Intelligent CIO Middle East Issue 109 | Page 33

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 .

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 . threat detection and reducing false positives . For instance , in a multi-cloud environment , unified data streamlining enables seamless visibility , ensuring that each system within an enterprise contributes to a collective defence strategy . GenAI can further enhance visibility by enabling Natural Language Processing models that streamline communication between systems , making cybersecurity data more accessible .
Security policies should encompass continuous authentication mechanisms , adaptive thresholds , and data encryption , ensuring that sensitive information is safeguarded even as it flows across various business units . By adopting an AI-driven digital culture , enterprises can transition from isolated , manual checks to a cohesive security ecosystem that adapts to emerging threats in real time .
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
RAMPRAKASH RAMAMOORTHY , DIRECTOR OF AI RESEARCH ,
MANAGEENGINE
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