Cloud security isn’t just about merely collecting data. You need to extract meaning from it if you want to actually improve your defenses. And while this may sound simple in practice, any seasoned cloud security practitioner will be quick to tell you otherwise.
The challenge today isn’t about what you’re not seeing. Your traffic logs probably capture terabytes of data, but when analyzing them, you could easily miss or find that the most critical security insights are obscured. These are the small, subtle anomalies that could make the difference between detecting an attack and becoming another breach statistic.
In other words, it’s not enough to just watch the data flow by. You actually need to understand what it’s trying to tell you.
The Invisible Threats
If you’re involved in cloud security, you probably review your traffic logs each day. You’ve set up alerts for unusual patterns, but the uncomfortable truth is that what you’re seeing here is likely only the tip of the iceberg. Truly effective cloud network security is about protecting your data as it moves between all of your services, containers, and workloads. To do this well, it takes looking beyond the obvious metrics to catch what matters.
Traffic log data provides a wide range of useful metrics to monitor, such as successful connections, completed requests and standard metadata. But what it doesn’t show are the subtle reconnaissance activities that occur before a real attack.
Just think, an attacker might spend weeks or even months making just one request per hour to different endpoints, mapping out your infrastructure while staying completely under your radar. Each request looks innocent on its own.
When you review your logs, how well are you connecting the dots between different services? Do you think you would notice when someone fails to access your storage but then successfully gets into a compute instance from the same IP address? Without seeing these connections, you’re missing the storyline and only catching disconnected scenes.
Beyond Volume Metrics
Most of us naturally focus our attention on traffic spikes as our main red flag. These are the sudden increases in trigger alerts, but clever attackers know this game. If they are experienced enough, they will carefully keep their activities within your normal traffic patterns, often mimicking legitimate user behavior.
What’s more important than how much traffic you’re seeing is what that traffic is trying to do. A single attempt to move laterally between network segments might be far more significant than thousands of requests to your public API.
Your monitoring needs to evaluate not just whether the traffic looks normal in volume but also whether it makes sense in context: Should this identity be accessing that database? Has this service ever talked to that endpoint before?
Try enriching your traffic analysis with threat intelligence. When you recognize that a particular sequence of API calls matches a known attack technique, even minimal traffic to certain endpoints becomes immediately suspicious rather than background noise.
The Time Dimension
Most traffic analysis works in a specific snapshot in time (hours or days of data in one specific window). While this may help to categorize data, it introduces fragmentation, which blinds you to attacks that play out over weeks or months, which is exactly how sophisticated threats operate.
Cloud security needs to look at traffic across various time scales, since some patterns only become visible when you zoom out. An IP address connecting once weekly for months, always at 3 AM on Sundays, should raise more flags than one that generates high volume for a single afternoon. Many security teams miss this because they’re constantly resetting their analysis windows.
The Missing Context
Without getting more context from other systems, relying on traffic log data alone can lead to rabbit holes, or worse, it could mean you miss crucial plot points.
For example, imagine there’s a surge in API requests to your payment system that might look alarming in isolation. But what if you just launched a flash sale or deployed a new feature? Suddenly, it makes perfect sense. On the other hand, completely normal-looking traffic patterns might hide something malicious when you don’t consider who’s behind them. Why is this service account suddenly doing things it never did before?
Try connecting your traffic analysis with your identity systems, cloud audit logs, endpoint protection, and threat feeds. Knowing that a server making database queries was just created by a user who recently gained new permissions gives you the context to spot potential data theft that raw traffic logs would never reveal.
The Human Element
Following on from the last point, traffic logs show machine conversations, but they don’t reveal the human intentions behind them. Consider how a legitimate employee accessing sensitive data creates the same log entries as an attacker using stolen credentials.
These are two completely different events, yet the only difference is in the behavior patterns that raw logs don’t capture. How quickly is someone moving between resources? Are they accessing systems in a logical order for their job? Does their activity match how they normally work, or are they suddenly downloading gigabytes of data from storage buckets they rarely touch?
It is the crucial context and nuance that paints the picture needed to spot a real threat. Behavior analysis helps bridge this gap. If you can understand what normal looks like for each user and system, you can spot deviations that suggest compromise. This is true even when the individual log entries look completely normal.
Final Word
The goal isn’t to go out there and collect even more data. The real secret to cloud security is making the most out of the data that you already have at your disposal. Start by adding that all-important context to your traffic logs with information you have from your identity systems and security tools. The more you can connect your cloud services and your data (ideally through a unified platform), the easier it will be to spot patterns across time, identity, behavior,
