AI and GenAI are having an increasingly pervasive impact on government — across missions, use cases, processes, and systems — in the Middle East and beyond. The disruptive impact of these technologies, compounded by geopolitical volatility, technical debt, digital sovereignty concerns, elevated citizen expectations, and regulatory changes, will require government leaders to approach innovation holistically. The acquisition and implementation of new technologies will not be enough. Realizing the benefits of AI, cloud, and industry platforms will require revisiting governance, risk management, culture, and the building of competencies to accelerate innovation.

Realizing the Value of AI at Scale in the MEA Region

The advent of GenAI prompted a surge of experimentation. Governments piloted GenAI for task automation, such as summarizing meeting minutes, drafting RFI and RFP documents for public tenders, creating job requisitions, synthesizing information to respond to freedom of information requests, and conducting research for the preparation of policy briefs. As pilot projects empowered them to evaluate benefits and risks, national governments and smart cities started to invest in scaling both traditional AI/ML and GenAI systems to address more complex industry-specific scenarios, such as service and benefits personalization, clinical care, and traffic safety. AI-enabled digital assistants started to help citizens interact with systems through conversational interfaces, instead of having to scroll through screens and fill out forms. Employee digital assistants started to help expert government case managers review, validate, and respond to citizen requests in a more holistic and personalized manner.

AI-powered governments will need to rethink their strategies, governance, people, and technologies to effectively adopt AI. This radical transformation will require governments to establish senior leadership roles that can build organizational capacities and competencies; design and enforce governance policies, structures, and processes; and deploy data and AI infrastructure, platforms, and application capabilities that align with strategic mission goals — all while complying with regulation. The MEA region is leading the charge; for instance, the Dubai government appointed 22 chief artificial intelligence officers (CAIOs) in 2024.

To achieve this level of automation, CAIOs need to work with line-of-mission and program leaders to re-engineer processes and systems so they can apply algorithms that recognize changes in their constituents’ circumstances, identify the root causes, and trigger operational workflows or dynamically reconfigure services and programs to meet constituents’ evolving needs and preferences.

From an architectural standpoint, this level of end-to-end process automation will require a combination of agents that will provide multimodal capabilities to process text, rules, and images, and will be orchestrated to deliver intended outcomes across end-to-end workflows.

To generate the desired outputs and outcomes from the application of AI and GenAI, government CAIOs and chief data officers need to feed data-hungry algorithmic training and fine-tuning. To avoid using low-quality datasets, which grow bias and hallucination, lower accuracy, and increase the risk of intellectual property infringement and other ethical and compliance risks, governments will invest in data logistics and control planes and establish governance polices and processes that enable them to control quality, reliability, and integrity of datasets.

Hybrid, multicloud environments are becoming the cornerstone for governments wanting to modernize their infrastructure, transform their applications, and take advantage of innovations such as AI and GenAI. FinOps practices and tools need to be in place to control costs, particularly as innovative capabilities are being tested and then scaled. AI will augment FinOps tools too, to optimize cloud resource sizing and usage, increase the transparency and accountability of cloud costs and carbon footprints, and detect anomalies.

Governments consider AI not only a tool for efficiency improvement, but a national strategic asset. They want to be able to harness AI to drive opportunities for the national AI innovation ecosystem and secure data and technical independence. This will drive new policy requirements for sovereign AI controls, such as data governance, data localization, and control requirements; scrutiny over hardware and software bills of material, algorithmic transparency, data protection, cybersecurity, and the ethical use of AI; and investments in local knowledge transfer. As a result of some of these policies, global cloud and AI platform companies have significantly increased their investments in local infrastructure and operations in the MEA region, with the Saudi Arabia and the UAE being the main beneficiaries.

As AI becomes more pervasive, robust security controls must be put in place, starting early on in the design stage for the hybrid, multicloud environments where these systems will be deployed. Security controls, along with updated governance policies and literacy programs, will be critical to ensure responsible AI innovation that minimizes the risk of misuse, such as generating misinformation, deepfakes, or biased content, as well as avoiding exposing systems to attacks and loss of sensitive and critical data.

Government CIOs and CAIOs that have a mandate to realize the benefits of AI at scale will have to develop trustworthy collaborative approaches to identify early wins, establish responsible AI governance and cybersecurity best practices, embed sovereignty principles in platform procurement and implementation, and apply FinOps best practices and tools to control the cost of innovation.

GenAI was coming. Predictive AI was coming.  No . . . wait, it was already here. Anyway, we sit here today focused on the art and the genuineness of the possible.

As we consider and dream of the possible, we sometimes forget the reality of the now. Between the hype around GenAI and the COVID-19 pandemic before that, we sometimes fail to acknowledge that cybersecurity has grown up. Once the dominion of hoodie-wearing basement dwellers, the topic has elevated to the C-suite and beyond. Attacks from the cyberthreat landscape do not just present a technical risk — the ramifications create a risk to the organization itself. In essence, cyber risk equals business risk.

Unlike many other corporate functions, cybersecurity did not develop from the typical path of strategy, goals, policies then tactics. It started in reverse with tactics first, then policies, then goals, and finally to strategy — if it made it there at all. The result is that formal strategy is really more of an amalgamation of small tactical decisions over time. This opportunistic cybersecurity strategy creation makes it challenging for organizations that are looking to create competitive advantages with AI. Thus, security needs to evolve from the tactical to the strategic, from being reactive to being proactive, from being an inhibitor to an enabler.

Cybersecurity leaders must now think strategically and act as business leaders alongside the executives of their organizations — creating insights, aiding executives in decision-making, and showing an organization’s risk posture are all critical for cybersecurity leaders’ success in today’s fast-changing threat landscape and regulatory environment.

The IDC Middle East CIO Summit looks to address security in this new reality of security becoming an enabling function for AI-created competitive advantage. We aim to guide you in working with the CEO and board of directors as we transition to delivering secure outcomes and a trusted organization to our executive constituencies.

In a rapidly changing technological landscape, companies face a pivotal decision: adapt to the transformative capabilities of artificial intelligence and automation or risk falling behind. A recent study by IDC reveals that the global artificial intelligence market stands at nearly $235 billion, with projections indicating a rise to over $631 billion by 2028, with significant investments in sectors such as software, banking, and retail. These sectors are increasingly leveraging AI to enhance operational efficiency and personalize customer interactions (Massey, K., 2024). With the rise of generative AI, more organizations are implementing these advanced tools, highlighting their integral role across industries.

As we look ahead in an increasingly digital world, businesses that harness the transformative capabilities of AI, robotics, and digital twins are poised to see significant productivity improvements, demonstrating a clear competitive advantage. The integration of these technologies is reshaping operational landscapes across various sectors. AI empowers organizations to analyze data at unprecedented speeds, enabling smarter decision-making and optimizing operational processes.

On the other hand, robotics is revolutionizing how physical tasks are executed. By automating repetitive and labor-intensive activities, robotics not only enhances operational efficiency but also enables human workers to focus on higher-value strategic activities. This synergy between AI and robotics significantly increases productivity while ensuring safety in environments that may be hazardous for human workers, particularly in supply chain and logistics.

Further, digital twins empower organizations to simulate scenarios, analyze performance, and predict maintenance needs, ultimately driving innovation and informed decision-making. Coupled with AI, these systems can analyze vast amounts of data in real time, leading to proactive adjustments and enhanced operational resilience. The integration of these technologies allows organizations to gain valuable insights that are crucial for sustainable growth and competitive differentiation.

However, the key to success lies in developing tailored solutions that meet the specific needs of each organization. By closely collaborating with clients to understand their unique challenges, businesses can design custom strategies that leverage AI and automation effectively. This personalized approach ensures that the implemented solutions align with operational goals, ultimately driving efficiency and fostering innovation. Organizations that prioritize customization in their technology deployments are better equipped to navigate the complexities of their industries and achieve long-term success.

Belief in the power of collaboration and innovation is essential. Together, businesses can navigate the complexities of the digital landscape and drive meaningful change, ensuring they not only keep pace but lead in their respective markets. With a commitment to expertise and innovation, organizations are invited to embark on this journey toward a smarter, more efficient future.

AI in the investment sector is a key enabler for transforming financial landscapes or M&A- and IM-focused organizations. Leveraging AI will revolutionize the investment sector and bring huge transformative changes that enhance decision-making, improve efficiency, and drive innovation.

Advanced data analytics are integral tools for investors, financial analysts, and portfolio managers since they lead to quick time to value for the following segments of business:

Investment Strategies

Using AI, we can fast-track pattern analysis of trends which might be missed when done via a human-only approach. It can analyze vast amounts of market trends, economic indicators, and even social media sentiment to predict and identify investment, their risk, and opportunities.

Quantitative & Predictive Analysis

Another key aspect in the investment sector is quantitative analysis, where one relies heavily on models/statistical techniques to evaluate investment opportunities. AI enhances this analysis with greater accuracy and speed and can uncover hidden insights, providing you with a competitive edge in the market.

Predictive analytics, on the other hand, allows investors to anticipate market trends and make proactive decisions by analyzing historical data and their patterns.

AI-Powered Investment Screening & Analysts: The New Advisors

With AI-powered platforms, financial advice and managed investment portfolios will change the way the industry manages portfolios, risk, risk tolerance, financial goals, and market conditions.

Risk Management

Effective risk management is crucial in the investment sector, and AI is playing a pivotal role in enhancing this aspect. AI-driven risk management systems can monitor and analyze market conditions, detect anomalies, and provide early warnings of potential risks. This enables investors to make informed decisions, enabling unknowns in the very early stages. In simple terms, AI-powered risk management processes make decision-making easy yet fast.

Fraud Detection

AI-powered fraud detection systems can analyze transaction patterns and identify suspicious activities that may indicate fraud. Machine learning algorithms can learn from historical data and continuously improve their detection capabilities. This proactive approach helps protect investors’ assets and maintain the integrity of financial markets.

Sentiment Analysis

AI’s ability to analyze unstructured data, such as news articles, social media posts, and financial reports, has given rise to sentiment analysis. Sentiment analysis tools can gauge public opinion and market sentiment, providing valuable insights into market dynamics.

For example, positive news about a company may indicate a potential rise in its stock price, while negative sentiment could suggest a decline.

Operational Efficiency with Automation

AI is streamlining various operational processes in the investment sector, leading to increased efficiency and cost savings by automating routine tasks such as data entry, compliance checks, and reporting. By automating these processes, investment firms can free up valuable human resources for more strategic activities. This not only improves efficiency but also reduces the risk of human error.

Enhanced Decision-Making

AI provides investment professionals with advanced analytics and real-time insights, enabling them to make more informed decisions. AI-driven dashboards and visualization tools present complex data in a user-friendly format, making it easier for analysts to interpret and act upon the information.Cost Efficiency

AI can offer a cost-effective alternative to traditional financial advisory. By automating investment screening, risk evaluation, and the end-to-end life cycle, organizations can significantly reduce the turnaround time of processes with great efficiency.

Conclusion

AI is undeniably transforming the investment sector, offering numerous opportunities for innovation, efficiency, and enhanced decision-making. However, we still believe it is essential to navigate the challenges and ethical considerations associated with this technology to ensure it is responsible, relevant, and reliable when augmented, since AI’s impact on the investment sector will undoubtedly grow, shaping the future of finance in profound ways.

Conglomerates, especially family-owned businesses, manage a complex array of industries, including real estate, manufacturing, construction, automotive, food, retail, logistics, and FMCG, to name just a few. Each industry comes with its own set of challenges, expectations, and leadership goals. At Al Ghurair Group, we have found that AI not only serves as a critical enabler of innovation but also acts as a unifying force to address the diverse needs across these verticals.

One of the biggest challenges is the differing demands and expectations from leaders within each sector. For example, in manufacturing and automotive, leaders may focus on precision, efficiency, and reducing downtime, while in retail, the emphasis may be on enhancing customer experience and personalization. In the food industry, sustainability and food safety are often paramount, while construction leaders may prioritize project management and cost optimization. Each vertical looks to technology for unique benefits, and AI allows us to tailor solutions to meet these varied demands.

At the holding or corporate head office (HO) level, the challenge is to ensure alignment between the diverse business verticals while maintaining group-wide governance, compliance, and reporting standards. AI supports these requirements by centralizing data management, improving visibility across subsidiaries, and enabling accurate and timely reporting. This not only strengthens decision-making at the group level but also ensures that strategic initiatives are cohesively implemented throughout the conglomerate.

AI enables us to harness data from multiple sectors and multiple resources, providing valuable insights that enhance decision-making at the executive level. Predictive analytics allows us to anticipate market trends, optimize supply chains, and streamline operations, while addressing the specific challenges of each industry. This ensures a cohesive strategy that benefits the entire group while also aligning with the goals of individual business leaders.

AI’s role in automating routine daily tasks and driving efficiencies is equally crucial. Whether we’re managing construction projects, enhancing customer experiences in retail, or optimizing production lines in our manufacturing and automotive operations, AI solutions ensure that processes are standardized and optimized across the board. The ability to implement adaptable AI solutions tailored to each business vertical — while maintaining centralized oversight — enables the group to deliver value across sectors rapidly.

Additionally, AI helps overcome the challenge of differing time horizons and business cycles within any conglomerate. Leaders in industries like real estate and construction may take a long-term view, focusing on sustainability and gradual growth, while in sectors like food and retail, shorter business cycles demand agility and quick decision-making. AI helps conglomerates bridge this gap by providing real-time insights and forecasting capabilities, allowing each vertical to operate optimally within its own cycle.

As we continue navigating an increasingly digital world, AI remains integral to maintaining the agility and competitive edge needed to thrive as a diverse conglomerate. It enables us to unify innovation while ensuring that each business vertical benefits from the latest advancements in technology, contributing to the success of the entire group.

Throughout my career I have seen some emerging technologies not entirely deliver on their expected promise, or their progress is hindered by regulatory and other constraints. Other technologies, mooted to transform our world, eventually find their niche, as in the case of blockchain and fintech, and others truly do become ubiquitous and land in the plateau of productivity — cloud computing being one.

Artificial Intelligence is revolutionizing various aspects of our world through its remarkable capabilities in predictive analytics, text summarization and generation, sensing our environment, and some impressive robotics.

The power of AI in leveraging data for predictive analytics cannot be overstated. Businesses are increasingly harnessing this capability to forecast trends, optimize operations, and make informed decisions. In the energy industry AI-enabled predictive maintenance is fast maturing increasing asset reliability and saving costs.

With respect to text generation and summarization, AI has made significant strides. Generative AI is already making inroads to transform corporate back offices and enhance productivity. From creating reports to summarizing complex documents, AI’s linguistic capabilities are revolutionizing how businesses handle information, making processes more efficient and accessible.

AI’s capacity to sense the environment is particularly evident in computer vision applications. For example, this technology is transforming drone operations, enabling the inspections of energy assets with accuracy. AI-powered drones can autonomously navigate complex environments, detect obstacles, and make real-time decisions, showcasing AI’s ability to interpret and respond to visual data.

AI is not just a passing trend but a fundamental shift in how we approach technology and problem solving. Its diverse applications in predictive analytics, text summarization, environmental sensing, and robotics are reshaping industries and creating new possibilities. As AI continues to evolve, its impact on our daily lives and various sectors of society will only grow, solidifying its position as a transformative force that is here to stay.

Customer portals can be a vital tool for businesses looking to improve customer experience and satisfaction rates. They provide a central location for customers to access information, manage accounts, and interact with your company. Without the right technology, however, building a customer portal can be a complex and time-consuming process.

That’s why it’s important for you to consider using a low-code development platform for your customer portal. Low-code platforms enable you to empower users who don’t have programming expertise to build applications with minimal or no coding, offering several advantages:

• Easier integration: Low-code platforms simplify integration with other systems, such as your CRM or ERP.

• Faster time to market: Low-code capabilities can help you launch your customer portal faster than traditional development methods.

• Improved user experiences: Low-code platforms allow you to create user-friendly and intuitive customer portals.

• Reduced costs: Low-code development can help you save money on your customer portal project.

Although low-code platforms streamline development, some may fail to provide the flexibility and scalability you need for the future. A digital experience platform (DXP) with low-code capabilities gives you the benefits of a strictly low-code platform while offering additional capabilities like CMS, DAM, and commerce that you can use to enhance your existing customer portal project or even build other solutions. Make sure you evaluate current project requirements as well as long-term goals when you’re selecting a technology vendor.

By understanding how low-code capabilities can help you create better user experiences for customers quickly with a customer portal, you can make the best decision for your technology investments while thinking about the evolution of your solutions and your business.

In 2025, we need to be prepared for increasing sophistication of existing challenges. Phishing is a clear example; the 2024 UK Government’s Cyber Security Breaches Survey identifies it as the most predominant attack vector, affecting 84% of those breached. While phishing itself is not new, cyberattacks like this have only grown in complexity as attackers exploit “mega trends” in technology that include artificial intelligence and cloud computing, among other factors.

A pivot toward “right of bang” thinking is needed, shifting the focus to what happens after an inevitable breach (the “bang”), aiming to build resilience in the center of business operations. This shift acknowledges that cyberthreats are not solely issues for IT departments but for entire businesses, with the end goal being cyber maturity.

A key driver of this strategy for 2025 is the recognition that organizations often lack this resilience in their cybersecurity positions. Traditional approaches have prioritized prevention but have not effectively prepared organizations for rapid recovery. Today, resilience means having both defense capabilities and recovery plans. This requires more than just the solution support — it also includes fostering a cyber-aware culture across all levels, from leadership teams all the way through to employees who recognize their own roles in safeguarding data.

Looking ahead, the pivot toward a resilience-first strategy in cybersecurity is likely to define success for businesses worldwide. As cyberthreats grow in scale and complexity, the emphasis on recovering quickly and effectively is no longer optional — it’s essential. Organizations must adapt to the new normal of unavoidable cyber incidents and take proactive steps to ensure they can withstand and bounce back from potential breaches. The board-level objective of meeting one’s survival time objective is now front and center for many organizations, and cyber-resilience solution stacks help fulfill this ask.

As industrial and critical infrastructure organizations adopt AI to power already automated processes, the vulnerability of AI systems themselves is an emerging concern. Devious tactics such as data poisoning, large language model prompt injection, or ML model evasion pose a formidable challenge as threats.

On the defenders’ side, the need to analyze and correlate vast amounts of data from dozens of sources presents a prime use case for AI and ML. Indeed, by now most cybersecurity vendors have incorporated AI into their products to various degrees. Today, you can assume that ML and behavioral analytics are at work throughout your cybersecurity stack to improve the speed and accuracy of every process.

AI-assisted OT cybersecurity requires even greater capabilities because, as we know, there’s more to protect and the stakes of an attack are often higher. You have control systems and physical processes, with configurable process variables, all potentially exploitable.

Organizations that manage critical infrastructure and other industrial environments must enlist AI to tackle every stage of the cybersecurity lifecycle — identify, protect, detect, respond, and recover — but with extra functionality to secure OT/IoT networks. Prime use cases include:

• Using ML to learn the behavior of process variables collected from network traffic and highlighting anomalies from the baseline time series

• Predicting and acting on abnormal bandwidth from each sensor’s baseline network activity

At Nozomi Networks, using AI to tackle the toughest OT/IoT security challenges is in our DNA. We introduced the industry’s first AI-powered visibility and cybersecurity solution for industrial control systems (ICS) in 2013, and we’ve been building AI into our platform ever since.

With the rapid advent of artificial intelligence (AI), the world is undergoing yet another seismic transformation. Every industry is racing to integrate AI to gain a competitive edge. According to the World Economic Forum’s Future of Jobs Report, AI is projected to displace 85 million jobs by 2025 while simultaneously creating 97 million new roles, signaling a shift in the division of labor between humans, machines, and algorithms.

This transformation has equally significant implications for cybersecurity. Many existing technologies are becoming obsolete, and only those organizations leveraging AI effectively will thrive in this changing landscape. Amid this, security information and event management (SIEM) solutions have emerged as the critical “command center” for organizational security — a role that continues to evolve to address modern challenges.

The Evolution of SIEM

From its inception in the late 1990s, when logs were primarily used for troubleshooting, SIEM has matured significantly. Early SIEM platforms (SIEM 1.0) combined security event management and security information management but were limited by their vertical scalability. The cloud revolution ushered in SIEM 3.0, incorporating advanced analytics, machine learning, and user and entity behavior analytics (UEBA). These platforms combined SOAR and threat intelligence capabilities, enhancing incident response and detection, but challenges like insufficient detection coverage and complexity persist.

Challenges and AI-Powered SIEM 4.0

To address these challenges, customers and vendors alike are demanding next-generation capabilities. Here’s what SIEM 4.0 powered by AI promises to deliver:

1. Enhanced Threat Detection: The number 1 challenge for today’s CISOs and SOC managers is detecting threats in real time. AI enables faster and more accurate anomaly detection, predictive analytics, and threat chaining, combining behavior analytics with data sets like cloud logs, on-premises attack surfaces, and external intelligence. This approach helps detect potential threats before they escalate.

2. AI-Assisted Threat Hunting and Investigation: The shortage of skilled SOC personnel and the complexities of threat investigations are significant hurdles. AI can convert raw alerts into actionable insights, generate detailed compliance reports, and recommend next steps. For example, AI can write queries, summarize findings, and even suggest remediation steps, saving analysts valuable time.

3. Automated Threat Response with Agentic AI: Trust in automated responses is a challenge for many organizations. Agentic AI systems, however, can autonomously detect, analyze, and triage security alerts. These systems understand service dependencies and generate infrastructure as code (IaC) for DevOps approval, reducing errors and boosting adoption.

4. Scalable, Resilient Architectures: Modern SIEM platforms are embracing microservices architectures, offering independent scalability for different components. This design enhances performance, resilience, and fault isolation while enabling cost-effective scaling to manage increasing cybersecurity demands.

The Future of SIEM

As we look toward 2025, the divide between traditional and AI-based SIEM will grow. Organizations adopting SIEM 4.0 can expect more intelligent, proactive, and efficient security monitoring powered by advanced analytics and automation. In this era, AI isn’t just an enhancement — it’s the foundation of the next generation of cybersecurity, enabling faster threat detection, streamlined workflows, and adaptive responses to evolving challenges. This paradigm shift will redefine how organizations protect their digital ecosystems, making AI-driven SIEM an indispensable tool for modern security operations.

You want to innovate with AI, but your ERP vendor mandates a cloud reimplementation, leaving your customizations and data behind? Instead, choose a better way to maximize potential, gain flexibility, and reduce risk; here’s how.

Whether viewed through a lens of doom and gloom or hype and hope, the reality is that AI is on the leading edge of revenue generation, competitive advantage, and corporate growth.

Conquer the Cloud-Only Conundrum

If vendors restrict their new AI offerings to cloud-based services only, where does that leave customers who want to retain their current, customized systems? Good news: Your organization can leverage the power of AI with your current systems, no reimplementation required. After all, the lifeblood of the AI revolution isn’t just the technology itself, but the data that’s fed into it.

Your Data Is the Lifeblood of AI

As new AI technology leaders emerge, organizations can use those AI services on top of their current enterprise datasets by “innovating around the edges” of their stable core ERP.

One reason why a composable ERP strategy is helpful when adopting AI technologies: One of the guiding principles of composable ERP is to have a robust data orchestration layer. Because there are multiple applications and technologies interacting with your ERP system in a modular fashion, you must ensure that these systems are interoperable. This requires a defined data strategy, which provides a foundation for future technologies to dock into your overarching ERP system; this same data orchestration layer will be able to pipe in clean, relevant data to whichever AI technology you need.

Maximize Potential, Gain Flexibility, Reduce Risk

In the same way that software ate the world in the early 2010s, AI is revolutionizing business in the 2020s. And because they’re the primary data store for enterprises, ERP systems will have an outsized role in this digital transformation. Successful organizations understand this, and are prioritizing their investments accordingly, leveraging the data they have today.

People, time, and money are your most precious resources. By rethinking ERP vendors’ clarion call to cloud, businesses can free up these precious resources to invest in important AI initiatives to innovate around the edges.

As data becomes increasingly central to business operations, having the right enterprise backup and recovery solutions is more important now than ever. With data volumes on the rise, cyberthreats becoming more sophisticated, and IT environments growing more complex, it’s crucial for businesses to adopt effective strategies to safeguard their data and ensure quick recovery from any disruption. Here are five best practices to help you build a stronger, more resilient data protection framework for 2025.

1. Make the Most of Cloud Resources for Scalable Backup and Recovery

Using cloud resources isn’t just about keeping up with the latest trends — it’s a smart move for businesses that want to enhance their backup and recovery capabilities too. The cloud offers the flexibility and scalability needed to efficiently manage growing amounts of data.

2. Beef Up Security to Guard Against Cyberthreats

Cyberattacks are getting more sophisticated, and backup systems are often in the crosshairs. This means that if your backups aren’t secure, your recovery options are limited. It’s crucial that you have a plan that doesn’t just respond to threats, but actively works to prevent them.

3. Streamline Operations with Backup Services

Managing backups in-house can be a heavy lift that requires specialized skills and constant monitoring. That’s where backup services come in! Backup services can take the load off your IT team and free them up to focus on more strategic tasks.

4. Update Your Backup Strategy for SaaS and Containers

If your organization relies on SaaS applications and containerized environments, traditional backup methods may not cut it. Modern data protection needs to include these newer architectures to keep your data safe across all platforms.

5. Use AI and Automation for Smarter Backup Operation

AI and automation are revolutionizing how enterprises manage their backup and recovery processes. These technologies can handle repetitive tasks, detect potential issues before they become problems, and even improve the overall efficiency of your data protection strategy.

Conclusion

To keep your data secure in 2025, it’s crucial that you adopt these five best practices for enterprise backup and recovery solutions. By integrating cloud resources, enhancing security, utilizing managed services, modernizing SaaS and container workloads, and leveraging AI and automation, you can build a resilient and future-proof strategy.

The retail industry is undergoing a significant transformation, with serverless architecture emerging as a key enabler for businesses looking to adapt and thrive in the digital age. As online channels increasingly surpass traditional brick-and-mortar stores in importance, retailers face the challenge of delivering differentiated and seamless omnichannel experiences to stay competitive.

Serverless technology offers a strategic advantage, particularly for retailers struggling to attract scarce technical talent. By minimizing the need for DevOps and infrastructure management, retailers can reallocate resources toward value creation and innovation. This shift is crucial as consumers demand more personalized and responsive shopping experiences, especially during peak times.

Moreover, serverless architecture allows retailers to efficiently manage fluctuating traffic with minimal operational overheads, ensuring that online and offline channels are effectively linked. Whether in fashion, F&B, or quick-service restaurants, the ability to scale automatically and pay only for what is used positions serverless technology as a game changer for retailers aiming to lead in an increasingly competitive market.

In the coming years, embracing serverless solutions will be essential for retailers that wish to innovate rapidly, optimize costs, and maintain agility in a fast-paced environment.

OT cybersecurity maturity continues to become more and more important every year. With unprecedented attacks targeting critical infrastructure, here are three critical areas of focus for strengthening OT security in 2025 and beyond.

1. Minimize the Attack Surface

Reducing the attack surface is vital as cyberthreats target vulnerabilities beyond the perimeter. Attackers often exploit weak points such as internet-accessible devices, remote services, or dual-homed assets connected to both IT and OT networks. Organizations must prioritize controls like remote access management, network segmentation, and peripheral media security to address these weaknesses. Implementing comprehensive access controls and isolating critical systems can help contain threats before they spread laterally.

2. Secure OT-to-IT Data Transfers

Data flowing between OT and IT environments presents a significant risk if not properly secured. Adopting hardware-enforced secure data transfer solutions, such as data diodes, can prevent misconfigurations and ensure compliance with industry regulations. Reliable data replication safeguards integrity while reducing potential entry points for attackers. Securing these data flows is essential for reducing cross-network risks and protecting sensitive operations.

3. Enhance Visibility and Monitoring

A lack of visibility remains a significant challenge for OT security, with only 5% of organizations reporting full visibility into their environments. Deploying advanced monitoring tools, conducting regular audits, and maintaining a comprehensive inventory of OT assets can bridge this gap. Real-time insights into system activity and vulnerabilities enable faster threat detection and response, ultimately reducing the likelihood of operational disruptions.

CIOs must prioritize investments in strategies that address these focus areas, ensuring alignment between IT and OT security initiatives. By taking a leadership role in fostering visibility, reducing vulnerabilities, and safeguarding data transfers, CIOs can position their organizations to thrive in a rapidly evolving threat landscape while protecting critical infrastructure.

The pressures on modern digital businesses are relentless. The rapid proliferation of new applications and services is driving an unprecedented surge in the volume, variety, and velocity of data. This flood of information generates a cascade of alerts, overwhelming IT teams and making it nearly impossible to manually analyze and correlate data while meeting operational expectations. As businesses scale, this challenge intensifies, demanding a more efficient, intelligent approach to IT operations.

This is where AIOps comes in. AIOps, or artificial intelligence for IT operations, leverages advanced analytics and automation to transform how IT teams manage their environments. By utilizing big data, machine learning, and multi-modal AI techniques such as causal, predictive, and Expert System AI, AIOps enables businesses to optimize IT operations for efficiency, reliability, and agility. Let’s explore how AIOps supports sustainable business growth:

Decreasing Time to Detect

At its core, AIOps excels in identifying unusual patterns or anomalies in IT operations data. Through event correlation, contextual insights and root cause analysis, AIOps significantly reduces the time required to detect issues. Faster detection ensures that applications and digital services perform as expected, which translates to satisfied customers and uninterrupted business operations. As one Riverbed customer noted, “There’s too much information for any one individual to know what’s going on 24/7/365. Riverbed IQ can help alleviate this by allowing SMEs to develop automated workflows — things we would normally check and do when a problem or outage occurs.”

Automating for Efficiency, Quality, and Speed

Advanced AIOps capabilities, such as automated investigation and remediation, enable IT teams to streamline workflows. By automating tasks like collecting diagnostic data, contextualizing incidents, prioritizing events based on business impact, and suggesting remediation steps, AIOps minimizes human intervention in repetitive processes. This not only improves operational efficiency but also enhances the quality and speed of issue resolution. Freed from the burden of routine tasks, IT teams can redirect their focus toward innovation — a critical factor for achieving market differentiation and driving business growth.

OP-portunity Abounds

For IT leaders, adopting AIOps represents a transformative opportunity to enhance digital user experiences while streamlining operations. By integrating AI-driven insights into IT workflows, organizations can proactively prevent, identify, and resolve issues, ensuring efficient and reliable digital services. In a world of increasing complexity, AIOps provides the intelligence and automation necessary for sustainable growth in the digital age — and if augmented with GenAI, the sky is the limit.

In the face of ever-increasing attack trends, organizations are deploying a diverse range of security solutions across on-premises, cloud, and hybrid environments. These tools are essential for safeguarding assets, but their sheer volume and complexity introduce new challenges. Whether managed by internal teams or through outsourced operational services, ensuring that these security products are correctly implemented and consistently maintained is a time-consuming and resource-intensive process.

The first step in managing these solutions involves the meticulous deployment of tools across all assets. This includes not only implementing the solutions but also verifying that the correct policies are in place and being enforced. A single misconfiguration or overlooked policy can leave critical gaps, exposing organizations to unnecessary risks. Yet, in dynamic environments where attack surfaces are constantly shifting, this process requires continuous attention, stretching the capacity of even the most skilled teams. What makes this even more challenging is the need for constant monitoring to ensure the effectiveness of security devices.

With the growing complexity of today’s cybersecurity ecosystem, relying solely on manual oversight is no longer sustainable. The operational burden on teams to track alerts, analyze system performance, and measure compliance can detract from higher-value strategic initiatives. This is where the need for automated, human-independent monitoring becomes critical, including governance, risk, and compliance (GRC) platforms and internal and external attack surface visibility tools.

With the help of these tools, you can establish clear KPIs to track your security solution’s implementation and effectiveness and continuously measure progress.

Proactive reporting, combined with actionable insights, enables organizations to not only respond to immediate threats but also make data-driven improvements to their overall security posture and compliance to regulations and frameworks like ISO 27001, GDPR, or NIST CSF.

In conclusion, navigating today’s complex cybersecurity landscape demands a proactive and automated approach. By leveraging GRC platforms and attack surface visibility tools, organizations can establish robust KPIs, ensure continuous improvement, and maintain compliance with critical frameworks such as ISO 27001, GDPR, and NIST CSF. This not only enhances operational efficiency but also empowers organizations to address evolving threats with confidence and resilience.

Quality engineering has become the backbone of modern operations in today’s fast-paced digital business world, where competition is relentless, and expectations are sky high. To illustrate its impact, imagine trying to run a marathon with a backpack full of bricks. That’s your company without DevOps. Now, picture running that same marathon with a dedicated support team solving problems as they arise. That’s the power of DevOps and quality engineering!

Understanding the importance of DevOps, quality assurance, and test automation in software development today means keeping in mind that these concepts are not just fancy buzzwords but the foundation of delivering top-tier applications and services, ensuring that your business isn’t just staying afloat but thriving. By embracing a quality engineering framework, companies can foster a culture of collaboration that drives efficiency, innovation, and scalability while securing a competitive edge.

DevOps is no longer optional — it’s necessary. It transforms how businesses operate by boosting development by up to 30% and accelerating time-to-market by 70%. Beyond speeding up processes, DevOps ensures scalable, resilient operations. It empowers teams to deliver high-quality software, resolve issues predictably, and improve customer satisfaction. Streamlined workflows allow innovation to flourish, turning ideas into reality faster and helping businesses stay ahead in competitive markets.

The latest insights from our IDC InfoBrief, Quality at Scale: 2024 Edition, revealed how technologies like AI, automation, and low code enhance DevOps capabilities. That is why a key development in DevOps is the rise of AI-driven automation. By 2028, it’s predicted that generative AI tools will handle up to 80% of software test creation. While AI significantly accelerates testing processes, human oversight remains crucial. It ensures accuracy, validates results, and aligns automated tests with real-world needs, maintaining the highest performance standards.

Let’s wrap this up with the real kicker: quality engineering is a transformative force shaping the future of business. It’s not about hopping on the latest trend — the race is already on. Those who embrace DevOps, quality assurance, and automation today will lead the way, driving innovation, delivering high-quality solutions, and scaling their businesses in an ever-evolving technological landscape.

As AI continues to evolve at an unprecedented pace, 2025 promises to be a pivotal year for redefining the roles of data, insights, and automation in business. The rapid adoption of AI introduces a blend of promise and peril, compelling organizations to adapt quickly to stay competitive.

At Qlik, we have identified three overarching themes that will shape the year ahead: authenticity, applied value, and autonomous agents.

1. Authenticity: The surge in AI-generated content has spurred a crisis of authenticity, with an estimated 57% of online content now produced by AI. As large language models (LLMs) increasingly rely on AI-generated data, the integrity of these systems is at risk. Businesses must prioritize high-quality, verifiable data, with initiatives like AI Trust Scores to establish provenance and reliability. Additionally, unlocking value from “dark data” and utilizing interoperable platforms are crucial steps toward building trust in AI systems.

2. Applied Value: Organizations are moving beyond AI hype to seek measurable results. Practical applications are prioritized over infrastructure investments, with generative AI (GenAI) tools enabling innovations in data interaction and decision-making. However, challenges persist, including data quality, cost management, and the need for clearer business value. Successful strategies will integrate GenAI with conversational interfaces and tailored use cases to unlock AI’s full potential.

3. Autonomous Agents: The era of autonomous agent systems is dawning, characterized by self-sufficient AI tools capable of executing complex tasks. Multi-agent architectures and real-time data processing are reshaping workflows, enhancing operational efficiency, and driving smarter decision-making. To realize these benefits, businesses must invest in robust data foundations, seamless process intelligence, and adaptive systems.

These themes are deeply interconnected — without data authenticity, the value and reliability of AI diminish, and without applied value, the potential of autonomous agents remains unrealized. As businesses embrace these trends, they must balance innovation with governance to ensure AI’s positive impact.

2025 will be a year of transformation for organizations willing to harness AI’s power responsibly. By focusing on authenticity, delivering practical value, and preparing for the agentic systems era, businesses can navigate this dynamic landscape and achieve sustainable growth.

As the MENA region advances in innovation, we are entering the “Intelligent Era,” defined by the widespread use of cutting-edge technologies like AI, machine learning, and data analytics. In this era, organizational regeneration is crucial for success. This transformation goes beyond technology adoption; it is about reshaping businesses to be more agile, resilient, and human-centric in a constantly evolving landscape. According to IDC’s Digital Executive Sentiment Survey, 2024, 78% of executives believe their organizations require a complete overhaul or new investments in AI and other areas such as cloud (73%) and integration tools (61%).

Internal Readiness and Change Management: Key to AI Success

Successful AI adoption relies on two critical factors: internal readiness and strategic change management. Internal readiness ensures AI initiatives align with business objectives, prepares infrastructure, and upskills the workforce to collaborate effectively with intelligent systems. By intelligently aligning people, processes, and technology, organizations can adapt, create value, and achieve long-term success in an AI-driven world.

Building a Holistic Framework for AI Adoption

Successfully managing AI-driven change requires a comprehensive framework built on these core elements:

1. Build an AI-Ready Culture: Cultivating a culture that embraces AI involves promoting continuous learning, knowledge sharing, and alignment with the organization’s core values. This shift helps employees see AI as a tool for enhancing their work rather than a threat.

2. Drive Employee Engagement and Communication: Engaging employees early in the AI adoption process is essential for buy-in and a smooth transition. Transparent communication about AI’s goals, benefits, and potential challenges fosters trust and minimizes uncertainty.

3. Align Strategic Leadership: AI initiatives must align with the organization’s strategic objectives. Leadership commitment is key to guiding transformation, ensuring cross-departmental alignment, and keeping the organization focused on its goals.

4. Prioritize Continuous Improvement: AI adoption is an ongoing journey. Organizations should regularly evaluate and refine their AI strategies to ensure they remain effective and aligned with evolving business needs.

By focusing on these elements, organizations can navigate the complexities of AI adoption more effectively, leading to sustainable growth and a competitive advantage in the evolving business landscape.

Positioning MENA Organizations as Pioneers in the Intelligent Era

By adopting a strategic, human-centric approach to AI, organizations in the MENA region can position themselves as leaders in the Intelligent Era, using AI not only for automation but as a tool for holistic business transformation.

AI continues to transform industries and reshape how organizations operate. As we move into 2025, its impact on application development is more profound than ever. Organizations across the region are looking to AI-powered solutions to enhance efficiency, boost productivity, and deliver business value. As AI becomes more integrated into enterprise strategies, IT leaders must prepare for the shifts ahead. Here are five key AI predictions that will shape the future of application development:

AI Prediction #1. AI and Low-Code Will Elevate Developers to Strategic Roles

AI-driven low-code platforms will no longer just generate code, they will generate entire applications. Developers will move beyond routine tasks, using AI-powered low code to focus on strategic initiatives and engage more with decision-makers. As a result, developers will play a more prominent role in shaping technology decisions, bridging the gap between IT and the C-suite.

AI Prediction #2. AI Interactions Will Go Multi-Modal

AI-powered applications will expand beyond text-based interactions to include images, video, and voice. Users will provide multimedia inputs such as screenshots or recorded instructions to refine application features, streamlining workflows, accelerating development cycles, and improving user experiences. This shift will enable IT teams to create and optimize applications with greater efficiency.

AI Prediction #3. Conversational AI Will Redefine Customer Engagement

Customer interactions will evolve from traditional interfaces to AI-driven, natural language experiences. Instead of interacting with windows, buttons, or forms, users will increasingly engage with businesses through text, audio, and video prompts. IT leaders will have the opportunity to prioritize the customer in technology decisions, increasing retention and loyalty.

AI Prediction #4. Security, Ethics, and Governance Will Take Center Stage

AI will continue to evolve, expanding its interactions through autonomous systems and devices. This will increase AI-controlled interactions, introducing new ethical, security, and governance concerns. IT teams will prioritize advancements in security and governance mechanisms, implementing DevSecOps practices to ensure AI applications are deployed securely, responsibly, and in compliance with regional regulations.

AI Prediction #5. Data Integration Will Be Key to GenAI Success

Organizations adopting GenAI will face challenges related to data integration, quality, and privacy. IT leaders and their teams need to prioritize data consolidation and integration, utilizing unified platforms to unlock the full potential of AI while ensuring data privacy and compliance.

AI and Low Code Are the Way Forward

AI and low code are transforming how organizations create, customize, and modernize applications. By leveraging new technological advancements, organizations can achieve efficiency, scalability, and innovation, empowering IT leaders to drive agility, build resilience, and gain a competitive advantage in 2025 and beyond.

As businesses increasingly recognize the transformative potential of generative AI (GenAI), preparing for its implementation becomes a strategic imperative. While aspirations for GenAI are high, success depends on careful planning that integrates robust cloud infrastructure and comprehensive data readiness.

GenAI relies on high-quality, curated and proprietary data that is continuously refreshed. This requires organizations to prioritize thoughtful data operations, intelligent data management and the deployment of adaptive data platforms.

Steps for data readiness

To effectively prepare data for GenAI projects, organizations should:

1. Identify relevant data sources: Conduct comprehensive knowledge sessions to determine all data sources required for training the GenAI model.

2. Assess data quality: Utilize tools to evaluate data quality and accessibility, consolidating disparate sources into an adaptive platform.

3. Address ethical and regulatory concerns: Ensure data usage complies with intellectual property and regulatory standards, while establishing processes for transparency and explainability.

4. Develop continuous data integration plans: Regularly update models with new data to maintain relevance and accuracy.

Ethical considerations

Proprietary data often originates from customers, employees or partners, creating ethical responsibilities. Businesses must ensure that data collection and usage benefit all stakeholders, adhering to principles of transparency and fairness. Furthermore, as regulations around data evolve, organizations need clear processes for data selection, model training and monitoring to mitigate risks and maintain trust.

The role of cloud infrastructure

GenAI has shifted the focus of cloud use cases beyond traditional applications like disaster recovery and testing. Today, organizations are leveraging cloud-native development to support scalable and efficient GenAI solutions. Investing in platform engineering automates complex cloud-native processes, enabling robust application management and scalability.

Addressing talent challenges

A common challenge in GenAI projects is the scarcity of skilled professionals with expertise in AI and cloud-native development. To bridge this gap, businesses are increasingly partnering with experienced technology providers who can offer both engineering and GenAI expertise. These collaborations facilitate knowledge transfer and accelerate in-house talent development, ensuring long-term success.

A multicloud future

Emerging trends indicate a growing adoption of multicloud environments. This approach enhances security and provides access to the latest technologies. The persistence of hybrid cloud architectures highlights the distributed nature of the data powering GenAI solutions, further emphasizing the need for seamless integration across on-premises and cloud infrastructure.

Aligning data strategies with cloud capabilities

To fully realize the benefits of GenAI, businesses must align their data strategies with the capabilities of cloud platforms. Prioritizing data readiness, ensuring computational performance and addressing ethical considerations are essential for turning GenAI aspirations into impactful solutions. Partnering with experienced integrators can help organizations accelerate their journey, unlocking innovation and achieving business objectives in a rapidly evolving technological landscape.

The year 2024 marked a pivotal point in AI’s transformative potential. With generative AI and large language models (LLMs) becoming more accessible,  industries and economies underwent significant transformation. A recent report by IDC highlights that by 2030, every dollar invested in AI solutions will generate $4.60 in global economic output. However, as we enter 2025,  the focus must shift to ensuring AI drives inclusive economic growth rather than exacerbating global disparities.

Historically, big tech has prioritized developed markets, building robust AI infrastructure and solutions that have established these nations as technological powerhouses. While these investments enhance AI capabilities in advanced economies, they risk sidelining emerging markets, restricting their ability to adopt AI-driven solutions, and widening the global digital divide.

Emerging markets often face barriers to AI adoption, including limited infrastructure and resources. While developed nations leverage cutting-edge AI technologies, many emerging economies struggle to access the necessary digital frameworks and expertise. This disparity inhibits their ability to harness AI for economic growth and innovation. Yet, these markets possess immense potential. With young, tech-savvy populations and fewer constraints from legacy systems, they have the agility to leapfrog traditional technological barriers.

For example, in India, where 65% of the population is under 35, AI-driven solutions are revolutionizing financial inclusion and education. Across Africa, AI-powered healthcare innovations are enhancing diagnostics and expanding medical access. These success stories underscore the potential of AI to drive transformative change in regions often overlooked by mainstream AI development.

The mobile phone revolution bypassed traditional infrastructure challenges and unlocked socio-economic progress in underserved regions. AI’s trajectory is similar, offering the promise of bridging digital divides, fostering innovation, and empowering communities worldwide. To fully realize AI’s potential in emerging economies, a strategic approach is required. This includes building AI-ready datacenters, nurturing local talent, and implementing policies that support inclusive AI development. Additionally, AI solutions must be tailored to diverse linguistic and cultural contexts to maximize accessibility and impact.

By prioritizing accessibility and inclusivity, AI can become a powerful tool for economic empowerment in 2025. When implemented thoughtfully, AI enhances productivity, accelerates development, and improves quality of life.

As we navigate this transformative era, emerging markets have a unique opportunity to redefine the global AI landscape. AI must serve as a universal enabler of progress — bridging disparities, fostering inclusion, and acting as a catalyst for equitable global growth.

In 2024, a deepfake scam cost a Hong Kong-based company $25 million when cybercriminals used AI-generated voices to impersonate executives in a fraudulent transfer request. This incident is just one example of the powerful ways that AI is transforming the cyberthreat landscape. While AI enhances cybersecurity defenses by automating threat detection and response, it also gives attackers unprecedented capabilities to launch more sophisticated and adaptive cyberattacks.

Organizations must recognize that traditional security models are no longer sufficient. Cybercriminals now use AI to automate phishing, break passwords, and create malware that can dynamically evade detection. To combat these evolving threats, organizations must transition from outdated security frameworks to more resilient and proactive approaches, such as zero trust and implementing AI-powered security strategies of their own.

The Rise of AI-Driven Cyberattacks

Cyberthreats are growing in speed, scale, and stealth as AI is increasingly weaponized by malicious actors. The U.K.’s National Cyber Security Centre (NCSC) has issued warnings that AI will lead to a surge in cyberattacks, particularly ransomware, which is becoming more intelligent and selective in its targeting. Industry experts predict that AI-powered attacks will reshape cybersecurity defenses, forcing organizations to rethink their security strategies. These threats are not hypothetical — attackers are already leveraging AI to automate reconnaissance, create deepfake scams, and bypass traditional security measures in real-time.

How AI Is Transforming Cyberthreats

AI is revolutionizing cyberattacks by automating, refining, and scaling malicious operations at unprecedented speeds. Unlike traditional hacking, which requires manual effort, AI allows cybercriminals to launch attacks with minimal human intervention, increasing their efficiency and success rates.

AI-Powered Reconnaissance and Automated Exploitation

AI has significantly enhanced reconnaissance techniques, enabling attackers to scan networks, identify vulnerabilities, and prioritize high-value targets automatically. Previously, attackers had to manually analyze network security, but AI-powered tools can now process vast amounts of data and uncover weaknesses more efficiently than ever before.

This advancement has led to the rise of automated penetration testing tools used for malicious purposes. These AI-driven programs simulate attacks, exploit vulnerabilities, and escalate privileges within a system — all without human intervention. They continuously learn from each attempt, improving their effectiveness over time.

Another major concern is AI-powered brute-force attacks. Traditionally, cracking passwords required substantial computing resources, but AI-enhanced password-cracking tools can predict passwords based on user habits, previous breaches, and behavioral patterns, making traditional authentication methods increasingly vulnerable.

AI-Powered Phishing and Social Engineering

One of the most alarming uses of AI in cybercrime is automated phishing. Traditional phishing attacks relied on generic emails that could often be detected by spam filters. AI-enhanced phishing, however, analyzes public data from social media, corporate websites, and data breaches to generate highly personalized messages. These emails can mimic a specific individual’s writing style and context, making them far more convincing and difficult to detect.

AI also plays a crucial role in voice phishing (vishing) and deepfake-based deception. As we saw with the example above, cybercriminals can use AI-generated voices and videos to impersonate executives, employees, or even family members. The increasing sophistication of deepfake technology makes these scams even harder to detect, posing a serious risk to businesses.

Beyond digital fraud, AI is being weaponized for disinformation campaigns and cyber-physical attacks. Malicious actors leverage AI-generated content to spread fake news, manipulate stock markets, and influence political events. AI can create realistic fake articles, deepfake videos, and synthetic social media accounts to distribute misinformation at an unprecedented scale.

Intelligent Malware and Adaptive Attacks

AI-driven malware is evolving at a rapid pace. Unlike traditional malware, which follows a fixed set of behaviors, AI-powered malware can modify its code and adapt to evade detection. Using machine learning, these programs analyze an organization’s defenses and adjust their attack strategy accordingly.

Polymorphic malware constantly alters its code to bypass antivirus software, while AI-enhanced ransomware selects high-value targets within a network before encrypting data, maximizing its impact. Additionally, self-learning malware can analyze system defenses and determine the most effective methods of infiltration. Some variants even mimic normal user behavior, making them harder to detect through behavior-based security tools.

Cyber-Physical Attacks: AI Threats Beyond Digital Systems

AI is not just a cyberthreat — it is also being used to target physical infrastructure. In critical sectors such as energy, transportation, and healthcare, attackers are disrupting industrial control systems (ICS), manipulating autonomous vehicles, and interfering with medical devices. These AI-driven cyber-physical attacks pose a serious risk to public safety and national security, requiring urgent defensive measures.

How Defenders Must Evolve

Traditional cybersecurity models focus heavily on detection and response, but given the sophistication of AI-driven threats, this is no longer enough. Organizations must shift toward prevention-first security strategies. A major flaw in conventional security is that implicit trust and unrestricted network access create exploitable vulnerabilities. Moreover, high network traffic often leads to an overwhelming number of false positives, making security operations inefficient.

To counter these challenges, cybersecurity leaders are turning to zero trust — a security model that operates on the principle that no entity, whether inside or outside the network, should be inherently trusted. This approach ensures continuous verification of users, devices, and network interactions.

Zero Trust: A New Era of Cyberdefense

Zero trust is a proactive security framework that minimizes attack surfaces, enforces strict access controls, and verifies all network interactions. One of its key principles is least privileged access, which ensures that users and devices only receive the minimum permissions necessary. This drastically reduces the risk of unauthorized access.

Another critical component is end-to-end encryption, ensuring that data remains secure even if intercepted. Authentication and authorization are continuously enforced, preventing attackers from exploiting stolen credentials. Additionally, identity-based access management moves away from perimeter-based security models, ensuring that only verified users can access critical systems.

To enhance defense-in-depth, zero trust includes distributed credential vaults, which eliminate single points of failure in identity management. Insecure protocols are terminated at the network edge, preventing attackers from exploiting outdated security measures. Meanwhile, segmentation restricts lateral movement within networks without relying on traditional firewall rules, significantly reducing the spread of threats.

A well-implemented zero trust architecture creates a resilient and distributed security model that significantly reduces the risk of AI-powered cyberthreats. Unlike traditional security frameworks that rely on centralized control, zero trust distributes security measures across multiple layers, eliminating single points of failure. This approach enables organizations to adapt to evolving threats, minimize attack surfaces, and maintain operational efficiency without compromising security.

Strengthening Cyber Resilience in an AI-Driven World

The rapid advancement of AI presents both challenges and opportunities in cybersecurity. While attackers continue to develop more sophisticated AI-driven threats, organizations can counteract these risks by adopting AI-powered security solutions, training employees on AI-driven scams, and enforcing zero trust principles.

As AI-driven cyberthreats grow more advanced, the future of cybersecurity lies in resilience, proactive defense, and the elimination of implicit trust. Organizations that embrace zero trust and AI-driven security solutions will be better positioned to fortify their defenses and stay ahead of evolving cyberthreats.

Generative AI (GenAI) is a topic of interest everywhere today — from homes and educational institutions to offices and executive boardrooms. GenAI, of course, refers to algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos based on the data they have been trained on.

Its applications are being tested and adopted across various industries, from healthcare to finance to retail. However, as with any groundbreaking technology, the response to generative AI has understandably been mixed.

While there’s enthusiasm for its potential benefits, like improving human efficiencies and creating personalized experiences, the C-suite is also exercising caution as they consider its implications on safety, ethics, and trust. Before diving in, business leaders must understand the potential impacts of generative AI on productivity and the future workforce.

In my own conversations with CIOs, a theme is clearly emerging — there is a gap between their GenAI ambitions and their readiness for it. Here are a few things to help business leaders build a strategic road map for responsible, scalable GenAI adoption.

A Strong Data Foundation

The success of any GenAI application hinges on the quality of the data feeding it. The old adage of “garbage in, garbage out” is more relevant than ever with generative AI.

If your data is flawed, incomplete, or siloed, you’re likely to end up with misleading — or worse, harmful — AI-generated outputs. That’s why building a strong data foundation is critical.

For scaling AI responsibly, organizations need their data foundation to include a strong and agile data strategy. This means having the flexibility to adapt to new data sources and changing business needs without missing a beat. It’s also vital to ensure compliance with regulations like GDPR, especially as privacy and data protection are a constant concern for companies of all sizes and in all verticals worldwide. Maintaining customer trust is paramount, and the last thing any organization wants is to compromise on data security.

A rigorous data cleaning and validation process is another cornerstone of a strong data foundation. By ensuring high-quality inputs, you set the stage for your AI to deliver valuable and actionable insights.

Additionally, integrating data across the organization helps avoid silos, ensuring comprehensive data coverage. This is where master data management comes into play — it helps achieve a unified view of your data, making it easier to manage and deploy across various AI applications.

Identify the Right Use Cases

Generative AI’s potential spans a wide range of functions — from customer service and marketing to software development. However, not every use case will justify the investment. It is crucial to prioritize those that align with your business goals and offer clear, measurable benefits.

My colleagues and I have seen a busy international airport use generative AI to help staff centralize customer feedback from multiple platforms, do sentiment analysis on the feedback and generate personalized responses.

We’ve seen a bank use GenAI to help developers automate routine code generation, so they could focus on delivering more business value.

We’ve also seen marketers use GenAI to create personalized campaigns that truly resonate with individual customers, tailoring messages to their unique preferences and behaviors.

By adopting a collaborative approach across business functions, your organization can identify the most impactful GenAI use case for your business to help you receive a return on your investment.

Leverage LLMOps

As you begin to operationalize generative AI, it is essential to incorporate large language model operations (LLMOps) — a framework of practices, techniques, and tools used for managing large language models effectively and securely. This helps ensure your AI deployments are reliable, scalable, and cost effective.

LLMOps is not meant to be a standalone framework — it needs to be part of your broader data and AI strategy to ensure seamless integration and optimal performance. Some best practices in LLMOps include implementing stringent data security measures to protect the integrity of your information. Regular updates and maintenance of AI models are also vital to ensure they remain relevant and effective over time.

Generative AI is an emerging technology, and integrating it into your operations requires a strategic, considered approach. This isn’t a race, but a journey.With a solid foundation, a proactive strategy, cost optimization, and the right use cases, enabled by a trusted partner, this journey can be a successful one.

The rapid evolution of artificial intelligence is pushing the boundaries of digital transformation, and one of the most significant shifts today is the rise of agentic AI. Unlike traditional AI models, which act as passive responders, agentic AI represents a new paradigm where AI agents operate autonomously, make decisions, and execute complex workflows.

At this year’s IDC Middle East CIO Summit, we will explore the fundamental shift that agentic AI is bringing to digital solutions, marketing, and customer experience. Our session will not just explain the theory behind agentic AI, but also explore the transformation of digital solutions and the subsequent impact on SaaS, customer experience, and marketing. We will also discuss the differences between agentic and agentive, explain how business can implement agentic AI today, and assess the overall implications for customer experience and marketing.

Agentic AI is more than just automation — it represents a fundamental shift in how businesses operate and interact with technology.

From customer experience to marketing and enterprise workflows, agentic AI will redefine digital experiences by enabling intelligent decision-making agents that work in harmony with human users. While SaaS will not disappear overnight, businesses that embrace solutions driven by agentic AI will gain a competitive advantage in efficiency, personalization, and automation.

The shift to cloud computing has brought unprecedented opportunities for businesses while presenting significant security challenges. Misconfigurations, insider threats, and cross-domain attacks dominate cloud security discussions as attackers find increasingly sophisticated ways to exploit vulnerabilities.

Cloud misconfigurations represent some of the most critical vulnerabilities. These security gaps — including exposed databases, unrestricted outbound access, excessive permissions, and improperly secured critical services like SSH or RDP — create opportunities for attackers to move laterally, escalate privileges, and steal data. Multicloud environments pose additional risks, as misconfigured systems expand the attack surface and heighten the likelihood of breaches.

The Evolution of Cloud-Based Threats

Attackers are using increasingly sophisticated methods, leveraging cloud environments to host command-and-control (C2) servers, phishing kits, and ransomware operations. A major concern is identity exploitation, where attackers target cloud identity providers to gain unauthorized access and increase their privileges.

Cross-domain attacks have also become more prevalent. These attacks typically begin with a breach in an on-premises or endpoint system, after which attackers move to cloud workloads. From there, they steal credentials and data, sometimes returning to the original system to deploy ransomware. This movement between different domains makes defense particularly challenging.

“One of the biggest shifts we’re seeing is the use of cloud environments as both an attack vector and a staging ground for further compromises. Attackers are no longer just targeting cloud infrastructure; they’re exploiting identities, misconfigurations, and interconnected systems to move across environments undetected. The key to defense is continuous visibility and a modern proactive security posture.” — Aaron Hambleton, Director for Middle East and Africa, SecurityHQ

Strategies to Strengthen Cloud Security

Organizations need a proactive, multi-layered approach to cloud security. Here are the essential solutions:

• Cloud Security Posture Management (CSPM): Continuously monitor and address misconfigurations to minimize vulnerabilities and maintain compliance.

• Cloud Infrastructure Entitlement Management (CIEM): Secure cloud identities by restricting excessive permissions and enforcing least-privilege access.

• Infrastructure-as-Code (IaC) Security: Shift security left by embedding best practices into development workflows to reduce risks before deployment.

• Cloud Workload Protection (CWP): Analyze and secure cloud workloads to protect against emerging threats.

Adapting to a Changing Landscape

The rapid evolution of cloud threats requires organizations to rethink their security strategies. Building resilience requires a three-pronged approach: adopting advanced tools, implementing continuous monitoring, and addressing vulnerabilities proactively.

As the cloud continues to transform how organizations operate, the ability to anticipate and respond to emerging threats will be crucial. SecurityHQ provides the expertise and solutions needed to secure changing cloud environments and prepare for the future of cybersecurity.

The UAE has surged ahead in AI-driven governance by blending strategic vision with persuasive competition among its public institutions. Pioneering moves — like appointing the world’s first AI Minister (2017) and launching the UAE AI Strategy 2031 — set the stage, but the true catalyst has been fostering rivalry through initiatives such as the Mohammed Bin Rashid Government Excellence Award (MBRGEA). This award incentivizes entities to compete in innovation, efficiency, and digital transformation, turning AI adoption into a race for excellence.

Departments vie for recognition by deploying cutting-edge solutions, inspiring others to upscale. The MBRGEA’s criteria — like “Future Readiness” — push entities to experiment, creating a cycle of improvement. When one agency pioneers a chatbot, others enhance it, accelerating public sector evolution.

Competition is balanced with collaboration. The Mohamed bin Zayed University of AI (MBZUAI) and partnerships with tech giants nurture talent and R&D, while the MBRGEA’s emphasis on public-private projects drives co-created solutions. This ecosystem thrives on peer pressure and pride — no entity wants to lag when rivals gain acclaim.

Why does it work? The UAE taps into institutional ambition to motivate risk-taking and citizen-centric innovation. This model proves that recognition can be as powerful as funding. For nations seeking AI leadership, the lesson is clear: merge strategy with incentives, and let healthy competition fuel progress. The UAE’s success lies not just in dreaming big, but in making every entity race to turn those dreams into reality.

Within the last few decades, the world has undoubtedly realized the huge benefits of embracing the mindset of digital first, automation first, mobile first, cloud first, and so on — both within business and general life. Today, the world is at another inflection point, with business needing to embrace an AI-first mindset to enable sustainable growth. And, as always, what applies in business will ultimately also apply in general life.

As a member of the AI for Developing Countries Forum (AIFOD) and a certified professional in using neuroscience in business (Brain Computer Interface), I can share my experiences from the recent Geneva Summit 2025 held in January at the United Nations Office.

One of the highlights of the agenda was a discussion on the topic “Digital Sovereignty and Governance in the Age of AI.” The critical action points identified the creation of a dynamic framework to:

• Balance regulation and innovation.
• Protect data rights and privacy that respect national sovereignty while enabling cross-border collaboration.
• Ensure all nations have an equitable voice in global AI governance, supported by effective public-private partnerships.
• Prevent data colonialism while promoting responsible innovation and collaboration.
• Ensure democratic and diverse global AI governance mechanisms that reflect the interests and concerns of all countries.
• Measure and benchmark the AI maturity of a nation.

In October 2017, the UAE Government launched its ‘UAE Strategy for Artificial Intelligence,’ which aims to achieve the objectives of UAE Centennial 2071, boost government performance at all levels, use an integrated smart digital system that can overcome challenges and provide quick efficient solutions, make the UAE the first in the field of AI investments in various sectors, and create a new vital market with high economic value.

At Convene, we have already embraced an AI-first mindset in our product road map and business operations to provide a best-in-class paperless board and committee meeting experience. Convene’s other products (e.g., ESG reporting software) also use AI technologies to generate highly compliant, informative, and engaging ESG reports.

We are yet to see the best from AI; and we are standing ready to welcome it.

AI will define the next era of computing and is an important part of the digital and economic growth strategies of nations across the Middle East, Turkiye, and Africa (META). At the recent IDC Directions META 2025 event in Dubai, IDC revealed that it expects annual AI spending in the region to reach $14.6 billion by 2028.

The UAE and Saudi Arabia lead the way, going beyond investment of capital, and viewing AI as a holistic part of a broader cultural transformation, which places cutting-edge technology at the heart of government, the economy, and industry. This is true of Saudi Arabia’s Vision 2030 and the UAE National Strategy for Artificial Intelligence 2031.

These government-led initiatives provide strategic direction and drive public-private industry collaboration, which is vital given the almost infinite possibilities enabled by AI and the demand for skills, training, and education it will drive.

While there is a sense of urgency around the entire AI conversation, this really is just the beginning. We will only realize the full potential of AI when the technology is pervasive and spans from the cloud to the edge to endpoints.

This means that AI is being used to optimize data processing in the cloud; adding intelligence to the networks which connect devices; and increasingly improving performance and capabilities of the devices we use every day.

IDC’s tech predictions for 2025–2029 refer to AI’s huge potential to transform businesses and create new markets. The company predicts a pivot away from what it describes as the “GenAI scramble” as the industry regains its poise and refocuses on the bigger picture.

Part of this landscape is the world of opportunity beyond AI. Whether it’s datacenter, cloud, or high-performance computing, businesses are investing in technologies that make them more intelligent, data driven, and efficient.

Furthermore, “efficiency” means different things to different people. Business leaders looking to build high-performance teams are using AI, data, and analytics to help their employees become more productive. Whereas CIOs are focused on maximizing the performance of their digital infrastructure — while reducing power consumption, cost, and physical server footprint.

So, while AI will define the next era of computing, it must form part of a diverse and cohesive digital strategy, which places as much emphasis on cultural change as on capital investment on technology infrastructure.

As artificial intelligence is at the forefront of innovation worldwide, organizations in the Middle East are rapidly adopting AI-driven solutions to enhance efficiency, security, and precise decision-making. However, AI workloads require robust, high-performance networking infrastructure to support their scale and complexity. Enterprises across oil and gas, banking, and the public sector must ensure their networks are AI ready to harness the full potential of AI-driven innovation.

AI and Network Evolution: A Symbiotic Relationship

AI workloads generate and process massive amounts of data in real time, requiring ultra-low latency, high-bandwidth connectivity, and scalable network environment. Traditional networks often struggle to meet these demands, making AI-ready networking a critical investment for organizations seeking to optimize their AI-driven applications.

LTIMindtree’s purpose-fit transformation offerings and holistic approach provide intelligent, secure, and high-performance networking solutions designed to support AI workloads across diverse industry verticals.

Oil & Gas: Enhancing Operational Efficiency with AI

The Middle East’s oil and gas sector is leveraging AI to optimize exploration, production, and predictive maintenance. AI-driven seismic data analysis, remote monitoring of drilling rigs, and predictive analytics for equipment failures require real-time data transmission from edge locations to central datacenters or the cloud.

AI-powered networking solutions ensure seamless, secure, and high-speed connectivity between offshore rigs, refineries, and control centers. HPE Aruba EdgeConnect SD-WAN, for instance, enables oil and gas companies to connect remote sites with high resilience, ensuring minimal latency for AI applications that depend on real-time insights.

Banking & Financial Services: AI-Powered Fraud Detection and Customer Experience

The banking sector in the Middle East is witnessing a surge in AI-driven use cases, from fraud detection and risk management to hyper-personalized customer experiences. AI models require secure, high-speed networks to analyze real-time transactions and customer interactions while maintaining regulatory compliance.

Our Zero Trust Security framework ensures that sensitive financial data is protected across distributed environments. Aruba’s cloud-native network management platform leverages AI-driven automation to enhance operational efficiency, ensuring optimal application performance for AI workloads in banking.

Public Sector: Smart Cities and AI-Driven Governance

Governments across the Middle East are embracing AI to enhance citizen services, public safety, and Smart City initiatives. AI-powered surveillance, traffic management, and predictive analytics for urban planning require a highly resilient, scalable, and secure network infrastructure.

Together with HPE Aruba, we can integrate AI-based automation, security, and orchestration to deliver a unified, intelligent network for public sector applications. With AI-driven insights, government agencies can optimize resource allocation, improve cybersecurity, and ensure seamless connectivity for mission-critical operations.

The Future of AI-Ready Networking in the Middle East

As AI adoption accelerates across industries, the next generation of networking solutions are also evolving to meet growing AI demands. By investing in AI-optimized networks that delivers low latency, high bandwidth, and advanced security capabilities, organizations can unlock AI’s full potential, driving innovation and achieving new levels of efficiency and productivity.

At LTIMindtree, we are committed to empowering enterprises with cutting-edge networking solutions that enable AI-driven transformation. By building intelligent, secure, and high-performance networks, businesses in the Middle East can stay ahead in the AI era and drive innovation across their industries.

The global race for AI dominance isn’t just about computational models — it’s about building the foundational infrastructure that enables nations and enterprises to harness AI’s true potential. As organizations witness the unprecedented pace of AI evolution, the critical challenge lies in creating robust, scalable infrastructures that can support AI transformation while respecting national sovereignty.

Leading enterprises recognize that successful AI implementation hinges on establishing the right infrastructure foundation. Organizations that will thrive in this new era are those that can balance rapid innovation with robust data governance, ensuring their AI journey is both ambitious and responsible.

Industries at the forefront of AI transformation understand that success depends on adopting scalable, secure, and versatile infrastructure that supports long-term innovation. As businesses accelerate toward a future powered by AI, the focus must be on leveraging global infrastructure that can scale with the growing demands of industry transformation.

This foundation enables rapid solution deployment, market adaptability, and unprecedented innovation speeds — empowering enterprises worldwide to confidently navigate their AI journey and reshape the possibilities within their industries.