Wanting to maintain a traditional traffic monitoring model, based on packet analysis, anomaly detection, and border inspection, is wasting precious time for IT teams. This happens because advanced techniques are increasingly being developed to evade detection by classical systems, using loopholes that remain invisible to security tools based solely on network traffic.
Indeed, 72% of respondents in a global survey by the World Economic Forum 2025, reported an increase in organizational cyber risks, reflecting how threats evolve to hide from traditional defenses. Additionally, fileless attacks have 10 times more chance of success than traditional file-based malware attacks.
Cybercriminals no longer act by trial and error. Today, they act precisely and without leaving traces. They heavily use fileless attacks, exploit legitimate system tools like PowerShell and WMI to execute malicious commands without raising suspicion, and move laterally through the network silently, as if they already belonged to the environment.
This type of offense is intentionally designed to appear legitimate; the traffic does not raise suspicions, the tools are not unknown, and the events do not follow common threat patterns. In this scenario, according to the World Economic Forum 2025 report, 66% of organizations believe that artificial intelligence will have the most significant impact on cybersecurity, both for defense and attacks, reflecting a paradigm shift.
Traditional solutions, such as firewalls, IDS, and simple correlation systems, no longer provide the necessary protection, especially since 47% of organizations cite adversarial advancements fueled by generative AI as their primary concern. Additionally, 54% of large organizations point to supply chain vulnerabilities as the biggest barrier to cyber resilience, increasing the complexity of the challenge.
The role of granular visibility
Given this scenario, granular visibility emerges as a fundamental requirement for an effective cybersecurity strategy. It is the ability to observe, in detail, the behavior of endpoints, users, processes, internal flows, and activities between systems, in a contextualized and continuous manner.
This approach demands the use of more advanced technologies, such as EDR (Endpoint Detection and Response), XDR (Extended Detection and Response), and NDR (Network Detection and Response). These tools collect telemetry across multiple layers, from the network to the endpoint, and apply behavioral analysis, artificial intelligence, and event correlation to detect threats that would go unnoticed in environments monitored only by traffic volume.
Techniques that exploit invisibility
Among the most common tactics used in invisible attacks, the following stand out:
- DNS tunneling, encapsulating data in seemingly normal DNS queries;
- Digital steganography, hiding malicious commands in image, audio, or video files;
- Encrypted command and control (C2) channels, secure communication between malware and its controllers, making interception difficult;
- These techniques not only bypass traditional systems but also exploit flaws in the correlation between security layers. The traffic may appear clean, but the real activity is hidden behind legitimate operations or encrypted patterns.
Intelligent and contextual monitoring
To deal with this type of threat, it is essential that analysis goes beyond indicators of compromise (IoCs) and starts considering indicators of behavior (IoBs). This means monitoring not only “what” was accessed or transmitted but “how,” “when,” “by whom,” and “in what context” a particular action occurred.
Additionally, integrating different data sources, such as authentication logs, command executions, lateral movements, and API calls, allows for detecting subtle deviations and responding to incidents more quickly and accurately.
What all this means
The increasing sophistication of cyberattacks demands an urgent reassessment of digital defense practices. Traffic monitoring is still necessary but can no longer be the sole pillar of protection. Granular visibility, with continuous, contextual, and correlated analysis, becomes essential to detect and mitigate invisible threats.
Investing in advanced detection technology and strategies that consider the actual behavior of systems is now the only effective way to face adversaries who know how to hide in plain sight.
