FEATURE: TRENDS IN CYBERSECURITY SOLUTIONS
innovation collides with rising threats and escalating demands for agility and resilience. Key product innovations will include the emergence of self-healing networks, which leverage AI-based monitoring and proactive diagnostics to detect, diagnose, and resolve issues autonomously.
Additionally, the rise of Resiliency as a Service will offer on-demand disaster recovery capabilities, enabling enterprises to achieve unprecedented levels of resilience. These innovations will redefine the standards for intelligent, autonomous infrastructure and business continuity.
Key skill sets include expertise in AI and machine learning, proficiency in all topics concerning the cloud, especially security and management, and knowledge of Zero-Trust architectures. Cybersecurity professionals must also be adept at threat intelligence analysis, incident response, and automation tools.
Emad Fahmy, Systems Engineering Director, NETSCOUT
AI and Generative AI are now central to both offensive and defensive cybersecurity strategies. Defenceside platforms embed AI to automate threat detection, accelerate response times, and enhance data accuracy across AIOps systems. These technologies analyse network traffic in real-time, identifying anomalies and stopping threats proactively.
Generative AI is also used to enhance deepfake detection and monitor AI-generated phishing or social engineering content.
interpret detailed telemetry data all contribute to faster detection and more effective remediation.
Harun Baykal, Head of Cybersecurity Practice Middle East and Africa, NTT DATA
AI and Generative AI are embedded into cybersecurity through two channels either on the perimeter as part of the defence or within security operations.
On the perimeter, we see AI infused solutions for intelligent threat detection, predictive analytics, and automated incident response. AI models analyse massive data sets to detect anomalies, forecast attacks, and trigger real-time defensive actions.
Generative AI is also being used to simulate attack scenarios and improve red teaming strategies. On the defensive side, it enhances threat modelling and helps secure Generative AI tools themselves, especially in unregulated environments.
As attackers increasingly use AI for sophisticated breaches like deepfakes, defenders are using
the same technologies to stay ahead. AI-infused cybersecurity solutions are essential to combat the growing complexity and velocity of modern threats.
For security operations, there is a massive effort and transformation going around how to replace some of the repetitive tasks with AI agents and replace the human effort with Agentic AI solutions. Although we are in the early stages of this transformation, the future is extremely promising.
Modern cybersecurity demands that professionals understand advanced threat vectors such as encrypted traffic, lateral movement, and zero-day exploits. They must leverage packet-level visibility to monitor network traffic across all layers of the Open Systems Interconnection, OSI model and detect threats in real time.
Experience with tools that can identify subtle or hidden attacks is valuable, as is familiarity with AI and machine learning techniques to enhance threat detection. Knowledge of forensic analysis for compliance purposes, along with securing IoT and cloud environments, is also essential. Strong analytical skills, accurate incident response, and the ability to
Tarek Abbas, Senior Director of Technical Solutions, EMEA South, Palo Alto Networks
By learning from historical security data, AI models can establish a baseline of normal network behaviour and then flag deviations that may signify security incidents. However, as organisations adopt AI solutions like ChatGPT, it is equally important to secure them.
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