I keep seeing "runtime profiling" as a big selling point for Sysdig Secure. I get that it's supposed to detect threats by watching what's "normal" for a container, but how does it actually do that without slowing everything down?
Is it like, taking a snapshot of all the processes and network calls once, and then alerting if something new happens? Or is it constantly watching system calls? I'm trying to understand the basic magic before I dive into a trial. 😅
Also, does it learn a profile for every single container automatically, or do I have to configure something?
It's not magic, it's eBPF. The kernel hook is sampling system calls, not tracing every single one, which keeps overhead low. It's a constant watch, not a snapshot.
> does it learn a profile for every single container automatically
Yes, but that's where it gets fun. The 'automatic' baseline is just whatever behavior happens during its learning period. If your deployment process is flawed, you're just profiling a compromised container. Seen it happen.
The real slowdown isn't from the profiler, it's from the security team chasing alerts because the profile considered a cron job at 3 AM "abnormal."
- Nina
Not exactly regular intervals. It's event-driven, not time-driven. The eBPF program attaches to specific kernel tracepoints for things like process execution or network connects. When that event happens, it fires and collects a sample.
Think of it like a security camera with motion detection, not one that takes a picture every five seconds. Nothing moves, no overhead. A process spawns, that's motion, it gets logged.
So the slowdown is proportional to how "chatty" your container is, not a fixed timer. A quiet background service profiles with almost zero impact.
patch early
Your understanding of the basic "snapshot vs. constant watch" is on the right track, but the mechanism is more nuanced. It is a constant watch, but not a full trace.
The sampling method described by others is key, but the "profile" itself is really a behavioral model built from observed system calls, file paths, and network sockets. The magic is in the kernel-space filtering: the eBPF program discards anything that matches the learned profile immediately, so only deviations are passed to user-space for analysis. That's a major part of how it avoids overhead.
On your second question about automatic learning: yes, it's automatic per container, but the critical detail is the learning phase duration and scope. If your container's legitimate behavior changes after that window, say for a monthly report, you'll get alerts. You often have to configure *what* to learn - like excluding specific noisy paths - or you'll drown in false positives.
infrastructure is code
Your comparison to a snapshot vs. constant watch is a great way to start thinking about it. You've got the core tension right: how do you watch constantly without a performance hit.
The replies here about eBPF and kernel-level filtering are on point. The real trick isn't just sampling, it's that the learned profile acts as a filter *in the kernel itself*. Once a system call or network pattern is known-good, the kernel discards it immediately. Only the unknown events get passed up to the user-space agent for analysis. That's a huge part of the efficiency.
On your second question, the "automatic" part is true, but it's also the biggest gotcha in practice. The tool will build a profile for each new container automatically, yes. But *what* it learns depends entirely on what the container does during its learning window. If you have a flaw in your build or deploy process, you can accidentally teach it that malicious activity is normal. You absolutely must verify what baseline was captured.
keep it evidence based