Modern day cybersecurity is being polarised by increasing levels of embedded AI, yet requires the skills of mature security administrators to derive the best results from these advanced solutions. AI and Generative AI are making platforms intuitive and powerful and introducing usage of Natural Language Processing. However, human oversight is important and teams need to be equipped with skills to understand, interpret, manage AI-driven security systems. Top executives from Acronis, Anomali, BeyondTrust, Check Point, Cloud Box, Fortinet, GBM, Globant, Help AG, Infoblox, NETSCOUT, NTT, Palo Alto Networks, Phosphorus, Positive Technologies, Proofpoint, Sophos, Tenable, comment on this polarisation.
From 2025 and beyond, cybersecurity solutions are expected to evolve to keep pace with rapidly interconnected systems and increasingly complex applications. A major trend is the surge in API security, driven by the need for seamless communication between modern applications and backend systems, especially as AI adoption grows across the region.
“As businesses integrate AI into their operations, APIs become essential for enabling that communication, making their protection critical,” says Nikola Kukoljac, Vice President Solution Architecture, Help AG
“Another innovation area is data security. As AI models require large datasets for training, there is now increased demand for data protection technologies like data classification, masking, and organisation to safeguard sensitive information,” adds Kukoljac.
Proactive and intelligent systems
AI and Generative AI are transforming cybersecurity by making platforms more intuitive and significantly more powerful. One major advancement is the use of natural language processing, which allows security professionals to interact with complex systems using plain English, eliminating the need for platform-specific query knowledge and accelerating adoption across teams.
AI is enhancing threat detection by analysing vast amounts of data in real time, identifying behavioural anomalies, and spotting early signs of attacks that traditional tools might miss.
Says Gerald Beuchelt, CISO Acronis, “Following the popular rise and broad availability of LLMs since the early 2020s, cybersecurity vendors quickly began incorporating Generative AI into their offerings. This led to features like platform assistants, automated incident summarisation, and AI-assisted threat hunting.”
Today, vendors are developing more advanced applications, such as agentic AI capabilities for autonomous Security Operations Centres. “At the same time, as the limitations of LLMs become better understood, we are seeing more intentional and practical feature design,” adds Beuchelt
“Cybersecurity is about cleaning up the alphabet soup and converging into a unified, intelligent platform. In 2025, innovation means unified data platforms, not stitched-together stacks. The next frontier is about systems that think, learn, and act without compromise, at scale, speed, and precision,” says Alexandre Depret-Bixio, Senior Vice President, Anomali.
“Cybersecurity innovation is defined by the convergence of intelligent automation, cloud-native defence and platform-driven architectures,” says
Ram Narayanan, Country Manager at Check Point Software Technologies, Middle East.
“The focus has shifted from reactive measures to predictive, adaptive security. Disruptive trends include quantum-resilient cryptography, Zero Trust models tailored for hybrid work and behavioural biometrics for identity assurance,” adds Narayanan.
As cyber threats become more sophisticated, the industry is moving toward integrated ecosystems that can self-heal, self-learn and scale dynamically. AI is becoming a crucial part of cybersecurity by boosting threat detection, automating tasks and speeding up responses. It can sift through enormous amounts of data to spot patterns and abnormalities, helping to stop security breaches.
Why today’s platforms have limitations
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,” says Help AG’s Kukoljac.
“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,” continues Kukoljac.
Many solutions still suffer from fragmented architectures, costly integrations, and limited visibility. Traditional SIEMs and bolt-on tools often throttle performance, restrict data access, or require trade-offs between speed and scale.
“They were not designed for today’s AI-driven threat landscape. Reliance on outdated detection models and limited historical data leaves blind spots,” says Anomali’s Depret-Bixio.
Despite significant advancements, modern cyber security solutions still face several limitations. One major challenge is the ever-evolving nature of cyber threats, which can outpace the development of defensive measures.
“Attackers are constantly finding new ways to exploit vulnerabilities, making it difficult for security solutions to stay ahead,” says Mohammed AlMoneer, Senior Regional Director, Middle East, Africa and Türkiye, Infoblox
“Despite technological advances, many cybersecurity solutions remain overly reliant on perimeter-based defences like firewalls, VPNs and intrusion prevention systems. These tools are essential but they often miss internal threats such as lateral movement, insider attacks, and zero-day exploits,” says Emad Fahmy, Systems Engineering Director, NETSCOUT.
Once attackers breach the perimeter, traditional systems lack visibility into deeper network activity. This blind spot allows malicious actors to remain undetected while executing advanced tactics, often mapped in the MITRE ATT&CK framework.
Without robust internal traffic and packet-level monitoring, organisations face delayed detection, poor incident response, and greater risk exposure, making post-breach visibility a critical cybersecurity priority.
“Modern cybersecurity solutions face limitations such as over-reliance on technology without sufficient human oversight, and fragmented toolsets creating integration and visibility challenges. Despite advancements, threats like AI-driven deepfakes and ransomware continue to outpace detection capabilities,” says Harun Baykal, Head of Cybersecurity Practice Middle East and Africa, NTT DATA.
“Cloud misconfigurations and weak supply chain defences remain vulnerabilities. Budget and staffing shortages hinder effective response, especially for small to mid-sized businesses. Generative AI introduces new risks around misuse and unsecured deployment,” continues Baykal.
“A deep understanding of an organisations systems is required to assess threats, protect assets, and prioritise those things that present the most risk. Machines and tools cannot analyse all of this and produce results, they can only present the relevant information to the person who can apply that analytical judgement,” adds Chester Wisniewski, Field CISO, Sophos.
“What most do not appreciate is that cybersecurity is a big data problem. Typically, organisations rely on up to 140 disconnected security tools, creating siloes that hinder efficiency and create blind spots,” says Maher Jadallah, Vice President, Middle East and North Africa, Tenable.
“As a result, data is often fragmented, disorganised and lacks context, making it difficult to effectively prioritise risks, report on security posture and answer basic security questions,” continues Jadallah.
To secure the modern attack surface security teams need a unified approach to security that transcends silos, illuminating the attack paths that threat actors look to exploit. Prioritising where to focus efforts first, closes the gaps and wipes out these paths, eradicating risks.
Challenges of ramping up future skills
“Automation is particularly important in cybersecurity given the ongoing shortage of expert security staff. However, human oversight will still be vitally important. Security teams will need to be equipped with the knowledge and skills to understand, interpret, and manage AI-driven security systems effectively,” says Alain Penel, Vice President, Middle East, Türkiye and CIS, Fortinet.
AI excels at tactical responses based on predefined rules. However, defining security policies, understanding risk tolerance, and making strategic decisions still require human expertise and intuition.
“Analysing new and evolving threats, understanding their potential impact, and developing innovative countermeasures will also still require human intelligence and creativity, adds Penel.
“In modern cybersecurity, if you know how to run an AI prompt or prompt AI for what you need, it is taking the knowledge and skill factor many used to bring to the table out of the equation,” says Christopher Hills, Chief Security Strategist, BeyondTrust.
“Unfortunately, this is also creating very lazy people, who are depending on AI to do their job. Do not get me wrong, while the upside is productivity and efficiency, there is a price to pay,” adds Hills.
“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,” points out Help AG’s Kukoljac.
“Skills in automation, orchestration, and behavioural analytics are increasingly essential, but so is the ability to collaborate with AI to outpace today’s adversaries,” says Anomali’s Depret-Bixio.
“Today’s security tools can only enhance human beings, not replace them. Much of the hype and promise of many solutions is that they can replace workers, yet the best implementations make your existing workforce more effective,” says Sophos’ Wisniewski.
“Teams struggle to keep up with rapid product evolution due to limited training or resource constraints. Even the best platforms underperform without a clear strategy, proper integration, and continuous review. The future of cybersecurity depends not only on innovation, but also on building capable teams who can unlock the full potential of these tools,” says Ilya Leonov, Regional Director MENA, Positive Technologies.
Familiarity with post-quantum encryption and blockchain applications for identity management is becoming important. Beyond technical acumen, cybersecurity teams need strong analytical, regulatory, and communication capabilities to work across functions and industries.
“Proficiency in integrating and managing diverse vendor technologies is vital in environments where clients run 50+ cybersecurity tools,” says NTT’s Baykal.
“Good communication and teamwork matter just as much as technical skills. Cybersecurity is a team effort, and being able to explain risks or train others is a big part of the job,” emphasises Osama Alzoubi, VP MEA, Phosphorus.
Gerald Beuchelt, CISO Acronis
The endpoint security market has matured, to a shift in focus toward enhancing EDR capabilities through cross-source data correlation and enrichment. There is also increasing emphasis on identity security, securing collaboration applications, and deeper integration with network security tools.
Following the popular rise and broad availability of LLMs since the early 2020s, cybersecurity vendors quickly began incorporating Generative AI into their offerings. This led to features like platform assistants, automated incident summarisation, and AI-assisted threat hunting.
Today, vendors are developing more advanced applications, such as agentic AI capabilities for autonomous Security Operations Centres. At the same time, as the limitations of LLMs become better understood, we are seeing more intentional and practical feature design.
Security teams now require specialised skills in key areas like cloud security, especially across multi-cloud environments, Identity and Access Management, and Zero-Trust implementation. Familiarity with DevSecOps practices and securing CI, CD pipelines is increasingly critical, especially in agile development environments.
Beyond technical knowledge, there is a growing need for expertise in threat intelligence analysis, incident response, and regulatory compliance. Soft skills such as stakeholder communication, risk prioritisation, and cross-functional collaboration are also vital, as security teams must now operate as strategic partners across the business.
A key limitation is the lack of automation. Many security operations still rely heavily on manual processes, making it difficult to counter fast-moving threats like credential-stuffing bots. Understaffed teams often struggle to respond to incidents in real time.
To improve resilience, organisations should adopt solutions that prioritise automation, allowing human analysts to focus on higher-value tasks and respond more efficiently during threat surges.
Alexandre Depret-Bixio, Senior Vice President, Anomali
Cybersecurity is about cleaning up the alphabet soup and converging into a unified, intelligent platform. In 2025, innovation means unified data platforms, not stitched-together stacks. Anomali cloud-native architecture ingests and correlates petabytes of telemetry and threat intelligence in real time, empowering organisations to detect, understand, and act in real time.
Anomali is moving beyond detection and response toward autonomous decision-making, AI-powered scoring, and anticipatory defence. The next frontier is about systems that think, learn, and act without compromise—at scale, speed, and precision.
With Anomali Query Language, the focus is on translating massive data into clear, confident action. Skills in automation, orchestration, and behavioural analytics are increasingly essential, but so is the ability to collaborate with AI to outpace today’s adversaries.
Many solutions still suffer from fragmented architectures, costly integrations, and limited visibility. Traditional SIEMs and bolt-on tools often throttle performance, restrict data access, or require trade-offs between speed and scale. They were not designed for today’s AI-driven threat landscape.
Even worse, reliance on outdated detection models and limited historical data leaves blind spots. Anomali addresses these limitations by redefining the architecture, unifying telemetry, and threat intelligence in a high-speed, cloud-native data lake.
Christopher Hills, Chief Security Strategist, BeyondTrust
Many vendors have innovated and developed their solutions to include more use cases to reduce risk, streamline business efficiency, and achieve the ROI their customers are looking for. Many of these developments and innovations have been in the identity space, from Strong Multi-Factor Authentication offerings, Access Management expansion and Governance and Compliance coverage to Cloud Entitlement management, and overall visibility to uncover where risks exist.
Other advancements and innovations taking place are integrations. Many vendors are finding value by integrating their products with other vendors, even if there is some overlap. The value multiplier by doing so, is much easier consumed by the customer and organisations, when it comes to operational efficiency and cost.
AI is such an abused word in today’s market – if you do not mention AI, you will not garner the attention of customers, so unfortunately, you must mention it, but it is also a catch-22. Is it really AI that is being baked into cybersecurity solutions? That is the real question. For those that are baking or embedding AI into their cybersecurity solutions, is it truly changing the landscape for vendors and organisations? While many are harnessing the benefits, we have not seen the downside yet.
Solutions are now able to run processes, fetch data, compute data, and provide outputs at efficiency levels we have never seen before. This includes everything from business task to risks and automation. Not to be a doomsdayer, there will be setbacks, risks, and exploits exposed, the bigger AI gets.
What is interesting is how the landscape for talent has changed and evolved when it comes to cybersecurity. Many used to define multitasker as a skillset, which is now frowned upon as a skill because that now means you are easily distracted.
In modern day cybersecurity, because many solutions, powered by AI, are doing more, much of the tribal knowledge that goes into being an SME or expert now becomes irrelevant. As an engineer, in previous years, one of the biggest skills you could bring to the table was the ability to troubleshoot. If you could trouble shoot a problem effectively, your value was through the roof.
Nowadays, in modern cybersecurity, if you know how to run an AI prompt or prompt AI for what you need, it is taking the knowledge and skill factor many used to bring to the table out of the equation. Unfortunately, this is also creating very lazy people, who are depending on AI to do their job. Do not get me wrong, while the upside is productivity and efficiency, there is a price to pay.
Some of the limitations with today’s cybersecurity solutions are part of its strengths. While many solutions are finding a way to do more or expand coverage of use cases, they are not always the right solution, or the enterprise solution, or the complete solution for tackling or mitigating risk.
Instead they are just enough. This then becomes a check box item for customers to declare they are doing or meeting a requirement, but this often still leaves less than desirable outcomes when you look at solutions that are positioned for addressing risk and closing gaps for security concerns. This becomes a very tight balancing act for organisations to understand what their Risk Appetite, Risk Tolerance, and Risk Acceptance is, and for many organisations it is never the same.
Ram Narayanan, Country Manager at Check Point Software Technologies, Middle East
Cybersecurity innovation is defined by the convergence of intelligent automation, cloud-native defence and platform-driven architectures. The focus has shifted from reactive measures to predictive, adaptive security.
Disruptive trends include quantum-resilient cryptography, Zero-Trust models tailored for hybrid work and behavioural biometrics for identity assurance. As cyber threats become more sophisticated, the industry is moving toward integrated ecosystems that can self-heal, self-learn and scale dynamically.
At the forefront of this evolution, Check Point Software unifies protection across cloud, networks, endpoints and IoT via the Infinity architecture—designed to provide consolidated, real-time security. By combining deep threat intelligence with scalable cloud capabilities, organisations can now defend against advanced threats while reducing complexity and operational overhead.
AI is becoming a crucial part of cybersecurity by boosting threat detection, automating tasks and speeding up responses. It can sift through enormous amounts of data to spot patterns and abnormalities, helping to stop security breaches. AI’s role in cybersecurity spans from streamlining routine tasks to upgrading situational awareness and decision-making.
For example, Check Point uses AI in its ThreatCloud AI and Infinity platform to deliver live intelligence, predictive threat detection and automated responses. Tools like the Infinity AI Copilot handle security management tasks, cut down on manual work and boost efficiency. Generative AI is also being investigated to further improve these abilities, offering smart, proactive solutions to tackle complex security issues and swiftly counter threats.
As cyber threats become more complex, enterprises are prioritising advanced skills in AI and machine learning, particularly for threat detection and response. Expertise in cloud security, particularly in managing hybrid and multi-cloud environments, is critical. There is also a growing demand for professionals skilled in Zero-Trust frameworks to mitigate risks across remote work and distributed networks.
Cybersecurity teams must also be proficient in managing and securing edge devices, including IoT systems. Finally, strong knowledge of data privacy laws and global regulations is essential as organisations navigate increasing regulatory complexity in 2025.
Cyber threats are constantly evolving, becoming more sophisticated and complex. To keep pace, cybersecurity solutions must also evolve. Protecting large, interconnected digital systems, such as supply chains, IoT devices and remote workstations, requires defences that are both flexible and scalable.
AI is essential for detecting new threats, but precision and resilience remain priorities. Additionally, there is a growing need for skilled cybersecurity professionals, making education and automation essential. Navigating the complexities of diverse global regulations adds another challenge. Improving cybersecurity will rely on continuous innovation and collaboration.
Biju Unni, Vice President, Cloud Box Technologies
Cybersecurity is becoming more proactive, intelligent, and adaptive. At Cloud Box Technologies, we see innovations like AI-powered behavioural analytics that detect real-time anomalies, preventing breaches before damage occurs. Another major leap is the emergence of quantum-resistant encryption methods, laying the groundwork for next-generation cryptographic security.
There is also a significant push towards autonomous threat response systems that minimise manual intervention and reduce incident response times. The future lies in customised, modular security stacks tailored to specific business needs that seamlessly integrate into existing IT frameworks, ensuring minimal disruption while delivering maximum protection in an evolving threat landscape.
AI and Generative AI redefine how we perceive and respond to cyber threats. At Cloud Box Technologies, we have embedded AI within our SOCs, enabling real-time behavioural analysis, anomaly detection, and intelligent automation.
These AI-driven systems continuously monitor data logs and user behaviour, drastically reducing detection time and allowing faster more precise threat mitigation. Generative AI goes further by helping simulate attack scenarios and stress-test security environments, enabling organisations to implement proactive and preemptive defence strategies. Together, they are transforming cybersecurity from a reactive shield to an anticipatory ecosystem capable of countering tomorrow’s threats today.
Modern cybersecurity demands deep technical expertise and adaptive thinking. Today’s professionals must be well-versed in AI, Zero-Trust architectures, cloud-native environments, and threat intelligence. Certifications like CISSP, CISM, and vendor-specific accreditations in AWS or Azure security frameworks have become essential.
At Cloud Box Technologies, we value multi-skilled teams capable of bridging technical capabilities with strong communication and analytical thinking. The rise of AI and ML has further amplified the need for data fluency and automation skills. Regular upskilling and cross-functional collaboration are now critical for navigating the complexity of today’s evolving threat landscape.
Fragmented security tools often operate in silos, hindering visibility and unified threat response. Over-reliance on AI without human oversight can lead to false positives or missed threats. Additionally, Generative AI, while powerful, is weaponised by threat actors to create sophisticated, hard-to-detect attacks.
The persistent cybersecurity skills gap continues to burden enterprises, delaying the implementation of robust security frameworks. At Cloud Box Technologies, we stress the importance of building a well-integrated, AI-augmented ecosystem that is constantly refined by expert oversight and upskilling, ensuring our clients remain resilient against current and emerging cyber threats.
Alain Penel, Vice President, Middle East, Türkiye and CIS, Fortinet
In 2025, cybersecurity challenges will evolve to become even more complex. Threat actors are becoming more specialised, especially in the early stages of attacks, focusing on reconnaissance and weaponisation.
Cybersecurity is heading in a direction where everything is becoming smarter, more connected, and easier to manage. The use of AI and quantum computing for example will continue to transform the threat landscape. Cybercriminals are already using AI to automate reconnaissance and streamline phishing attacks, and this trend will only grow.
On the other side, AI offers promise for real-time threat detection and response. Quantum computing, while still in its early stages, could disrupt traditional encryption methods, making it crucial for businesses to adopt post-quantum cryptography to protect sensitive data. These technologies highlight the need for businesses to stay ahead of the curve and rethink their cybersecurity strategies.
AI plays a significant role in cybersecurity, assisting businesses in staying ahead of constantly evolving threats.
As cyber risks continue to grow, it is important that organisations are empowered with solutions that streamline security processes, improve decision-making, and bolster resilience against evolving threats.
That is why Fortinet has expanded its Generative AI, offering, which enhances seven different products across our portfolio. By integrating FortiAI in such a broad range of solutions, we’re equipping our customers with powerful, adaptive tools that transform how they manage and respond to cyberthreats.
The cybersecurity landscape is always evolving and this means that professionals need to continuously learn specialised skills, especially in areas of AI-driven security, cloud security, and automation.
Automation is particularly important in cybersecurity given the ongoing shortage of expert security staff. However, human oversight will still be vitally important. Security teams will need to be equipped with the knowledge and skills to understand, interpret, and manage AI-driven security systems effectively.
AI excels at tactical responses based on predefined rules. However, defining security policies, understanding risk tolerance, and making strategic decisions still require human expertise and intuition. Analysing new and evolving threats, understanding their potential impact, and developing innovative countermeasures will also still require human intelligence and creativity.
Modern cybersecurity solutions are facing tough challenges from increasingly sophisticated cyber threats. These threats need continuous updates and new strategies to stay ahead. As organisations implement more AI-driven tools in particular, the complexity of managing multiple platforms and ensuring smooth integration becomes tricky.
Moreover, privacy concerns regarding AI and the protection of sensitive data are significant. Organisations must ensure their security solutions respect privacy while offering strong defence mechanisms against AI-driven attacks.
Hasanian Alkassab, Director Cyber Security, GBM
By 2025, cybersecurity will witness transformative innovations, including autonomous security operations, deep integration of real-time threat intelligence, and widespread 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. These technologies are driving a leap forward in speed, scale, and sophistication. AI detects anomalies across massive datasets with unprecedented precision, while Generative AI simulates sophisticated cyberattacks, enhancing readiness and resilience.
According to GBM’s latest security report, AI significantly enhances threat detection, automates security workflows, and improves operational efficiency. With over 37% of regional organisations citing increased efficiency and 22% noting enhanced creativity through Generative AI, embedding AI across the security lifecycle is no longer optional.
According to GBM’s latest annual security report, data privacy concerns remain a major issue for 31% of regional organisations, while 27% cite compliance and regulatory challenges — often complicated by varying Gulf country requirements.
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 next-generation security tools. Familiarity with Zero-Trust frameworks and regulatory compliance standards — including region-specific mandates — is important.
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.
Despite technological progress, key cybersecurity challenges persist. 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. Overcoming these limitations requires a holistic, regionally-informed approach that blends advanced technologies with strategic governance, local insight, and skilled professionals.
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.
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.
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. They create a proactive cybersecurity approach, instantly isolating compromised systems and patching vulnerabilities, the shift toward automated, AI-driven defence systems.
Today’s cybersecurity professionals need expertise in AI and machine learning, cloud security, and regulatory compliance. Skills in incident response, threat hunting, and ethical AI use are as essential as traditional cybersecurity knowledge. Ethical hacking and penetration testing are critical for identifying vulnerabilities from an attacker’s perspective.
Professionals must stay updated on emerging attack vectors and understand AI-related risks. Strong leadership and decision-making skills are vital for designing AI-driven cybersecurity strategies that align with regulations like GDPR or PDPL, requiring ongoing training and awareness programmes.
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.
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. Globant leads in tackling these issues, using Generative AI to enhance cybersecurity while prioritising human oversight and ethical implementation.
Ray Kafity, Vice President Middle East, Türkiye and Africa, Halcyon
Enterprise CISOs are navigating an era where digital environments grow more complex by the day, even as cyber threats become faster, more automated, and increasingly relentless. Ransomware, particularly in the form of Ransomware-as-a-Service, has grown into a sprawling criminal industry, often bypassing traditional cybersecurity defences. Halcyon notes that sophisticated extortion tactics—ranging from double to quadruple extortion—now dominate the threat landscape.
Meanwhile, gaps in endpoint visibility, ineffective patch management, and a shortage of skilled personnel further complicate response efforts. The pressure to reduce mean time to detect and respond, all while maintaining business continuity, places CISOs under constant strain to do more with limited resources.
CISOs expect cybersecurity vendors to deliver more than just tools—they seek strategic partners who understand evolving threat models and regional nuances. From Halcyon’s perspective, there is a rising demand for specialised solutions, particularly dedicated anti-ransomware platforms that offer behavioural detection, resilience, and recovery capabilities.
Vendors are also expected to provide rapid integration with existing infrastructure, transparent threat intelligence, and proactive support during incidents. CISOs want platforms that not only prevent attacks but reduce operational downtime and legal exposure. Trust is built on responsiveness, innovation, and a vendor’s ability to evolve alongside the threat landscape.
CISOs rely on cybersecurity channel partners to bridge the gap between technology and execution. The expectation is for partners to provide localised expertise, deep product knowledge, and proactive service—not just sales. In regions like the Middle East, where ransomware threats are surging, partners are also expected to contextualise global solutions for regional regulatory and operational needs.
CISOs value channel partners who co-own risk, anticipate emerging threats, and serve as long-term allies in building cyber-resilient infrastructures.
Modern CISOs must blend technical depth with strategic foresight. As cyber threats become more automated and AI-driven, there is need for leaders to master incident response frameworks, data recovery planning, and behavioural analytics.
CISOs must champion cross-functional coordination, risk quantification, and board-level communication. Strengthening the human element—through role-based training and cyber hygiene—is no longer optional. Future-ready CISOs are part technologist, part strategist, and full-time change agents.
Nikola Kukoljac, Vice President Solution Architecture, Help AG
From 2025 and beyond, cybersecurity solutions are expected to evolve to keep pace with rapidly interconnected systems and increasingly complex applications. A major trend is the surge in API security, driven by the need for seamless communication between modern applications and backend systems—especially as AI adoption grows across the region.
As businesses integrate AI into their operations, APIs become essential for enabling that communication, making their protection critical.
Another innovation area is data security. As AI models require large datasets for training, there is now increased demand for data protection technologies like data classification, masking, and organisation to safeguard sensitive information.
There is a strong focus on strengthening cyber defence and accelerating recovery—enhancing visibility, enabling faster, smarter threat detection and response, and ensuring minimal disruption to business operation.
AI and Generative AI are transforming cybersecurity by making platforms more intuitive and significantly more powerful. One major advancement is the use of natural language processing, which allows security professionals to interact with complex systems using plain English—eliminating the need for platform-specific query knowledge and accelerating adoption across teams.
AI is enhancing threat detection by analysing vast amounts of data in real time, identifying behavioural anomalies, and spotting early signs of attacks that traditional tools might miss. Together, these innovations are streamlining security operations and enabling faster, smarter responses to today’s evolving threats.
While new technologies like AI are gaining momentum in cybersecurity, the most critical skill sets for enterprise professionals remain rooted in the fundamentals. A strong grasp of how networks, operating systems, applications, endpoints, databases etc. work is essential. These foundational skills enable professionals to understand cyber hygiene, identify vulnerabilities, and assess risk effectively.
Modern tools, including AI, build on top of this knowledge—helping automate, accelerate, and enhance decision-making—but they cannot replace the need to know what you are looking for. In short, 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.
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.
This is driving a trend toward platformisation—where integrated solutions offer a unified user experience and allow for more streamlined threat detection and response. Simplification and interoperability are becoming essential to overcome the challenges of an increasingly fragmented security landscape.
Mohammed AlMoneer, Senior Regional Director, Middle East, Africa and Türkiye, Infoblox
In 2025, enterprises will face a critical inflection point as technology 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.
AI and Generative AI are being embedded in cyber security solutions to enhance threat detection, automate responses, and predict potential vulnerabilities. AI algorithms can analyse vast amounts of data to identify patterns and anomalies indicative of cyber threats.
Generative AI can be used to simulate attack scenarios, helping organisations prepare for and mitigate real-world incidents. Solutions like Infoblox’s SOC Insights for example apply AI-driven analytics to turn vast amounts of event, network, ecosystem, and DNS intelligence data into actionable insights to elevate SecOps efficiency.
Managing and implementing modern cyber security solutions requires a blend of technical and strategic skills. 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. Additionally, strong collaboration and communication skills are essential for working across IT, security, and operational teams. Continuous learning and staying updated with the latest cyber threats and technologies are crucial for maintaining effective security measures.
Despite significant advancements, modern cyber security solutions still face several limitations. One major challenge is the ever-evolving nature of cyber threats, which can outpace the development of defensive measures. Attackers are constantly finding new ways to exploit vulnerabilities, making it difficult for security solutions to stay ahead.
The DNS as first line of defence is something every company should leverage more to overcome this struggle. Additionally, there is a shortage of skilled cybersecurity professionals, which hampers the ability of organisations to fully leverage advanced security technologies.
Emad Fahmy, Systems Engineering Director, NETSCOUT
A key trend is the integration of visibility and security across hybrid infrastructures. Advancing this approach with a decent visibility strategy can offer real-time data insights for both performance and threat detection. Additionally, adaptive DDoS protection now uses artificial intelligence and machine learning to analyse extensive internet traffic.
This analysis creates intelligence feeds that identify threats early, enabling automated mitigation. This also includes new features like source host misuse detection and active campaign tracking, helping service providers minimise risk and ensure continuity.
AI and Generative AI are now central to both offensive and defensive cybersecurity strategies. Defence-side 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. As attacks grow in complexity and scale, AI-driven tools empower security teams to act swiftly and decisively, while reducing manual workloads and improving resilience across IT environments.
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 interpret detailed telemetry data all contribute to faster detection and more effective remediation.
Despite technological advances, many cybersecurity solutions remain overly reliant on perimeter-based defences like firewalls, VPNs and intrusion prevention systems. These tools are essential but they often miss internal threats such as lateral movement, insider attacks, and zero-day exploits.
Once attackers breach the perimeter, traditional systems lack visibility into deeper network activity. This blind spot allows malicious actors to remain undetected while executing advanced tactics, often mapped in the MITRE ATT&CK framework. Without robust internal traffic and packet-level monitoring, organisations face delayed detection, poor incident response, and greater risk exposure, making post-breach visibility a critical cybersecurity priority.
Harun Baykal, Head of Cybersecurity Practice Middle East and Africa, NTT DATA
In 2025 and beyond, cybersecurity solutions will be increasingly shaped by AI-powered threat detection, Zero-Trust architectures, and CNAPP and its components. Emerging innovations also include Post-Quantum Cryptography for enhanced encryption, decentralised identity solutions using blockchain, and privacy enhancing technologies.
With cyber threats becoming more AI-driven and sophisticated, solutions will emphasise anomaly detection, real-time automated response and remediation, and risk-based segmentation.
Channel partners will integrate multiple vendor technologies to offer scalable, tailored, and financially viable solutions. These innovations are essential as organisations face a growing threat landscape fuelled by deep fakes, ransomware-as-a-service, and supply chain vulnerabilities.
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.
Today’s cybersecurity professionals must master AI and machine learning fundamentals to effectively operate next-gen detection and response platforms. Cloud security expertise, especially in posture management and multi-cloud environments, is critical due to digital transformation. Skills in Zero-Trust architecture, OT, IT security convergence, and incident response planning are increasingly in demand.
Familiarity with post-quantum encryption and blockchain applications for identity management is becoming important. Beyond technical acumen, cybersecurity teams need strong analytical, regulatory, and communication capabilities to work across functions and industries. Proficiency in integrating and managing diverse vendor technologies is vital in environments where clients run 50+ cybersecurity tools.
Modern cybersecurity solutions face limitations such as over-reliance on technology without sufficient human oversight, and fragmented toolsets creating integration and visibility challenges. Despite advancements, threats like AI-driven deepfakes and ransomware continue to outpace detection capabilities.
Cloud misconfigurations and weak supply chain defences remain vulnerabilities. Budget and staffing shortages hinder effective response, especially for small to mid-sized businesses. Generative AI introduces new risks around misuse and unsecured deployment.
Additionally, many solutions are reactive rather than proactive and lack contextual industry awareness. These limitations underscore the need for tailored strategies, robust partner ecosystems, and ongoing investment in skills and innovation.
Tarek Abbas, Senior Director of Technical Solutions, EMEA South, Palo Alto Networks
In 2025, some of the key areas of focus for the cybersecurity industry are on AI-driven automation, real-time threat detection, and seamless cloud security. Products are evolving to also offer predictive analytics and faster response through AI-powered SOCs. Zero-Trust frameworks will be further embedded across networks, applications, and user access. Cloud security tools will offer more unified, end-to-end security for hybrid and multi cloud environments.
Enterprise browsers are also emerging as a critical layer of defence, offering secure access to web-based applications and helping enforce Zero-Trust at the browser level. These innovations aim to reduce complexity, enhance efficiency, and provide organisations with stronger, more proactive defence against increasingly sophisticated cyber threats.
As cyber threats become more advanced, AI plays a critical role in enabling faster, more accurate, and proactive defence strategies across organisations and industries. AI and Generative AI help enhance threat detection, automate response, and reduce manual workloads. Machine learning models are being integrated into traditional systems to analyse large volumes of data, identify patterns, and detect anomalies in real time.
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. AI tools can be vulnerable to misuse or manipulation if not effectively managed. Clear usage policies and robust safeguards are essential to ensure these technologies are both effective and protected.
With the rise of digital transformation, it is important for enterprises to have stringent measurement of cyber security efforts with the rising impact of AI on cyberattacks, in both defending against and executing cyberattacks. As AI and automation increasingly manage routine tasks like manual threat detection, cybersecurity roles will evolve – requiring updated job descriptions that focus on higher value, strategic decision-making and oversight.
Adopting greener practices will be necessary to minimise the environmental impact of digital infrastructures. With the increase in cybersecurity related regulations organisations will need to go beyond written, approved and implemented policies. A growing trend towards providing real-time evidence and assurance to regulators will become the norm.
Even with new technologies, today’s cybersecurity solutions have some clear limitations. Many tools do not work well together, making it harder to see and stop threats quickly. As attacks become more advanced, especially with AI, it is getting tougher to tell real threats from false alarms. There is also a big shortage of skilled cybersecurity professionals, which adds pressure to already busy teams.
Some tools cannot keep up with fast changes in cloud environments or new types of attacks. To stay protected, organisations need security that is not only smarter and more connected, but also platformed – where tools and systems work together seamlessly, offering a unified, AI-driven approach that is easier to manage and scale.
Osama Alzoubi, VP MEA, Phosphorus
We are in the year 2025, yet we have not addressed the basic challenge, zero-day vulnerabilities. Most of the successful attacks took advantage of a vulnerability that has an available patch; due to the scale issue most of compromised devices were not patched.
Cybersecurity innovations focus heavily on tackling the challenges posed by IoT environments. Zero-day vulnerabilities, which remain a critical issue, are being addressed with smarter, more efficient vulnerability assessment and mitigation tools. Innovations like the ones provided by Phosphorus will enable organisations to scan millions of IP addresses quickly, identifying risks and providing the ability to mitigate and patch at scale.
This will help prevent attacks that exploit unpatched vulnerabilities, a common weakness in IoT networks. Additionally, AI-driven solutions will continue to evolve, offering real-time threat detection and automated responses, making it easier to manage and secure vast, interconnected IoT ecosystems.
In the world of IoT, AI and Generative AI are becoming essential for cybersecurity. With so many connected devices generating massive amounts of data, security teams can easily get overwhelmed. AI helps by filtering out irrelevant information and focusing on actual threats, making it easier to spot unusual behaviour across a vast number of devices.
It can also predict potential vulnerabilities and automate responses to minimise damage. Generative AI is used to simulate attacks on IoT systems, test security measures, and create realistic scenarios for training. These technologies allow security teams to manage the complexity of IoT environments more effectively and stay ahead of cyber risks.
To keep up with modern cybersecurity, organisations need a comprehensive approach that covers Process, People and Technologies. Focusing on the skills, organisations need people with a mix of technical knowledge and practical skills.
Organisations need experts who understand how to protect cloud systems like AWS or Microsoft Azure. They also need people who can spot cyber threats quickly and know what to do when something goes wrong. It is important to have team members who can find weak spots in systems and build secure setups from the ground up.
With Zero-Trust approach, knowing how to control who has access to what is imperative. Computer Science coding and Hacking skills are a must, especially for automating tasks.
Good communication and teamwork matter just as much as technical skills. Cybersecurity is a team effort, and being able to explain risks or train others is a big part of the job.
With all the progress in cybersecurity, protecting IoT devices is still difficult. There are so many of them, there are at least 10 IOT devices for every IT device. They also come from many different vendors, on average an organisation would have around 50 different IOT vendors; each using different systems and security standards.
It is hard to keep track of them all, and many do not get regular updates or have strong protection built in. Most security tools were not designed to manage this huge mix of devices, so they struggle to detect threats or manage risks. As more devices connect to the internet, the problem keeps growing, making it harder to keep everything safe and secure.
Ilya Leonov, Regional Director MENA, Positive Technologies
Cybersecurity automation is reshaping industry landscape. In 2025 and beyond, we will see widespread adoption of automated penetration testing platforms, breach and attack simulation, as well as continuous security validation systems. These innovations will reduce reliance on manual effort and enable real-time insights into exploitable paths.
Integrated DevSecOps pipelines will leverage automation to enforce secure code practices across the software development lifecycle, SDLC. Combined with contextual threat modelling and cloud-native security orchestration, organisations will shift from reactive to proactive cybersecurity postures, reducing time to detect and respond dramatically.
AI and Generative AI are now core components of modern cybersecurity platforms. They enhance detection by identifying subtle anomalies and attack patterns that traditional tools often miss. Large language models, LLMs are also being embedded into Security Operations Centre, SOC workflows, helping analysts interpret alerts and generate automated investigation reports.
On the response side, AI orchestrates containment and mitigation across cloud and on-premises environments. In red teaming, Generative AI is used to simulate attacker behaviour more realistically. AI bridges the gap between the volume of threats and the shortage of expert resources, elevating both efficiency and precision.
Today’s cybersecurity professionals must master architectural thinking—understanding how security layers interact across hybrid environments. Skills in secure cloud infrastructure, DevSecOps, and automation frameworks, like Terraform and Ansible are critical. A foundation in identity and access management, network segmentation, and secure coding principles is essential.
Additionally, familiarity with AI-assisted threat detection, MITRE ATT&CK, and incident response frameworks empowers teams to effectively implement and fine-tune modern solutions. Importantly, soft skills such as risk communication and cross-functional collaboration remain key in aligning technical execution with business strategy.
Despite technical advancements, human factors remain a critical limitation. Sophisticated tools still require skilled professionals to deploy, configure, and maintain them effectively. Misconfigurations, lack of contextual tuning, and underutilisation of key features often leave gaps open.
Additionally, many teams struggle to keep up with rapid product evolution due to limited training or resource constraints. Even the best platforms underperform without a clear strategy, proper integration, and continuous review. The future of cybersecurity depends not only on innovation, but also on building capable teams who can unlock the full potential of these tools.
Emile Abu Saleh, Vice President Northern Europe, Middle East Türkiye and Africa, Proofpoint
The cyber security landscape is evolving as cyber threats grow in sophistication and scale. Threats are increasingly targeting individuals rather than systems.
Even as AI has widened the attack surface, it has emerged as an opportunity for organisations. Product innovations using technologies like AI, ML, and NLP are increasingly helping systems to detect, understand, and respond to threats in real time.
With data sprawl and multi-cloud environments on the rise, organisations need security solutions that unify threat protection, posture management, and user education to identify potential threats, like phishing, malicious code, and social engineering attacks.
Generative AI is a defining capability in today’s cybersecurity architecture, with its strength tied directly to data quality – AI is only as effective as the data it learns from. A few years ago, language and cultural barriers shielded Arabic-speaking regions. Now, advanced language models let attackers craft convincing, multilingual phishing emails. Our research shows that in 2024, the UAE saw a 29% rise in BEC attacks.
Simultaneously, organisations are grappling with fragmented data scattered across multi-cloud environments and applications, making visibility difficult. As employees use Generative AI tools, the risk of data leakage grows, and organisational data leakage is a serious concern.
There is a growing need for enterprise skills to strengthen modern-day digital infrastructure across network security and programming, cloud security, incidence response, and malware and threat detection.
The 2024 Voice of the CISO report shows that the biggest cyber security threats for UAE CISOs were cloud account compromise, 44%, ransomware attacks, 42%, and malware, 42%, highlighting a growing need for organisation-wide cyber hygiene. In an environment where human error is the #1 risk, organisations also require talent capable of building strong security cultures through education, behaviour change programs, and real-time threat response.
Despite rapid innovation in AI and Generative AI, many cybersecurity solutions still fail to address where most breaches originate: the human factor. Research shows that 76% of CISOs in the UAE identified human error as their top cybersecurity risk; in Saudi Arabia, that number jumps to 84%. Infrastructure-focused tools are not enough.
Generative AI could create realistic phishing, deepfakes, or hallucinated outputs, amplifying risks. Meanwhile, fragmented, siloed solutions lack visibility across collaboration platforms. Organisations need human-centric cybersecurity that secures every user interaction, not just the perimeter.
Without this shift, even AI-powered tools can fall short, missing context, mishandling sensitive data, or failing to stop insider-driven threats. A unified, adaptive security framework is essential to mitigate today’s people-driven threats.
Chester Wisniewski, Field CISO, Sophos
There is a lot of intense focus on process automation using Artificial Intelligence across product lines. AI will not be a major fix for any deep, complex problems, but it will help analysts perform their jobs more efficiently and automate away much of the boring work involved in triage and analysis.
We are seeing the most progress in process automation, translation from human speak to code speak, and alert management and triage. Many Security Operations Centres, SOCs receive thousands of alerts a day and by using AI to assist with this triage we can address the problem of alert fatigue that many analysts face.
Instead of writing complicated SQL queries, analysts can now ask AI to help build the query using human language to get answers faster when doing investigations. Another example is using Generative AI to help author reports and interpret scripts to help analysts quickly document their work as well as investigate instances more quickly.
As AI adoption increases, having skills in knowing how to ask the right questions, known as prompt engineering, is of increasing importance. Automation is the hallmark of any successful security program, so skills in scripting, process engineering, and AI are all required in all enterprise environments.
Today’s security tools can only enhance human beings, not replace them. Much of the hype and promise of many solutions is that they can replace workers, yet the best implementations make your existing workforce more effective.
A deep understanding of an organisations systems is required to assess threats, protect assets, and prioritise those things that present the most risk. Machines and tools cannot analyse all of this and produce results, they can only present the relevant information to the person who can apply that analytical judgement.
Maher Jadallah, Vice President, Middle East and North Africa, Tenable
Innovation within cybersecurity solutions will see the power and speed of Generative AI, such as Google Vertex AI, OpenAI GPT-4, LangChain and many others, harnessed to return new intelligent information in minutes. Generative AI offers the promise to be a game changer in this regard for cyber defenders, helping them to break down silos, correlating data from multiple cybersecurity point solutions and using the data to create something new.
This data amalgamation can inform security teams as they search for patterns, explain what is identified in the simplest language possible, which all helps to decide what actions to take to reduce cyber risk.
Generative AI is enabling security professionals to better interact with security data. Embedded within solutions, Generative AI makes it possible to automate detection and labelling to continuously identify, prioritise and manage risk for all resources, services and data. This takes security from reactive to proactive to reduce risk across evolving attack surfaces.
However, it is important to bear in mind the quality of the data used to power any AI engine. If you fail to educate the AI model correctly, then the model fails to deliver reliable results. It is gold in, gold out and vice versa. For now, humans should remain the ones making critical decisions on where and when to act.
What most do not appreciate is that cybersecurity is a big data problem. Typically, organisations rely on up to 140 disconnected security tools, creating siloes that hinder efficiency and create blind spots. As a result, data is often fragmented, disorganised and lacks context, making it difficult to effectively prioritise risks, report on security posture and answer basic security questions.
To secure the modern attack surface security teams need a unified approach to security that transcends silos, illuminating the attack paths that threat actors look to exploit. Prioritising where to focus efforts first, closes the gaps and wipes out these paths thus eradicating risks.
