Generative AI Cybersecurity Market Size, Share & Growth Report, 2026–2034
REPORT DETAILS
Generative AI Cybersecurity Market Summary
The global generative AI cybersecurity market size was valued at USD 2.58 billion in 2025, growing at a CAGR of 21.6% from 2026 to 2034. Adoption of artificial intelligence across industries and regulatory & compliance pressures drive the market growth. Further, advanced technology consolidation and rising incidence of cyber threats fuels the market expansion.
Market Statistics
Generative AI Cybersecurity Market Key Takeaways
- North America held the largest share of 39.8% in 2025. This is due to its high enterprise cybersecurity spending and strong adoption of advanced AI-driven threat detection solutions.
- The Asia Pacific market is expected to grow the fastest at a 24.3% CAGR during the forecast period. The regional market growth is driven by rapid digital transformation and increasing investments in cybersecurity infrastructure.
- The threat detection & analysis segment led with a 36.7% market share in 2025. The segment’s dominance is attributed to its proactive role in identifying zero-day threats and detecting complex cyberattacks.
- The reinforcement learning segment is anticipated to record a significant CAGR of 25.8% during the forecast period. The growth is fueled by its ability to autonomously adapt to evolving cyberattack strategies.
- The BFSI sector dominated with a 31.9% market share in 2025. This is owing to its critical requirement for advanced security to address rising financial fraud and stringent data protection demands.
Note: Figures and projections outlined in this report are the result of Polaris Market Research’s proprietary analytical processes, grounded in the latest available datasets and market observations.
What Is the Generative AI Cybersecurity Market?
Generative AI cybersecurity involves the use of generative AI models to detect, predict, analyze, and protect against evolving cyber threats. The growing threat landscape makes conventional security measures inadequate because companies begin to incorporate AI in their work. In such a situation, there is an increasing need for new cybersecurity solutions that can handle sophisticated threats.
Generative AI cybersecurity refers to the application of generative artificial intelligence models to automatically identify, predict, analyze, and counter evolving cyber threats through adaptive and intelligent defense mechanisms. Expanding attack surface makes traditional security methods insufficient as enterprises integrate AI into their operations. In this scenario, there is a rising demand for advanced cybersecurity measures capable of handling refined attacks.
Attackers are using GenAI to create credible phishing emails, malware, and fake news. Rising incidents of such attacks propel the demand for cybersecurity practices. According to the FBI March 2025 report, the Internet Crime Complaint Center (IC3) received 452,868 complaints in 2025, and total potential losses reached USD 17.69 billion. Cyber-enabled frauds accounted for about 85% of all losses reported to IC3 in 2025. GenAI offers an adaptive solution against the ever-evolving threats, by creating countermeasures that can automate responses. With this ability, businesses in different industries, including banking, health, retail, and manufacturing, can safeguard themselves from the evolving cyber threats (Source: ic3.gov).
How Generative AI Cybersecurity Works
The idea of generative AI in cybersecurity relies on the concept of utilizing AI to learn and generate information. AI-based solutions enable specialists to process large amounts of data in emails and networks to protect systems and respond to threats automatically. For instance, the program can recognize a phishing email or a network attack and alert the security department. It also recommends particular actions to neutralize threats and automatize some routine tasks. Notably, malicious actors can use the same technology to create phishing emails or other types of cyber threats. That is why it is essential to invest in robust passwords, protection software, and other security measures.
There is increasing pressure associated with regulatory compliance among businesses across different countries. Such compliance requirements boost the demand for innovative generative AI for cybersecurity solutions. Enterprises are implementing sophisticated cybersecurity frameworks. This helps them meet increasingly stringent data protection regulations, industry-specific guidelines, and global standards. Autonomous cybersecurity systems enable automated compliance tracking, risk management, and incident reporting. Such a system relieves cybersecurity teams of these responsibilities. Also, through their capacity to produce timely insights and adaptive cyber defense strategies, enterprises can comply proactively with regulatory demands.
AI vs Traditional Cybersecurity
| Factors | AI-Based Cybersecurity | Traditional Cybersecurity |
| Threat Identification Technique | Uses machine learning and behavior pattern techniques for identifying threats and abnormalities | Primarily depends on rule-based techniques, signature-based techniques, and known threat databases |
| Response Rate | Makes real-time identification and mitigation of security threats | Depends upon human investigation and intervention for security measures |
| Detection of New Threats | Identifies new threats that have not yet been discovered, such as zero-day threats | Limited effectiveness in dealing with new threats |
| Automation Capabilities | Increases automation in monitoring, detection, and security response process | Manual intervention and monitoring are critical to its success |
| Scalability | Can manage the volume and complexity of security data easily | Becomes hard to manage with increasing complexity and volume of data |
| Operational Efficiency | Helps in reducing the work of analysts due to automation capabilities | Needs regular updating and monitoring to be effective |
Source: Polaris Market Research Analysis
Market Dynamics
Driver: Advanced Technology Consolidation
Advanced technology consolidation is driving the generative AI cybersecurity market as organizations aim for integrated, unified platforms to manage increasingly complex security environments. The fragmented nature of cybersecurity solutions tends to create gaps that are exploited by cybercriminals. In such scenarios organizations rely on various digital tools, cloud computing systems, and Internet-of-Things (IoT) devices. GenAI can consolidate technologies such as automation, predictive analytics, and machine learning into a stronger cybersecurity system. In July 2026, Microsoft launched its AI model, MAI-Cyber-1-Flash, for cybersecurity. The model integrates various technologies to automate bug detection and remediation using teams of AI agents. Technology consolidation helps organizations improve their operational efficiency and increase visibility within their networks. It also improves threat detection and management. Therefore, by avoiding redundancy and promoting compatibility, technology consolidation strengthens overall resilience while optimizing resource utilization. Thus, it is a key driver of generative AI adoption in cybersecurity (Source: pluang.com).
Driver: Rising Cyber Threats
The increasing cybersecurity threats play an important role in prompting the use of these cybersecurity measures due to the growing intensity of cyberattacks. Cybercriminals are using automation technologies, AI, and advanced methods to conduct cyber-attacks, which compels the firms to develop equally advanced defense systems to counter the attacks. As stated in a CSIS report, in April 2025, a hacker breached a U.S. bank regulatory body and obtained information about more than 150,000 emails from 103 individuals for more than a year. In this case, the infiltration, through a hacked admin account, revealed very sensitive financial information, fueling the need for generative AI in cybersecurity. The ability of GenAI technology to identify and create probable attacks is an important approach to being one step ahead of the hacker since it will help in evaluating the system’s vulnerability. GenAI constantly studies patterns of emerging threats, ensuring that defense strategies evolve alongside attack methodologies. Due to this adaptability, enterprises are preferring GenAI in cybersecurity solution (Source: csis.org).
Challenges: Data Privacy and Security Concerns
The issue of data privacy and security is restraining the market growth. Generative AI requires a huge amount of data for its correct functioning and achieving the needed result. At the same time, this data may contain valuable company information, customers’ data, and even country’s security data. In case of a data breach, this information could be misused, stolen, or shared further without consent. Therefore, companies may be afraid of adopting this technology and investing in its development. The firms should ensure proper data encryption, protection, and privacy for Generative AI security. The issue may affect the speed of cybersecurity companies’ growth and development substantially.
Restraint: High Implementation Cost and Lack of Skilled Professionals
The high cost of implementing generative artificial intelligence cybersecurity solutions is limiting the growth of the market. The companies require expensive technologies and advance infrastructure for adopting such solutions. Moreover, they would need skilled professionals and regular software updates to maintain the systems efficiently. The small and medium-sized enterprises may be unable to afford such expenses and hire experts in the field of cybersecurity and AI. Additionally, training programs for educating employees about the effective usage of such tools can be time-consuming and expensive. Consequently, several organizations may refrain from considering generative AI solutions for their cybersecurity applications, which, in turn, restrain the growth of the generative AI in cybersecurity market.
Opportunity: Growing Focus on Securing AI Models & LLM Security
Growing popularity of generative AI and large language models (LLMs) in the field of cybersecurity will propel the market expansion. Organizations are exposed to multiple threats, including data poisoning, prompt injection, tampering of models, unauthorized access, leaking sensitive data, and adversary attacks. Thus, organizations have started to pay more attention to ensuring security of their AI and LLMs through continuous monitoring, securing access, data validation, filtering outputs, and testing security of the systems. Growing importance of secure-by-design guardrails of AI and LLM is opening up many business opportunities for vendors providing security of AI and LLMs.
Opportunity: Emergence of AI-Powered Security Operations & Agentic AI (AI-SOC)
The increasing complexity and volume of cyber threats are driving organizations to adopt AI-powered security operations and agentic AI capabilities within security operations centers (SOCs). AI-driven SOC systems leverage the use of generative AI, machine learning, and autonomous AI agents to analyze huge amounts of security data, detect anomalies, correlate alerts, investigate threats, and facilitate incident response. Traditional security products are mainly geared toward alert generation. But in case of cybersecurity and agentic AI, they can operate across multiple steps without human intervention. They prioritize incidents, suggest remediation steps, and coordinate workflows among security platforms. The integration of AI agents into security information and event management (SIEM), extended detection and response (XDR), security orchestration, automation and response (SOAR), and threat intelligence platforms can help alleviate alert fatigue and expedite time-to-detect and time-to-response. As enterprises seek to address cybersecurity skills shortages and improve SOC efficiency, demand for AI-driven and SOC automation is expected to increase. The increasing adoption of AI agents for threat response would create opportunities for vendors developing secure, explainable, and human-supervised agentic AI solutions capable of operating across increasingly complex cybersecurity environments.
Generative AI Cybersecurity Use Cases
Recently, there has been an increasing use of generative AI in practice to detect and respond to cyber threats in a better way. Many organizations use it for detecting phishing emails, analyzing suspicious events, creating and presenting reports, and many other tasks. Thus, it can reduce the workload of cybersecurity specialists and allow them to do more important tasks.
| Use Cases | Primary Industry | Outcome It Enables |
| Real-time phishing email detection | IT & Telecom, all sectors | Faster identification of AI-generated phishing and social-engineering attempts |
| Automated ransomware detection & response | Manufacturing, Healthcare | Containment before encryption completes, reduced dwell time |
| Fraud & anomaly detection in transactions | BFSI | Real-time flagging of suspicious payment and account activity |
| Network intrusion prevention | Enterprises, Energy & Utilities | Earlier detection of lateral movement across networks |
| Malware pattern prediction | IT & Telecom | Proactive blocking of malware variants before wide distribution |
| Automated compliance & incident reporting | Government & Defense, BFSI | Faster regulatory reporting with reduced analyst workload |
| LLM application security (prompt-injection defense) | Any org deploying GenAI apps | Protection of AI systems themselves from manipulation and data leakage |
Source: Polaris Market Research Analysis
Segmental Insights
Type Analysis
Based on type, the segmentation includes threat detection & analysis, adversarial defense, insider threat detection, network security, and others. Threat detection and analysis dominated with 36.7% share in 2025. The dominance is attributed to its capability to proactively detect threats and attack vectors. It can detect zero-day vulnerabilities, driving the segment growth. Companies today use generative AI-based solutions that can be used for simulating threats. Cyber criminals use new techniques to bypass existing defenses. Thus, market players are introducing adaptive solutions against cyber attacks. These technologies increase visibility throughout complex information technology infrastructures, allowing for faster identification and automatic management of threats. In addition, threat detection and analysis solutions can leverage predictive analytics and real-time monitoring. These benefits make them a key component of AI-based cybersecurity approaches.
Technology Analysis
In terms of technology, the segmentation includes generative adversarial networks (GANs) security, variational auto encoders (VAEs), reinforcement learning (RL), deep neural networks (DNNs), natural language processing (NLP), and others. The reinforcement learning (RL) segment is projected to experience the highest CAGR of 25.8% throughout the forecasted period. The growth is driven by the ability of RL to learn from and adapt to dynamic situations of cyberattacks. RL models ensure the decision-making abilities of the system along with optimizing the defense strategy to counter the intrusion autonomously without any human interference. Reinforcement learning not only increases resilience but also enables proactive protection through identifying and neutralizing various attack methods. Its growing application in autonomous intrusion detection and adaptive access control highlights its transformative role in the evolution of generative AI cybersecurity solutions.
End Use Analysis
The segmentation, based on end use, includes banking, financial services, and insurance (BFSI), healthcare & life sciences, government & defense, retail and e-commerce, manufacturing & industrial, IT & telecommunications, energy & utilities, and others. The banking, financial services, and insurance (BFSI) segment led the market with 31.9% share in 2025. This is owing to rising demand for sophisticated security against cyber threats, including fraud, identity theft, and data breaches. BFSI organizations deal with huge amounts of transactions and data. This makes it easy for cyber criminals to target them. Generative AI technology helps in securing the BFSI sector by facilitating real-time fraud detection, anomaly detection, and dynamic risk modeling to protect their assets and meet regulatory compliance. The ability to maintain secure digital transactions while reducing operational risks highlights BFSI as a leading adopter of generative AI cybersecurity technologies.
Segment-at-a-Glance
| Dimension | Leading Segment (2025) | Fastest-Growing Segment (2026–2034) |
| Type | Threat Detection & Analysis | Network Security |
| Technology | Generative Adversarial Networks (GANs) / Deep Neural Networks | Reinforcement Learning (RL) |
| End Use | BFSI | Healthcare & Life Sciences |
| Region | North America | Asia Pacific |
Source: Polaris Market Research Analysis
Regional Analysis
North America Generative AI Cybersecurity Market Overview
North America accounted for the largest global market share of 39.8% in 2025. This dominance is attributed to the region’s advanced digital ecosystem. Strong integration of AI technologies and extensive cybersecurity investments across industries also boost the growth. In August 2025, CISA and FEMA granted funding of more than USD 100 million to boost cybersecurity defenses for communities across the U.S. Enterprises in North America are early adopters of emerging technologies. They adopt generative AI to address evolving threats and protect critical infrastructure. GenAI for cybersecurity is also adopted to meet stringent compliance frameworks. The presence of well-established technology providers and cybersecurity innovators creates a mature market landscape. This combination of technological readiness and industry-wide adoption drives the regional market expansion (Source: cisa.gov).
U.S. Generative AI Cybersecurity Market Insights
The U.S. dominated the North America generative AI cybersecurity market share by holding 70.21% in 2025. The growth is due to its strong technological infrastructure. Widespread adoption of AI and investments in advanced cybersecurity solutions also drive the U.S. generative AI cybersecurity market growth. Organizations across critical sectors such as finance, defense, and healthcare are early adopters of generative AI to counter sophisticated cyberattacks. The presence of leading technology providers and a mature regulatory framework further enhances the U.S. position for innovation and adoption in this market.
Asia Pacific Generative AI Cybersecurity Market Trends
The market in Asia Pacific is projected to witness fastest growth of CAGR 24.3% during the forecast period due to rapid digitalization, expanding internet penetration, and the growing complexity of cyber threats in the region. According to Press Information Bureau of India, the country has 1,002.85 million internet subscribers as of year 2025. Increasing emphasis on defense cybersecurity propels the market growth. Further, enterprises across sectors such as e-commerce, financial services, and telecommunications are increasingly vulnerable to large-scale cyberattacks, creating a strong demand for adaptive AI-powered defenses. Generative AI solutions provide predictive modeling and advanced threat detection capabilities tailored to the region’s evolving digital infrastructure. The combination of rising enterprise security awareness and the need for scalable protection is expected to fuel market growth in Asia Pacific during the forecast period (Source: pib.gov.in).
China Generative AI Cybersecurity Market Overview
The China recorded significant share of 42.8% in the Asia Pacific region. This is due to the rapid digitalization of its economy and the rising demand for advanced security in cloud-based services, e-commerce, and financial platforms. The country’s large-scale deployment of AI technologies across industries has increased the need for intelligent, adaptive defenses against evolving cyber threats. Generative AI solutions provide the capability to simulate, predict, and mitigate threats in real time, aligning with China’s growing focus on securing its digital ecosystem.
Europe Generative AI Cybersecurity Market Outlook
The generative AI cybersecurity industry in Europe is projected to hold a substantial share of 27.6% by 2034. Rising emphasis on complying with stringent regulatory requirements and data privacy policies boost the regional market growth. Players are required to adhere to advanced cybersecurity measures. Companies are being driven to adopt AI-powered cybersecurity solutions for continuous monitoring, compliance reporting, and proactive risk management, with rigorous guidelines like GDPR dictating corporate practices. As per an EU report published in January 2025, 55.03% of large enterprises in the European Union adopted AI technology in 2025. European businesses are spending significantly on generative AI for protecting their crucial infrastructure, financial operations, and other digital services from advanced cybersecurity risks. This alignment of strict data governance with AI-enabled cybersecurity innovation strengthens Europe’s position as a prominent region for these AI solutions in the long term. (Source: ec.europa.eu).
Germany Generative AI Cybersecurity Market Overview
Germany is expected to witness significant CAGR of 22.4% during the forecast period. The growth is driven by the country’s strong focus on industrial digitalization and protection of critical infrastructure. The need for cybersecurity solutions that adapt to changing scenarios is increasing as manufacturing and Industry 4.0 operations become more interconnected. The Generative AI technology gives the capability of securing smart factories, the supply chain, and industrial networks from threats. Compliance with stringent European data protection regulations increases the speed at which such solutions can be implemented.
Competitive Analysis
The generative AI cybersecurity landscape is witnessing intense competition. Established players and niche vendors take advantage of competitive intelligence and strategy to capture business opportunities. Business strategy in the domain is largely aimed at technological innovation, specifically in autonomous response and threat hunting technologies. These can be considered disruptive generative AI cybersecurity market trends. These innovations have become very important differentiators for small and mid-size companies. Generative AI cybersecurity vendor strategies increasingly prioritize boosting region-wise market size. Expert insights suggest that future development strategies must align with macroeconomic trends to capitalize on latent demand and opportunities across both developed and emerging markets.
List of Key Generative AI Cybersecurity Companies
- Abnormal Security
- Amazon Web Services, Inc. (AWS)
- BigID
- Cohesity, Inc.
- CrowdStrike
- Darktrace Holdings Limited
- IBM
- Lakera Inc
- Microsoft
- Musarubra US LLC (Trellix)
- Palo Alto Networks
- Recorded Future
- SentinelOne
- Snyk
- Zscaler, Inc.
Vendor & Product Positioning
| Company | Positioning | Notable GenAI Security Capability |
| Microsoft | Cloud & platform giant | Security Copilot — AI-assisted SOC triage and investigation |
| | Cloud & platform giant | Gemini-powered threat intelligence within Google Cloud Security |
| AWS | Cloud & platform giant | Amazon GuardDuty AI Protection, purpose-built threat detection for Bedrock and SageMaker paired with Amazon Bedrock Guardrails for content/PII safeguards. |
| IBM | Enterprise security & AI platform | IBM Security QRadar Suite + watsonx.governance for AI model risk |
| Palo Alto Networks | Pure-play cybersecurity leader | Precision AI across its Security Operating Platform; strengthened via 2025 Protect AI acquisition |
| CrowdStrike | Pure-play cybersecurity leader | Falcon platform with GPU-accelerated detection via NVIDIA collaboration |
| SentinelOne | Pure-play cybersecurity leader | Purple AI threat-hunting assistant; expanded via Prompt Security acquisition |
| Darktrace | AI-native specialist | Prevent — autonomous, self-learning threat response |
| Zscaler | Cloud security specialist | ZDX Copilot for AI-assisted zero-trust operations |
| Fortinet | Network security incumbent | FortiAI embedded across the Security Fabric platform |
| Trellix (Musarubra US LLC) | Enterprise security incumbent | XDR platform with integrated AI/ML detection |
| Cohesity | Data security & resilience specialist | AI-assisted data classification and ransomware recovery |
| Lakera Inc | AI-native specialist | LLM/prompt-injection and AI-application security |
Source: Polaris Market Research Analysis
Generative AI Cybersecurity Industry Developments
- May 2026: Akamai Technologies, Inc., announced a definitive agreement for the acquisition of LayerX. According to Akamai, the acquisition will unite browser-native controls with the company’s Zero Trust and application security portfolio to advance its workforce security strategy. (Source: akamai.com)
- May 2026: Cisco announced its intent to acquire Israeli non-human identity (NHI) and AI security startup, Astrix Security. The company stated that the acquisition will enable it to extend Zero Trust principles to the emerging agentic workforce. (Source: cisco.com)
- July 2025: Accenture and Microsoft collaborated to co-invest in generative AI for cybersecurity. The collaboration focuses on SOC modernization, automated AI security, cyber migration, and enhanced IAM to help organizations better mitigate advanced threats and optimize costs. (Source: newsroom.accenture.com)
Future Outlook
According to our generative AI cybersecurity market forecast, the market is expected to witness the rapid growth in the coming years. Future prospects in the generative AI cybersecurity market are likely to be shaped by advanced cyber threats, widespread adoption of AI solutions by businesses, and increasing needs for automation of cybersecurity measures. AI-based solutions contribute to fast threat detection and mitigation, improved security monitoring and analytics, prediction and mitigation of threats, among other advantages. With further development, such technologies may become even more important components of cybersecurity systems. Issues related to the regulation of AI, data privacy concerns, and various security threats will continue to present challenges. Nonetheless, further developments are expected to lead to increased adoption of generative AI technologies in cybersecurity.
Generative AI Cybersecurity Market Segmentation
By Type Outlook (Revenue, USD Billion, 2021–2034)
- Threat Detection & Analysis
- Adversarial Defense
- Insider Threat Detection
- Network Security
- Others
By Technology Outlook (Revenue, USD Billion, 2021–2034)
- Generative Adversarial Networks (GANs)
- Variational Auto encoders (VAEs)
- Reinforcement Learning (RL)
- Deep Neural Networks (DNNs)
- Natural Language Processing (NLP)
- Others
By End Use (Revenue, USD Billion, 2021–2034)
- Banking, Financial Services, And Insurance (BFSI)
- Healthcare & Life Sciences
- Government & Defense
- Retail and E-Commerce
- Manufacturing & Industrial
- IT & Telecommunications
- Energy & Utilities
- Others
By Regional Outlook (Revenue, USD Billion, 2021–2034)
- North America
- U.S.
- Canada
- Europe
- Germany
- France
- UK
- Italy
- Spain
- Netherlands
- Russia
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- Malaysia
- South Korea
- Indonesia
- Australia
- Vietnam
- Rest of Asia Pacific
- Middle East & Africa
- Saudi Arabia
- UAE
- Israel
- South Africa
- Rest of Middle East & Africa
- Latin America
- Mexico
- Brazil
- Argentina
- Rest of Latin America
Generative AI Cybersecurity Market Report Scope
| Report Attributes | Details |
| Market Size in 2025 | USD 2.58 Billion |
| Market Size in 2026 | USD 3.13 Billion |
| Revenue Forecast by 2034 | USD 15.01 Billion |
| CAGR | 21.6% from 2026 to 2034 |
| Base Year | 2025 |
| Historical Data | 2021–2024 |
| Forecast Period | 2026–2034 |
| Quantitative Units | Revenue in USD Billion and CAGR from 2026 to 2034 |
| Report Coverage | Revenue Forecast, Competitive Landscape, Growth Factors, and Industry Trends |
| Segments Covered |
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| Regional Scope |
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| Competitive Landscape |
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| Report Format |
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| Customization | Report customization as per your requirements with respect to countries, regions, and segmentation. |
Source: Polaris Market Research Analysis
Generative AI Cybersecurity Market FAQ's
The global market size was valued at USD 2.58 billion in 2025 and is projected to grow to USD 15.01 billion by 2034.
The global market is projected to register a CAGR of 21.6% during the forecast period.
North America dominated the market with a 39.8% share in 2025, driven by strong market demand and advanced infrastructure.
A few of the key players in the market are Cohesity, Inc.; CrowdStrike; Darktrace Holdings Limited; Google; IBM; Lakera Inc; Microsoft; Palo Alto Networks; SentinelOne; Musarubra US LLC (Trellix); and Zscaler, Inc.
The threat detection & analysis segment accounted for the largest market share of 36.7%, driven by the growing need for advanced threat monitoring and analysis.
The reinforcement learning (RL) segment is expected to witness the fastest growth during the forecast period.
Generative AI in cybersecurity refers to a system that detects threats, analyzes suspicious activity, and automates responses in cybersecurity. With rising use of generative AI by attackers to create convincing phishing emails, malware, and fake content, demand for strong cybersecurity practices is rising.
The BFSI sector is expected to see strong adoption due to the growing need to protect sensitive financial and customer data.
Agentic AI in cybersecurity refers to autonomous AI systems that independently analyze threats, investigate incidents, make decisions, and execute security actions with limited human intervention.
The market is growing due to increasing cyberattacks and rising data security concerns. Also, rising demand for automated threat detection fuels the market expansion.
High implementation costs and data privacy concerns hinder the market growth. Further, the shortage of skilled professionals is becoming a key challenge.
Generative AI helps automate threat detection and analyze large amounts of security data. Its adoption is also supporting faster incident response.
Generative AI risks in cybersecurity include data leakage, prompt injection, model manipulation, and adversarial attacks. Also, misinformation, unauthorized access, and automated cyberattacks are among the other risks.
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