EDITOR ’ S QUESTION
NED BALTAGI , MANAGING DIRECTOR , MIDDLE EAST ,
AFRICA AND TURKEY , SANS INSTITUTE
As cyber adversaries embrace Artificial Intelligence and automation to enhance their attacks , organisations must evolve their defences accordingly . Traditional , signaturebased approaches no longer suffice . Today ’ s cutting-edge cybersecurity solutions embed capabilities designed to proactively detect , respond to , and neutralise advanced AI-driven threats .
Central to this evolution is the integration of adaptive machine learning and behavioural analytics . Instead of relying solely on known threat indicators , next-generation tools analyse ongoing patterns , spotting subtle anomalies that hint at intrusions before they materialise into full-blown attacks .
Another critical shift is the emergence of Extended Detection and Response , XDR . By correlating insights from endpoints , networks , cloud workloads , and applications , these platforms leverage AI to filter out noise and highlight meaningful alerts . As a result , security teams can focus on truly suspicious activities rather than sifting through mountains of irrelevant data .
In tandem , intelligence-driven threat hunting is becoming a core practice . Incorporating fresh , high-quality threat intelligence into internal telemetry empowers defenders to anticipate attacker behaviour and uncover hidden indicators early .
The ability to proactively search for lurking threats , and not merely wait for alerts , significantly reduces an attacker ’ s advantage .
Equally important is continuous validation of security controls . AI-driven breach and attack simulation tools test an organisation ’ s defences in real-time , revealing gaps and enabling dynamic adjustments . This ensures that security posture remains agile and effective against evolving threats .
Automated and orchestrated response capabilities further streamline incident handling . By automating steps like isolating compromised hosts or blocking malicious traffic , teams can limit damage swiftly and free analysts for strategic work .
As the threat landscape evolves , driven by increasingly automated and sophisticated attacks , modern cybersecurity solutions must integrate advanced features to proactively address AI-driven risks . While large language models , LLMs have dominated the conversation around AI , their progression in 2025 is expected to be incremental rather than revolutionary . However , multimodal AI systems , capable of processing and analysing data from various sources such as text , images , and voice , will gain prominence . These systems are poised to play a critical role in defending against advanced phishing campaigns and social engineering scams , which continue to challenge traditional security measures .
The adoption of multi-modal AI systems will be complemented by incremental advancements across AI modalities . Enhanced hardware capabilities in processing , memory , and storage are expected to drive these improvements , enabling quicker and more accurate threat detection . Additionally , renewable energy technologies are likely to gain traction , helping mitigate the environmental impact of AI and other resource-intensive innovations . Despite these technological strides , relying on technology alone is insufficient . Integrating Generative AI tools into traditional security systems , such as email gateways to detect phishing and spam , can bolster defences . Yet , the human factor remains pivotal .
Organisations must foster a culture of cyber resilience through robust security awareness programmes . Employees must be trained to critically evaluate communications and resist the impulse to trust potentially malicious messages . Banning Generative AI outright is neither practical nor effective . Where restrictions exist , employees often find workarounds driven by curiosity or necessity . Instead , organisations must educate their workforce about AI ’ s risks and provide clear , actionable guidelines for safe use .
HARISH CHIB , VP EMERGING MARKETS , MIDDLE EAST AND
AFRICA , SOPHOS
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