FEATURE: TRENDS IN CYBERSECURITY SOLUTIONS adoption of Zero-Trust architectures. Convergence of cybersecurity with observability will enhance real-time visibility across hybrid environments.
AI and Generative AI are rapidly becoming force multipliers in modern cybersecurity strategies. AI detects anomalies across massive datasets with unprecedented precision, while Generative AI simulates sophisticated cyberattacks, enhancing readiness and resilience.
The evolving cybersecurity landscape demands a blend of technical expertise and strategic acumen. Skills in cloud security, threat intelligence, and automated incident response are becoming crucial, alongside proficiency in AI, ML to manage nextgeneration security tools.
Soft skills such as cyber risk communication, analytical thinking, and cross-functional collaboration are equally critical. As threats continue to grow in sophistication, continuous upskilling remains vital.
Many enterprises still operate within reactive models, relying on fragmented toolsets that limit end-to-end threat visibility. Integration with legacy infrastructures remains a critical hurdle, creating potential security gaps. Talent shortages further compound these challenges, particularly in highly specialised areas like AI and cloud security.
Julio De Salvo, SVP Technology, Head of Solution Strategy, MENA and APAC, Globant
A major trend is Generative AI, which transforms how we detect and respond to cyber threats. This technology predicts risks, generates real-time threat intelligence, and simulates attacks to test system resilience, all with minimal human effort.
AI and Generative AI are revolutionising cybersecurity by enhancing real-time threat detection, incident response, and predictive analytics. For instance, Generative AI excels at detecting deepfake content in phishing attacks, spotting subtle details that traditional systems miss.
These technologies also enable anomaly detection to identify unusual network activity, automate threat intelligence for adaptive security, and support real-time threat mitigation.
Generative AI models often lack explainability, making it hard for humans to understand their actions, which complicates regulatory compliance and internal accountability. False positives from AI systems can waste resources or cause real threats to be overlooked.
The shift to Zero-Trust architecture enforces strict verification for every user and device, driven by growing cloud and mobile usage. Quantum-safe cryptography is also emerging to protect against future quantum computing risks.
Trust in AI-driven systems is also a challenge, as many hesitate to rely fully on AI for critical security decisions, requiring a balanced approach.
Nikola Kukoljac, Vice President Solution Architecture, Help AG
A strong grasp of how networks, operating systems, applications, endpoints, databases work is essential. These foundational skills enable professionals to understand cyber hygiene, identify vulnerabilities, and assess risk effectively. Mastering the basics is, and always will be, the backbone of effective cybersecurity.
One of the biggest limitations in modern cybersecurity is complexity. A decade ago, organisations relied on just a handful of security tools. Today, the average enterprise manages over 30 different cybersecurity vendors and solutions, making it impossible for teams to fully understand, configure, and optimise each one.
Simplification and interoperability are becoming essential to overcome the challenges of an increasingly fragmented security landscape. This overload leads to underutilised technologies and reduced return on investment. Compounding the issue is a global skills shortage, making it harder to manage such complex environments.
Mohammed AlMoneer, Senior Regional Director, Middle East, Africa and Türkiye, Infoblox
In 2025, enterprises will face a critical inflection point as technology
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