Chicago, Aug. 12, 2024 (GLOBE NEWSWIRE) -- The global Generative AI Cybersecurity Market is anticipated to grow at a compound annual growth rate (CAGR) of 33.4% over the course of the forecast period, from an estimated USD 7.1 billion in 2024 to USD 40.1 billion by 2030, according to a new report by MarketsandMarkets™.
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Generative AI Cybersecurity Market Dynamics:
Drivers:
- Advanced AI-Driven Threats
- Proactive Defense
- Efficiency in Cybersecurity
- Improved Capabilities
Restraints:
- AI Governance Concerns
- Shadow IT Risks
- Need for Strict Measures
- Securing AI Deployment
Opportunities:
- Bridging the Skills Gap
- Efficiency in Entry-Level Roles
- Reduction in Incidents
- Untapped Opportunity
List of Key Players in Generative AI Cybersecurity Market:
- Palo Alto Networks (US)
- AWS (US)
- CrowdStrike (US)
- SentinelOne (US)
- Google (US)
- MOSTLY AI (Austria)
- XenonStack (UAE)
- BigID (US)
- Abnormal Security (US)
- 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 employing generative artificial intelligence systems for cyber security purposes, organizations will expand their market base. Major trends include leveraging generative AI to automate threat detection and response; enhance natural language interfaces for security products; and protect against sophisticated cyber threats such as deepfakes or social engineering attacks. This notwithstanding, some challenges like data privacy, security risks and the need for explainable AI persist thus requiring robust risk mitigation strategies. In order to strike a balance between innovation and security, organizations are likely to make huge investments into generative AI.
Generative AI cybersecurity landscape 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 anomalies in behavior. Tools for deepfake detection that are powered by AI tackle the challenges of verifying authenticity in synthetic media like video, audio, or images. Advanced security mechanisms and new cyber threats are created using Generative Adversarial Networks (GANs) hence necessitating continuous advancements in defensive technologies. Additionally, cloud-native DLP and DSPM tools efficiently identify, manage, and mitigate risks to ensure protection of data across environments. Even though these technologies strengthen security measures they can also result in risks such as data privacy issues, biases in AI models or wrong outputs being generated.
By software type, cybersecurity solutions for protecting generative AI is undergoing rapid growth, as AI moves into a multitude of sectors. For example, industry surveys estimate that by 2025, almost 10% of all data will be generated by generative AI, which calls for urgent and robust cybersecurity measures. In this regard, companies are designing specialized threat detection systems that are AI-focused, such as AI driven anomaly detection, which has increased breaches identification rate by 30% compared with the standard method. For instance, TensorFlow Extended (TFX) employed by Google can provide guarantees regarding safe deployment and monitoring of LLMs to combine cyber defense and artificial intelligence directly in AI model pipelines. Additionally, there have been adversarial attacks where malicious attackers tamper with outputs from AIs thus prompting the development of more advanced defenses such as adversarial training and robust model architectures that make it possible for AI workloads to withstand such threats.
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The domain of the application security is going through significant changes with the introduction of generative AI that has made cybersecurity both better and more complicated. As apps increasingly employ generative AI for different tasks, there is a greater need for enhanced security measures. For example, IBM implemented artificial intelligence in its security systems which improved threat detection accuracy by 20%, showing how AI based security protocols are becoming feasible. In addition, the mounting fears of adversarial attacks against artificial intelligence applications have led to initiatives such as adversarial training as well as adoption of strong model architectures that ensure secure and consistent outputs from AI systems. This orientation toward building robust security into development and deployment processes for generative AI applications is a key trend in contemporary application safety approaches.
Dual revenue streams can be obtained for vendors involved in generative AI cybersecurity by selling AI-driven cybersecurity solutions and specialized security tools to shield generative AI systems. Vendors can employ generative AI to improve their cybersecurity toolkits, so as to provide state-of-the-art threat identification and response capabilities which will appeal to organizations looking for advanced protection. For instance, Darktrace uses machine learning to detect and respond to threats in real time therefore lowering the response time and increasing demand on such innovative products. At the same time, vendors can build robust cybersecurity frameworks specifically meant for securing generative AI programs addressing concerns of emerging AI vulnerability. Through this approach vendors not only expand their product lines, but become all-inclusive solution providers in a market increasingly necessitating reliance on artificial intelligence. This allows them access into the growing IT budgets allocated for both: adoption as well as security of systems against cyber attacks.
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