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The growing interconnection of devices and systems, as well as the constant advancement in technology, has triggered cyber threats, which are increasingly becoming more sophisticated and dangerous. In this context, generative artificial intelligence (generative AI) is emerging as a key factor for risk management in cybersecurity. The integration of AI in the field of cybersecurity is revolutionizing the sector, especially when it comes to producing incident responses. Before the advent of AI, detecting and responding to cyber threats used to rely heavily on human efforts , often resulting in slow response times and increased risk exposure.

The first half of 2023 showed discouraging

Figures for the field of cybersecurity, there are records of more than 14,000 million attempted cyber attacks in Mexico, which has become one of the main targets of hackers. According to information compiled by the Association of Banks of Mexico and the American Chamber , two thirds Sweden Phone Number Data of the cyber attacks recorded in Latin America have occurred in the Aztec country and have generated losses ranging between 3,000 and 5,000 million dollars annually. A scenario that can be avoided with investment in cybersecurity and the adoption of a technological culture focused on prevention. Generative artificial intelligence for cybersecurity.

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What is generative artificial intelligence

How does it contribute to cybersecurity? Generative AI is a subfield of artificial intelligence that focuses on the creation of content, whether it be text, images, music, or any other type of data. This is achieved through deep learning algorithms that can generate new information based on patterns and input data. In cybersecurity, generative AI contributes to aspects such as: 1. Threat detection. Generative AI can analyze Denmark Phone Number large data sets for suspicious patterns, making it ideal for early threat detection. It can identify anomalies in network traffic, user behaviors or malicious activities that conventional methods might miss. 2. Automation of incident responses. Once a threat is detected, generative AI can generate automated responses to mitigate the risk.

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