Authors : Katikam Mahesh

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/mrrnfhpz

Scribd : https://tinyurl.com/3ac8pjep

DOI : https://doi.org/10.38124/ijisrt/26mar858

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : With projections to rise from USD 2 billion in 2024 to USD 14.79 billion by 2034, the worldwide market for generative AI in cybersecurity is expanding quickly. Council for the Global Development of Skills (GSDC) .to save from various cyber-attacks need an effect technology to tackle it. In order to counter increasingly complex, automated threats that elude conventional detection techniques, generative artificial intelligence (GenAI) is crucial to current cybersecurity. By providing real-time anomaly detection, quick threat analysis, automatic vulnerability patching, and AI-driven phishing defines, it functions as a force multiplier and significantly improves security posture. Existing deep learning models such as ANN (Artificial Neural Network) and DNN (Deep neural networks).so to enhance performance of these with the help of Generative ai technique is GAN (Generative Adversarial Network) easy to detect fake and real images automatically

Keywords : ANN (Artificial Neural Network), Generative ai Technique is GAN (Generative Adversarial Network), Generative Artificial Intelligence (GenAI), Deep Neural Networks(DNN).

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