EDITOR ’ S QUESTION
EMAD FAHMY , DIRECTOR OF SYSTEMS
ENGINEERING , NETSCOUT
Generative AI can take many facets of cyberthreats to new levels . These include enhancements to social engineering , such as crafting more convincing and unique phishing emails or mimicking voices in audio messages . Image or video generation , including deepfake images , have been shown to trick biometric facial recognition if they are executed correctly , and adversaries have access to this technology .
Scaling an attack to be bigger and better is easier than ever due to the automation it can empower . Automating rudimentary processes , such as sending phishing emails , which can let adversaries target more individuals within an organisation to increase their chances of gaining access
Nevertheless , businesses can take advantage of AI to automate IT processes , analyse data , and enhance cybersecurity protocols . AI platforms can leverage network data to automatically discover threats and aid in removing them from networks and applications in record time . Efficiency is the name of the game with AI , and it delivers that when utilised properly for cyber defences .
Although AI simplifies processes and increases efficiency , the best defence starts with humans . Having an adequately trained workforce and qualified cybersecurity teams is paramount to keeping networks secure . With properly trained teams , organisations are far less likely to fall victim to a social engineering campaign , preventing many breaches before they can even start .
Even with the most highly trained staff , breaches are still a major risk . Strong network detection and response , NDR tools and AI insights can arm security teams with the necessary resources to identify and remove threats in a timely manner .
RAMPRAKASH RAMAMOORTHY , DIRECTOR OF AI RESEARCH , MANAGEENGINE
AI-powered solutions bring enhanced detection mechanisms into the enterprise , pinpointing ransomware , phishing attempts , and other cyberthreats with high accuracy . By integrating three critical data categories , user , entity , and process , security teams can detect unusual patterns indicative of malicious activity , whether that ’ s a sudden spike in data access or an unexpected login location .
This triadic approach to threat detection enables a continuous and adaptive security model , where GenAI can develop new response protocols as threats evolve , thus enhancing enterprise-wide threat intelligence .
Using ML-driven multivariate anomaly detection , organisations can predict outages or potential security breaches by analysing historical patterns and real-time data . GenAI augments these techniques by offering contextual analysis , which transforms raw alerts into actionable intelligence , helping administrators prioritise responses based on likely impact and urgency .
Data integration is another critical component of modern security . By breaking down data silos , organisations can ensure that cybersecurity intelligence is shared across systems , strengthening threat detection and reducing false positives .
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