The Impact of AI on Cybersecurity
Posted: Thu Feb 13, 2025 5:47 am
Artificial intelligence has drawn a lot of media attention for everything from taking people’s jobs to spreading disinformation and infringing copyrights, but AI’s impact on cybersecurity may be its most pressing immediate issue.
AI’s impact on security teams is predictably double-edged. When properly applied, it can be a powerful force multiplier for cybersecurity practitioners, through such means as processing vast amounts netherlands whatsapp number data of data at computer speeds, finding connections between distant data points, discovering patterns, detecting attacks, and predicting attack progressions. But, as security practitioners are well aware, AI is not always properly applied. It intensifies the already imposing lineup of cybersecurity threats, from identity compromise and phishing to ransomware and supply chain attacks.
CISOs and security teams need to understand both the advantages and risks of AI, which requires a substantial rebalancing of skills. Security engineers, for example, must grasp the basics of machine learning, model quality and biases, confidence levels, and performance metrics. Data scientists need to learn cybersecurity fundamentals, attack patterns, and risk modeling to effectively contribute to hybrid teams.
AI Models Need Proper Training to Assist Cybersecurity
The task of dealing with the proliferation of AI-fueled threats compounds the challenges for CISOs and already overworked security teams who must not only deal with new sophisticated phishing campaigns crafted by a large language model (LLM) like ChatGPT, but still have to worry about an unpatched server in the DMZ that could pose a bigger threat.
AI’s impact on security teams is predictably double-edged. When properly applied, it can be a powerful force multiplier for cybersecurity practitioners, through such means as processing vast amounts netherlands whatsapp number data of data at computer speeds, finding connections between distant data points, discovering patterns, detecting attacks, and predicting attack progressions. But, as security practitioners are well aware, AI is not always properly applied. It intensifies the already imposing lineup of cybersecurity threats, from identity compromise and phishing to ransomware and supply chain attacks.
CISOs and security teams need to understand both the advantages and risks of AI, which requires a substantial rebalancing of skills. Security engineers, for example, must grasp the basics of machine learning, model quality and biases, confidence levels, and performance metrics. Data scientists need to learn cybersecurity fundamentals, attack patterns, and risk modeling to effectively contribute to hybrid teams.
AI Models Need Proper Training to Assist Cybersecurity
The task of dealing with the proliferation of AI-fueled threats compounds the challenges for CISOs and already overworked security teams who must not only deal with new sophisticated phishing campaigns crafted by a large language model (LLM) like ChatGPT, but still have to worry about an unpatched server in the DMZ that could pose a bigger threat.