring distal genes into close spatial proximity to permit strong co-expression, which could potentially contribute to cancer progression. High-throughput methods like Hi-C are impractical for very large cohort analyses, thus we developed AI4Loop, an Artificial Intelligence (AI) Deep Learning -based tool to predict GGIs using RNA-Seq data. Applying AI4Loop to 12,000 patient samples from the TCGA database across 32 cancer types revealed that GGIs show increased cancer sub-type predictivity compared to RNA-Seq data and demonstrated oncogenic gains of GGIs interaction in almost all cancers examined. To target the therapeutic vulnerability of gain of GGIs in cancers, using low-information RNA expression datasets from the CLUE database, we also constructed a drug-perturbation GGI atlas from 50,000 drug-treated samples to identify and repurposed compounds that disrupt oncogenic GGIs. Notably, we found that the antibiotics eperezolid and radezolid reduced cancer-acquired GGIs, which we confirmed with Hi-C experiment. This work showcases AI-directed research in epigenetics, enhances cancer biology predictivity and can promote wide-range drug repurposing in the future.

Generative AI Security Market Overview The global generative AI security market size was valued at USD 2879 million in 2025 and is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period. The growth is primarily attributed to the rising adoption of generative AI technologies across various industries, increasing concerns over data privacy and security, and the need for robust models to mitigate vulnerabilities in generative AI systems. Additionally, the increasing availability of datasets and advancements in computing power have further accelerated the growth of this market. Market Drivers, Trends, and Restraints Drivers:

Growing adoption of generative AI in industries such as entertainment, design, and technology Increasing concerns over data privacy and security, especially in the context of generative AI Demand for robust models to enhance security and prevent malicious use of generative AI

Trends:

Integration of generative AI with other security technologies, such as threat intelligence and intrusion detection systems Development of new techniques to detect and mitigate vulnerabilities in generative AI models Growing focus on regulatory compliance and standards for ethical use of generative AI

Restraints:

Limited understanding of generative AI security risks and vulnerabilities High cost of developing and deploying effective security measures Lack of skilled professionals in the field of generative AI security

Generative AI security is a rapidly evolving field that applies generative AI to enhance the security of various systems, applications, and data. This report provides a comprehensive overview of the generative AI security market, including its concentration, product insights, regional trends, driving forces, challenges, and emerging trends.

According to a 2023 survey of cybersecurity professionals, nearly ** percent of respondents worldwide considered improved threat detection as the most significant benefit of incorporating artificial intelligence into their cybersecurity operations. Improved vulnerability management ranked second, according to ** percent of respondents. Overall, over one-third of respondents saw the automation applications of AI as a way to ease talent shortage issues in cybersecurity operations. Generative AI is everywhere As a phenomenon that has taken the internet by storm, generative AI is increasingly being tested for business functions, including cybersecurity. Generative AI-powered solutions can search through vast amounts of data to identify abnormal behavior and detect malicious activity. Consequently, CEOs and IT professionals alike expect generative AI to be increasingly used to bolster cybersecurity, offering greater speed, accuracy, and cost-effectiveness. The other side of the coin Despite the security benefits of generative AI, there is the other side of the coin to account for, as the same advantages can also benefit hostile actors’ capabilities, such as phishing, malware development, and deepfakes. Looking forward, companies will have to adapt and stay up to speed so that generative AI does not end providing overall cyber advantage to attackers.

Artificial Intelligence in Cyber Security Market size was valued at USD 9.86 Billion in 2024 and is projected to reach USD 67.95 Billion by 2031, growing at a CAGR of 30.10% from 2024 to 2031.

Global Artificial Intelligence in Cyber Security Market Drivers

Increasing Sophistication of Cyber Attacks: With cyber threats becoming more advanced, AI-powered systems help detect, predict, and respond to attacks that traditional security measures cannot handle. According to the FBI's Internet Crime Complaint Center (IC3), cybercrime reports increased by 69.4% in 2020, with reported losses exceeding USD 4.2 billion.

Growing Adoption of IoT and Cloud Technologies: According to (ISC)², the global cybersecurity workforce gap stands at 3.4 million professionals, with 63% of organizations reporting a shortage of IT security staff. The National Institute of Standards and Technology (NIST) reports that organizations using AI-powered security tools can handle 63% more security incidents with the same staff size. As more devices become interconnected through the Internet of Things (IoT) and cloud platforms, organizations require AI-driven solutions to safeguard against vulnerabilities.

Rising Data Breaches and Compliance Regulations: Stringent regulations like GDPR and CCPA push organizations to adopt AI-based cyber security solutions to comply with data protection laws and mitigate risks.

Shortage of Skilled Cyber Security Professionals: AI-driven solutions automate many security functions, addressing the shortage of skilled cybersecurity professionals and streamlining threat detection and response processes.

The Security and Vulnerability Management (SVM) market is experiencing robust growth, projected to reach $14.45 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 7.50% from 2025 to 2033. This expansion is fueled by the increasing sophistication and frequency of cyberattacks targeting businesses across diverse sectors. The rising adoption of cloud computing and the Internet of Things (IoT) further exacerbates vulnerabilities, driving demand for comprehensive SVM solutions. Large enterprises, with their extensive IT infrastructure and sensitive data, represent a significant market segment, followed by small and medium enterprises (SMEs) increasingly recognizing the need for robust security measures. The BFSI (Banking, Financial Services, and Insurance), healthcare, and manufacturing sectors are particularly active in adopting SVM solutions due to stringent regulatory compliance requirements and the sensitive nature of their data. Competition is fierce, with established players like Qualys, Hewlett Packard Enterprise, and McAfee alongside emerging innovative companies shaping the market landscape. Geographic distribution reflects global digital transformation, with North America and Europe currently holding significant market shares, but Asia and other regions showing rapid growth potential. The continued growth trajectory is projected to be influenced by several factors. The increasing prevalence of ransomware attacks, data breaches, and regulatory penalties will incentivize organizations to invest more heavily in SVM. Advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of SVM tools, enabling faster threat detection and response. However, challenges remain, including the skills gap in cybersecurity professionals, the complexity of managing diverse security tools, and the rising costs associated with implementing and maintaining comprehensive SVM solutions. The market will likely see further consolidation, with mergers and acquisitions among players seeking to expand their product portfolios and market reach. The focus will shift towards integrated solutions offering comprehensive security capabilities and proactive threat management. Recent developments include: June 2024 - CrowdStrike has announced a strategic partnership with HPE innovation, including large language models (LLMs), accelerated by NVIDIA, With the integration of the CrowdStrike Falcon Cyber security platform and HPE GreenLake cloud and OpsRamp AIOps, organizations can seamlessly unify IT operations and cybersecurity in a single, unified process to securely accelerate AI innovation., September 2023 - IBM has revealed the enhancement of the IBM Cloud Security and Compliance Center to assist businesses in managing risk and safeguarding data in their hybrid, multicloud environments and workloads. When clients seek to deal with new supply chain threats and adapt to changing global regulations, the suite of solutions helps to enhance their resilience, performance, security, and compliance, all while reducing operational expenses.. Key drivers for this market are: Increasing Number of Cyber Attacks, Growing Adoption of Cloud Computing by Enterprises. Potential restraints include: Increasing Number of Cyber Attacks, Growing Adoption of Cloud Computing by Enterprises. Notable trends are: BFSI Segment is Expected to Hold the Major Market Share.

Generative AI Security Market Outlook

According to our latest research, the global Generative AI Security market size stood at USD 1.98 billion in 2024, reflecting robust momentum driven by the rapid integration of generative AI technologies across industries. The market is projected to expand at a CAGR of 28.1% from 2025 to 2033, reaching a forecasted value of USD 17.54 billion by 2033. This exceptional growth is underpinned by the escalating adoption of generative AI tools and the surging need for advanced security solutions to mitigate emerging AI-driven threats. As organizations increasingly leverage generative AI for innovation and automation, the imperative to secure these systems propels the market forward, making generative AI security a critical investment area for enterprises worldwide.

The primary growth driver for the generative AI security market is the exponential increase in the deployment of generative AI models across business processes and digital ecosystems. Organizations are leveraging generative AI for content creation, data analysis, and automation, but these advancements also introduce new vectors for cyber threats, such as data poisoning, model inversion, and adversarial attacks. The sophistication of these threats necessitates equally advanced security frameworks, prompting firms to invest in specialized generative AI security solutions. Moreover, the rising number of high-profile breaches involving AI-generated content and deepfakes has heightened awareness among both enterprises and regulators, further accelerating demand for robust generative AI security platforms.

Another significant factor fueling market growth is the tightening regulatory landscape surrounding AI and data security. Governments and industry bodies across North America, Europe, and Asia Pacific are introducing stringent compliance requirements to safeguard sensitive data processed by AI systems. These regulations mandate organizations to implement advanced security protocols, including real-time monitoring, threat detection, and automated response mechanisms specifically tailored for generative AI environments. Additionally, the growing emphasis on ethical AI usage and transparency compels organizations to adopt security solutions that not only protect data but also ensure the integrity and accountability of AI-generated outputs. This regulatory pressure, combined with increasing consumer expectations for privacy and trust, is a key catalyst for sustained market expansion.

The proliferation of cloud-based generative AI solutions is also reshaping the security landscape, creating both opportunities and challenges for market stakeholders. Cloud deployments offer scalability and flexibility, enabling organizations to rapidly experiment with and deploy generative AI models. However, this shift also exposes enterprises to new security risks, including multi-tenant vulnerabilities, data leakage, and unauthorized access to AI models and training data. As a result, there is a surge in demand for cloud-native generative AI security solutions that can provide end-to-end protection across distributed environments. Vendors are responding with innovations in secure model deployment, encryption, and access control, driving the evolution of the market and reinforcing the need for specialized expertise in generative AI security.

Regionally, North America continues to dominate the generative AI security market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States leads in both adoption and innovation, supported by a mature technology ecosystem and proactive regulatory initiatives. Europe is witnessing rapid growth due to the enforcement of GDPR and AI Act regulations, while Asia Pacific is emerging as a high-growth region driven by digital transformation initiatives in China, Japan, and India. Each region presents unique opportunities and challenges, with local market dynamics, regulatory frameworks, and industry verticals shaping the trajectory of generative AI security adoption.

Component Analysis

The generative AI security market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall security architecture. The software segment dominates the market, accounting for the highest revenue share in 2024, as organizations prioritize investment in advanced security platforms, threat detection tools, and AI-driven analytics. These software so

The Risk-Based Vulnerability Management (RBVM) tool market is experiencing robust growth, driven by the increasing frequency and sophistication of cyberattacks and stringent regulatory compliance requirements. The market, estimated at $8 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $25 billion by 2033. This growth is fueled by several key factors. Organizations are increasingly adopting a risk-based approach to vulnerability management, prioritizing the remediation of critical vulnerabilities that pose the greatest threat to their business operations. Furthermore, the rising adoption of cloud computing and the expanding attack surface it creates is driving demand for comprehensive RBVM solutions that can effectively secure both on-premises and cloud-based environments. The integration of Artificial Intelligence (AI) and Machine Learning (ML) in RBVM tools is also contributing to improved vulnerability detection, prioritization, and remediation, boosting market expansion. The market is segmented by deployment (cloud, on-premises), organization size (small and medium-sized enterprises (SMEs), large enterprises), and industry vertical (BFSI, healthcare, retail, government, IT/telecom). While the cloud-based deployment segment is currently dominating due to its scalability and cost-effectiveness, on-premises deployments retain a significant market share, primarily among large enterprises with stringent data security and compliance requirements. Competitive intensity is high, with established players like Qualys, Rapid7, and Tenable competing alongside emerging innovative companies. However, the market presents significant opportunities for specialized niche players focusing on specific industries or leveraging advanced technologies like AI/ML for improved vulnerability management capabilities. The primary restraints on market growth include the high initial investment costs associated with implementing RBVM solutions, the shortage of skilled cybersecurity professionals, and the complexity of integrating these tools with existing security infrastructure.

VAITP Vulnerability Corpus

  Dataset Description

The DeVAITP Vulnerability Corpus is a curated collection of 1,406 Python code files, each corresponding to a real-world vulnerability documented in the Common Vulnerabilities and Exposures (CVE) database. This dataset was developed to support research in automated security analysis, vulnerability detection, and AI-driven vulnerability injection. Each file in this dataset represents the patched or non-vulnerable version of the… See the full description on the dataset page: https://huggingface.co/datasets/FBogaerts/DeVAITP.

Device Vulnerability Management Market Outlook

The global device vulnerability management market size was valued at USD 3.4 billion in 2023 and is projected to reach USD 7.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.3% during the forecast period. This market growth is primarily attributed to the escalating cyber threats and complexities in the IT infrastructure landscape, which necessitate robust vulnerability management solutions. The increasing adoption of Internet of Things (IoT) devices across various sectors has exacerbated the potential attack surface, thereby driving the demand for advanced vulnerability management solutions to ensure security and compliance.

One of the primary growth factors for the device vulnerability management market is the proliferation of cyber-attacks and the rising sophistication of threat actors. As organizations continue to embrace digital transformation, the risk of cyber threats increases, necessitating advanced solutions to protect sensitive data and infrastructure. The growing use of cloud services and remote working models, accelerated by the COVID-19 pandemic, has further amplified security challenges. Organizations are increasingly recognizing the importance of proactive vulnerability management to mitigate potential threats, leading to a surge in demand for reliable solutions that can detect, assess, and remediate vulnerabilities in real-time.

Technological advancements such as artificial intelligence (AI) and machine learning (ML) are playing a crucial role in enhancing the capabilities of vulnerability management solutions. These technologies enable automated threat detection and response, reducing the time required to identify and mitigate vulnerabilities. AI-powered solutions are capable of analyzing vast amounts of data to detect patterns and predict potential security breaches, thereby improving the efficiency and effectiveness of vulnerability management processes. The integration of AI and ML into vulnerability management solutions is expected to drive market growth further, as organizations seek to bolster their security posture.

Regulatory compliance requirements are also a significant driver of market growth. Governments and regulatory bodies worldwide are implementing stringent data protection laws and regulations, compelling organizations to adopt comprehensive vulnerability management solutions. Failure to comply with these regulations can result in severe penalties and reputational damage, prompting organizations to prioritize security measures. As a result, there is an increasing demand for solutions that can provide continuous monitoring, risk assessment, and compliance reporting to meet regulatory standards.

The regional outlook for the device vulnerability management market indicates significant growth potential across various geographies. North America is expected to dominate the market, owing to the presence of major technology companies, advanced IT infrastructure, and a high level of awareness regarding cybersecurity. The Asia Pacific region is anticipated to witness the highest growth rate, driven by rapid digitalization, increasing cyber threats, and government initiatives promoting cybersecurity measures. Europe also presents promising growth opportunities, with stringent data protection regulations and a strong focus on cybersecurity driving the demand for vulnerability management solutions.

Vulnerability Assessment is a critical component of any robust security strategy, particularly in the context of the growing complexity of IT environments. As organizations increasingly rely on interconnected systems and IoT devices, the potential for security breaches expands. A thorough vulnerability assessment helps identify potential weaknesses in an organization's infrastructure, enabling proactive measures to be taken before these vulnerabilities can be exploited by malicious actors. By systematically evaluating the security posture of their systems, organizations can prioritize remediation efforts, ensuring that the most critical vulnerabilities are addressed promptly. This proactive approach not only enhances security but also supports compliance with regulatory requirements, which often mandate regular assessments of security controls and practices.

Component Analysis

The device vulnerability management market is segmented into software and services components, each playing a vital role in the ecosystem. The software segment is projected to hold

Security and Vulnerability Management Market Outlook

According to our latest research, the global Security and Vulnerability Management market size reached USD 17.8 billion in 2024, driven by the escalating sophistication of cyber threats and the increasing adoption of digital transformation initiatives across all major industries. The market is demonstrating a robust growth trajectory, registering a CAGR of 9.2% from 2025 to 2033. By 2033, the Security and Vulnerability Management market is forecasted to attain a value of USD 39.3 billion. This expansion is primarily fueled by the growing need for proactive security solutions, regulatory compliance mandates, and the proliferation of remote and hybrid work environments, which have accentuated the importance of comprehensive vulnerability management frameworks.

The growth of the Security and Vulnerability Management market is being significantly propelled by the exponential rise in the frequency and complexity of cyberattacks targeting organizations of all sizes. As digital infrastructures expand and become more interconnected, the attack surface for malicious actors widens, making it imperative for enterprises to invest in advanced security and vulnerability management solutions. The increasing adoption of Internet of Things (IoT) devices, cloud computing, and mobile technologies introduces new vulnerabilities that require continuous monitoring and remediation. Organizations are recognizing the critical necessity of integrating vulnerability management into their overall cybersecurity strategies to minimize potential breaches and ensure business continuity. This heightened awareness is shifting vulnerability management from a reactive to a proactive discipline, further accelerating market demand.

Another core growth driver for the Security and Vulnerability Management market is the stringent regulatory landscape across various industry verticals. Governments and regulatory bodies worldwide are imposing rigorous data protection and cybersecurity compliance requirements, such as GDPR in Europe, HIPAA in the United States, and other region-specific mandates. Non-compliance can result in substantial financial penalties and reputational damage, compelling organizations to adopt comprehensive security and vulnerability management solutions. These regulatory pressures are particularly pronounced in sectors like BFSI, healthcare, and government, where the protection of sensitive data is paramount. As regulations continue to evolve and expand, the demand for automated, scalable, and efficient vulnerability management platforms is expected to surge, ensuring compliance and reducing operational risks.

The market is also benefitting from technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) within security and vulnerability management tools. These innovations enable real-time threat detection, predictive analytics, and automated remediation processes, significantly enhancing the effectiveness of security operations. AI-driven solutions can rapidly analyze vast amounts of security data, identify patterns indicative of potential vulnerabilities, and prioritize remediation efforts based on risk assessment. This technological evolution not only improves the speed and accuracy of vulnerability management but also addresses the growing shortage of skilled cybersecurity professionals by automating routine tasks. As organizations strive for more efficient and resilient security postures, the adoption of intelligent vulnerability management platforms is expected to become increasingly prevalent.

Regionally, North America holds the largest share of the Security and Vulnerability Management market, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to the high concentration of technology-driven enterprises, robust cybersecurity spending, and the early adoption of advanced security solutions. Europe’s market growth is underpinned by stringent data privacy regulations and increasing investments in digital infrastructure. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid digitalization, expanding internet penetration, and rising awareness about cybersecurity threats among enterprises. The region’s dynamic economic landscape and increasing government initiatives to bolster cybersecurity frameworks are expected to drive significant market expansion over the forecast period.

The AI in Cybersecurity Market offers a diverse range of products harnessing the power of AI to bolster cybersecurity defenses. Key product categories include: Network Security: AI-powered network security solutions provide real-time threat detection and prevention by analyzing network traffic patterns, identifying anomalies, and automatically blocking malicious activity. This proactive approach minimizes disruptions and protects critical network assets. Endpoint Security: AI-driven endpoint security solutions safeguard individual devices (laptops, desktops, mobile devices) against malware, viruses, and other threats. These solutions utilize advanced machine learning algorithms to detect and respond to suspicious behavior, preventing breaches at the endpoint level. Application Security: AI significantly enhances application security by automatically identifying and mitigating vulnerabilities, detecting anomalies in application behavior, and preventing unauthorized access attempts. This proactive approach reduces the attack surface and protects sensitive application data. Cloud Security: AI plays a critical role in securing cloud environments by detecting and responding to threats across various cloud platforms. It offers features such as anomaly detection, threat intelligence, and automated incident response to enhance cloud security posture. Data Loss Prevention (DLP): AI-powered DLP solutions are used to identify and prevent sensitive data from leaving the organization's control. They utilize machine learning to identify patterns and anomalies in data usage, ensuring data remains within the specified boundaries. Recent developments include: November 2020: IBM and AMD collaborated to improve their cybersecurity and AI products.. Notable trends are: Increasing AI technologies, such as natural language processing and machine learning is driving the market growth.

215296273/2021/12As chatbots become more popular, the insurance industry has adopted their use. Although chatbot has been used a lot in customer relationship management (CRM), there is a lack of data security and privacy control strategies for data in chatbots. During data exchange, the client's data may be compromised through computer security breaches, thus exposing the client to possible fraud and theft. The lack of data security and privacy control strategies for data in chatbots has become a major security concern in financial services institutions. Chatbots access a lot of company and client information and that makes the data contained in chatbots to be the target of hackers which can cause harm to companies and customers.This study explored how data security in chatbots in South African insurance organisations can be attained. To realise the aim of this study, five objectives were formulated as follows, to: 1) identify the potential use cases of chatbots for CRM in a South African insurance organisation; 2) identify the challenges of securing data in a chatbot in a South African insurance organization; 3) determine the security goals, threats, and vulnerabilities associated with the use of chatbots in a South African insurance organisation; 4) develop a threat model for the security and privacy of data in chatbots for a South African insurance organization; and 5) evaluate the threat model for security and privacy of data in the chatbots for a South African insurance organisation.The mixed-methods research methodology was adopted for the study. A case study research strategy that involved data collection from a South African insurance company was used. Semi-structured interviews were conducted with participants that were purposively selected. Also, the STRIDE modelling approach was used to collect data on the security threats and vulnerabilities that pertain to each insurance use case with for each component of STRIDE — Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. Based on the outcome of the STRIDE modelling, a threat model for data security in chatbots for the South African insurance industry was developed using the Attack Defence tool. The threat model reveals the data security threats in chatbots, and how they can be mitigated. An evaluation of the threat model was conducted using security experts who assessed the quality of the threat model. They also provided qualitative feedback on the threat model. The evaluation of the threat model adopted the System Usability Scale (SUS) questionnaire which is a standard questionnaire to evaluate a system or product. The SUS score for each evaluator was calculated, and a mean SUS score was obtained.From the expert evaluation, the developed threat model for data security in insurance chatbots obtained a mean SUS of 79.4 which corresponds to a grade B rating, which is a good rating based on the rules for the SUS scores. From the qualitative feedback, the security experts observed that the threat model can help to improve overall security and protect against potential attacks, and also proactively identify and mitigate potential threats in chatbots.The insurance industry and academia will benefit from this study. Insurance organisations can implement security using the proposed threat model for the security of data in their business chatbots. Also, this study contributes new information to the body of knowledge since this is the first study to develop a threat model for data security in

The Vulnerability Assessment Tools Market, valued at approximately XXX million in 2025, is projected to expand dramatically, reaching an estimated XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). This growth is driven by the rising need for robust cybersecurity measures amidst increasing cyber threats, along with the growing adoption of cloud services and the proliferation of connected devices. Furthermore, the increasing regulatory mandates and data privacy concerns are also acting as key drivers for the market's expansion. Key market trends include the growing popularity of cloud-based vulnerability assessment tools, the integration of artificial intelligence (AI) and machine learning (ML) technologies to enhance detection accuracy, and the increasing demand for vulnerability management solutions from small and medium-sized enterprises (SMEs). Key market players in the Vulnerability Assessment Tools Market include Acunetix, SecPod SanerNow, Intruder, Invicti, Astra, SolarWinds, Nessus Professional, ManageEngine Vulnerability Manager Plus, Aircrack, and OpenVAS. North America and Europe are expected to account for major market shares, while Asia-Pacific is anticipated to witness the fastest growth rate during the forecast period. The Vulnerability Assessment Tool market is rapidly growing, with the market size expected to reach $1.5 billion by 2025. This growth is being driven by the increasing number of cyberattacks and the need for organizations to protect their data and systems from these attacks. Vulnerability assessment tools are essential for organizations of all sizes. These tools can help organizations identify and fix vulnerabilities in their systems before they can be exploited by attackers. This can help organizations reduce the risk of data breaches, financial losses, and damage to their reputation. There are a number of different vulnerability assessment tools available, each with its own unique features and capabilities. Organizations should choose a tool that is appropriate for their specific needs and budget.

The Data Breach and Attack Simulation (DBAS) solution market is experiencing robust growth, driven by the escalating frequency and sophistication of cyberattacks targeting diverse sectors. The market, currently valued at approximately $2 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% over the forecast period (2025-2033). This growth is fueled by increasing regulatory compliance mandates (like GDPR and CCPA) necessitating robust security testing, rising awareness of vulnerabilities within organizations, and the adoption of cloud-based and hybrid infrastructure which introduce new attack vectors. Key segments driving market expansion include the financial sector, government affairs, and healthcare, demanding sophisticated simulations to protect sensitive data. The configuration management, patch management, and threat management functionalities within DBAS solutions are witnessing significant demand, reflecting a shift towards proactive security measures rather than reactive incident response. While the market faces some restraints, such as the high initial investment costs for implementing DBAS solutions and the need for skilled cybersecurity professionals, the overall growth trajectory remains positive due to heightened security concerns and the increasing value placed on data protection. The competitive landscape is fragmented, with a mix of established players and emerging companies vying for market share. Companies like Qualys, Rapid7, and AttackIQ are leading the charge, offering comprehensive DBAS solutions. However, the emergence of niche players focused on specific sectors or functionalities presents opportunities for specialization and innovation. Geographical expansion is also a key driver, with North America currently dominating the market due to strong regulatory frameworks and high cybersecurity awareness. However, Asia-Pacific and Europe are emerging as promising markets, driven by rising digitalization and increasing adoption of advanced security technologies. The continued expansion into emerging markets and ongoing technological advancements in areas such as Artificial Intelligence (AI) and Machine Learning (ML) integration within DBAS solutions will further fuel market growth in the coming years, leading to more sophisticated and effective cybersecurity posture assessments.

Analysis of ‘COVID-19 Community Vulnerability Crosswalk - Rank Ordered by Score’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c4cab2a7-e11a-4a58-9d95-31be7173f39f on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This crosswalk weighs communities down to the census tract with COVID-19 data, considering disease morbidity and mortality, as well as social and economic contextual data. To do this, this spreadsheet crosswalks four key open datasets: Hardest Hit Area, Low Income Area, Tribal Community, and Rural Community, and uses the FCC's scoring methodology to weigh them to provide an evaluation of the most vulnerable communities in the country.

--- Original source retains full ownership of the source dataset ---

AI in Cybersecurity Market Outlook

According to our latest research, the global AI in Cybersecurity market size reached USD 22.1 billion in 2024, reflecting robust adoption across diverse industries. The market is set to expand at a compelling CAGR of 21.7% from 2025 to 2033. By the end of 2033, the AI in Cybersecurity market is projected to attain a value of USD 161.5 billion. This remarkable growth is primarily fueled by the escalating sophistication of cyber threats, the exponential increase in digital transformation initiatives, and the rising need for advanced threat detection and response capabilities within organizations globally.

Several key factors are propelling the growth of the AI in Cybersecurity market. Firstly, the surge in cyberattacks targeting critical infrastructure, financial institutions, and government agencies has heightened the demand for advanced security solutions. AI-driven cybersecurity tools are increasingly being deployed to analyze vast datasets, identify anomalies, and respond to threats in real-time, thereby reducing the response time to zero-day vulnerabilities and minimizing potential damages. Additionally, the proliferation of connected devices and the expansion of the Internet of Things (IoT) ecosystem have expanded the attack surface for cybercriminals, making traditional security measures inadequate. AI-powered solutions are uniquely positioned to address these challenges by leveraging machine learning algorithms for behavioral analytics, intrusion detection, and automated threat intelligence, which are crucial for modern digital environments.

Another significant growth driver is the widespread adoption of cloud computing and digital transformation strategies across enterprises of all sizes. As businesses migrate workloads to the cloud and embrace remote work models, the complexity and volume of data requiring protection have increased exponentially. AI in cybersecurity enables organizations to automate security operations, enhance incident response, and ensure compliance with evolving regulatory frameworks. The integration of AI with security information and event management (SIEM) tools, endpoint detection and response (EDR) systems, and identity and access management (IAM) platforms is rapidly becoming a standard practice, further fueling market expansion. Enterprises are also investing in AI-enabled security analytics to gain actionable insights and proactively mitigate emerging threats.

Furthermore, the growing emphasis on data privacy and stringent regulatory mandates such as GDPR, CCPA, and other regional data protection laws are compelling organizations to adopt advanced AI-driven cybersecurity solutions. These regulations require enterprises to implement real-time monitoring, rapid breach detection, and transparent incident reporting, all of which are areas where AI excels. The ability of AI in cybersecurity to automate compliance checks, detect insider threats, and prevent data exfiltration is increasingly recognized as a critical asset for enterprises operating in highly regulated sectors such as BFSI, healthcare, and government. The convergence of AI and cybersecurity is thus becoming indispensable for organizations aiming to safeguard sensitive data and maintain business continuity in an ever-evolving threat landscape.

Regionally, North America continues to dominate the AI in Cybersecurity market due to its advanced technological infrastructure, high concentration of cybersecurity vendors, and proactive government initiatives. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid digitalization, increasing investment in cybersecurity, and a rising number of cyber incidents. Europe also holds a significant market share, supported by strong data protection regulations and growing awareness of cyber risks. Latin America and the Middle East & Africa are emerging markets, with increasing adoption of AI-powered security solutions in response to rising cyber threats and digital transformation efforts. The global landscape is thus characterized by dynamic regional trends and diverse growth opportunities.

Component Analysis

The Component segment in the AI in Cybersecurity market is categorized into software, hardware, and services. Software solutions dominate this segment, accounting for the largest share in 2024, as organizations prioritize the deployment of AI-powered platforms for threat detection, behavioral analysis, and autom