Model Inversion

Secure-GenAI-KrishnaG-CEO

GenAI: Security Teams Demand Expertise-Driven Solutions

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Generative AI (GenAI) refers to a subset of artificial intelligence technologies designed to create new content, such as text, images, videos, and even code, based on patterns and data fed into it. Unlike traditional AI systems that rely on predefined algorithms and data sets, GenAI models learn from vast amounts of data and can generate original outputs that resemble human-created content. These outputs can range from realistic-looking deepfakes to sophisticated malware and phishing schemes, making GenAI a powerful tool for both cyber defenders and attackers.

In the context of cybersecurity, GenAI’s potential is vast. It can be utilised for automating threat detection, creating advanced defence mechanisms, and developing incident response strategies. However, the same capabilities that make GenAI a valuable asset to security teams also make it an attractive tool for cybercriminals, who can use it to create new, more complex forms of cyber attacks.

Adversarial-ML-KrishnaG-CEO

Adversarial Machine Learning Attacks: A C-Suite Guide to Mitigating Risks

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In today’s data-driven world, machine learning (ML) has become an indispensable tech for businesses across various industries. From fraud detection to customer segmentation, ML algorithms extract valuable insights and make informed decisions. However, the increasing reliance on ML systems has also made them a prime target for malicious actors. Adversarial machine learning attacks exploit the vulnerabilities of ML models to compromise their integrity and functionality. This blog article will delve into the intricacies of adversarial machine learning attacks, exploring their various types, real-world implications, and effective mitigation strategies. We will adopt a C-suite-centric perspective, focusing on the business impact, ROI, and risk mitigation associated with these attacks.