model manipulation

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.

PenTest-Anthropic-KrishnaG-CEO

Penetration Testing Anthropic: Securing the Future in an Era of Advanced Cybersecurity Threats

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**Penetration Testing Anthropic** combines traditional penetration testing methods with a more nuanced understanding of human behaviour, cognitive psychology, and artificial intelligence (AI). The term “anthropic” refers to anything that relates to human beings or human perspectives, and in this context, it highlights the critical role human elements play in both security and attack strategies.

While traditional penetration testing often focuses on exploiting technical vulnerabilities in systems, Penetration Testing Anthropic goes beyond these boundaries by considering how human behaviours—both of attackers and defenders—can influence the outcome of a cyberattack. This includes social engineering tactics, cognitive biases, organisational culture, decision-making processes, and the integration of AI and machine learning into attack and defence mechanisms.

This approach represents a shift from purely technical penetration testing to a more comprehensive model that accounts for the psychological, social, and technological aspects of cybersecurity.