Chicago, Aug. 12, 2024 (GLOBE NEWSWIRE) — The world Generative AI Cybersecurity Market is expected to grow at a compound annual growth rate (CAGR) of 33.4% during the forecast period from around USD 7.1 billion in 2024 to USD 40.1 billion in 2030, according to a new report by MarketsandMarkets™.
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Generative AI-Based Cybersecurity Market Dynamics:
Drivers:
- Advanced AI-generated threats
- Proactive defense
- Cybersecurity Effectiveness
- Improved Abilities
Restrictions:
- AI Governance Concerns
- Risks of Shadow Computing
- Need for strict measures
- Securing AI Deployment
Opportunities:
- Bridging the skills gap
- Effectiveness in entry-level positions
- Incident Reduction
- An untapped opportunity
List of Key Players in Generative AI Cybersecurity Market:
- Palo Alto Networks (USA)
- AWS (United States)
- CrowdStrike (USA)
- SentinelOne (United States)
- Google (United States)
- MOSTLY AI (Austria)
- XenonStack (UAE)
- BigID (USA)
- Abnormal Security (United States)
- Adversa AI (Israel)
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The generative AI cybersecurity market is growing rapidly and evolving significantly as more industries adopt generative AI technologies. By using generative AI systems for cybersecurity purposes, organizations will expand their market base. Key trends include the exploitation Generative AI Enterprises need to automate threat detection and response, improve natural language interfaces of security products, and protect against sophisticated cyber threats such as deepfakes or social engineering attacks. Despite this, challenges such as data privacy, security risks, and the need for explainable AI persist, requiring robust risk mitigation strategies. In order to strike a balance between innovation and security, organizations are likely to make huge investments in generative AI.
The cybersecurity landscape of generative AI has been impacted by several technologies. NLP and LLMs such as GPT-4/4o enhance threat detection systems and automate incident response by analyzing vast volumes of text data to detect security threats, phishing attempts, and behavioral anomalies. Deepfake detection tools powered by AI Generative adversarial networks (GANs) address the challenges of verifying the authenticity of synthetic media such as videos, audio files, or images. Advanced security mechanisms and new cyber threats are created using generative adversarial networks (GANs), which requires continuous advancements in defense technologies. In addition, cloud-native DLP and DSPM tools effectively identify, manage, and mitigate risks to ensure data protection in all environments. While these technologies strengthen security measures, they can also lead to risks such as data privacy issues, bias in AI models, or generation of erroneous results.
By software type, cybersecurity solutions to protect generative AI are growing rapidly as AI expands across a multitude of industries. For example, industry surveys estimate that by 2025, nearly 10% of all data will be generated by generative AI, requiring urgent and robust implementation cybersecurity measures. In this regard, companies are designing specialized AI-driven threat detection systems, such as AI-driven anomaly detection, which has increased the breach identification rate by 30% compared to the standard method. For example, TensorFlow Extended (TFX) used by Google can provide guarantees regarding the secure deployment and monitoring of LLMs to combine cyber defense and artificial intelligence directly into AI model pipelines. In addition, adversarial attacks have occurred where malicious attackers falsified AI results, which has prompted the development of more advanced defenses such as adversarial training and robust model architectures that enable AI workloads to resist such threats.
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The field of application security is witnessing significant changes with the introduction of generative AI that has made cybersecurity both better and more complex. With applications increasingly using generative AI for different tasks, there is a growing need for stronger security measures. For example, IBM implemented AI in its security systems, which improved the threat detection accuracy by 20%, which shows how AI-based security protocols are becoming feasible. Furthermore, the growing fears of adversarial attacks on AI applications have led to initiatives such as adversary training as well as the adoption of robust model architectures that ensure safe and consistent results from AI systems. This shift towards integrating robust security into the development and deployment processes of generative AI applications is a key trend in contemporary application security approaches.
Vendors involved in generative AI cybersecurity can achieve dual revenue streams by selling AI-based cybersecurity solutions and specialized security tools to protect generative AI systems. Vendors can use generative AI to enhance their cybersecurity toolkits, providing cutting-edge threat identification and response capabilities that will appeal to organizations seeking advanced protection. For example, Darktrace uses machine learning to detect and respond to threats in real time, reducing response time and increasing demand for these innovative products. At the same time, vendors can create robust cybersecurity frameworks specifically targeted at securing generative AI programs by addressing concerns about emerging AI vulnerabilities. With this approach, vendors not only expand their product offerings, but also become full-solution providers in a market that increasingly requires the use of artificial intelligence. This allows them to access growing IT budgets allocated to both the adoption and security of systems against cyberattacks.
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