By: Mohammed Al-Moneer, Sr. Regional Director, Türkiye, France, Africa & Middle East at Infoblox
Artificial intelligence is rapidly reshaping how organizations across the Middle East approach IT operations and cybersecurity. From predictive analytics to automated remediation, AI is widely viewed as the engine that could reduce operational complexity and move enterprises closer to a “zero-touch IT” model. Yet many AI initiatives struggle to scale or produce tangible outcomes. The underlying issue is rarely the technology itself, but rather a lack of accurate and comprehensive visibility into digital assets.
AI depends on reliable data. When that data is incomplete, outdated, or inconsistent, automation becomes unreliable and risk increases rather than decreases. In modern hybrid environments that span on-premises infrastructure, multiple cloud platforms, SaaS applications, and remote endpoints, maintaining an accurate view of assets and their connections has become one of the most significant challenges for IT and security leaders.
Asset visibility today goes far beyond maintaining an inventory of servers and devices. It now includes applications, workloads, virtual machines, containers, and the relationships between them. Each asset represents both operational value and potential exposure. Without clear visibility, organizations cannot confidently automate operations, enforce security controls, or respond effectively to incidents. In the age of AI, visibility forms the foundation of everything else.
One of the most common obstacles to achieving this foundation is the Configuration Management Database (CMDB). In theory, the CMDB should serve as a single source of truth for IT assets and their dependencies. In practice, many CMDBs are significantly inaccurate, often reflecting only a portion of what is actually deployed in the environment. When accuracy drops to 20, 40, or even 70 percent, trust in the data erodes and teams revert to manual checks and workarounds.
This issue is widespread across industries and regions. Organizations increasingly recognize that unreliable CMDB data weakens both operational efficiency and security posture. More importantly, it limits what AI can realistically achieve. If AI systems rely on poor-quality data, their outputs — whether automated actions or security decisions — will inevitably be flawed. For this reason, improving asset data accuracy is not optional; it is the first and most critical step in any successful AI initiative.
Traditionally, organizations have attempted to address this challenge through manual processes such as periodic audits, manual updates, and governance policies. While well intentioned, these methods cannot keep pace with the speed and scale of modern IT environments. Assets are continuously created, modified, and retired, often without direct human oversight. Expecting teams to track these changes manually is no longer practical.
What is needed instead is an automated, AI-driven approach to asset discovery and validation. By continuously observing the network — where every device, application, and workload must communicate — organizations can build a dynamic, real-time understanding of their environments. This approach reduces dependence on manual input and provides a far more accurate representation of infrastructure. With trustworthy asset data in place, AI can begin delivering meaningful automation across infrastructure and applications.
However, asset visibility alone is not sufficient to address today’s expanding attack surface. Modern threats exploit relationships between assets, identities, and data. A compromised credential, for example, can provide access to multiple systems and sensitive information within minutes. Effective defense requires the ability to correlate asset intelligence with identity context and other critical data sources.
When asset data, user identity information, and behavioral signals are integrated, security teams gain a clearer understanding of what normal activity looks like and what represents potential risk. This cross-domain visibility enables AI-driven systems to prioritize threats more accurately and automate containment measures before incidents escalate into major breaches.
The urgency of this integrated approach is increasing as attackers themselves adopt AI and automation. Cybercriminals now use advanced tools to rapidly scan for exposed assets, identify vulnerabilities, and move laterally across networks at machine speed. Defending against these threats using manual processes is no longer viable. Organizations must counter automation with automation, supported by accurate and correlated data.
For enterprises across the Middle East pursuing ambitious digital transformation strategies, this reality presents both a challenge and an opportunity. AI has the potential to become a powerful operational and security enabler, but only if it is built on a foundation of visibility and data integrity. Investments in accurate asset intelligence today will deliver long-term benefits across operations, security, and resilience.
At Infoblox, asset visibility is viewed as a strategic cornerstone for AI-driven IT and security. By establishing a continuously updated understanding of assets at the network level and aligning that intelligence with identity and security workflows, organizations can reduce manual effort, shrink their attack surface, and move closer to achieving zero-touch IT.
In an era defined by AI, success will not depend on who deploys the most tools, but on who builds the most reliable foundations. Comprehensive asset visibility and effective attack surface management are no longer optional. They are essential to enabling AI to operate securely, reliably, and at scale.
