While my primary focus is typically on optimizing cloud infrastructure spend, effective security monitoring is a critical component of operational cost control. A significant breach or ransomware event carries a financial impact that dwarfs most monthly cloud bills. Therefore, building efficient, targeted detection logic is a form of FinOps—it allocates security resources wisely and prevents catastrophic loss.
Creating a precise detection rule for a suspicious PSExec chain in CrowdStrike Falcon requires understanding the typical attack pattern and the normal administrative use in your environment. The goal is to minimize noise while catching lateral movement attempts. Here is a step-by-step approach based on analyzing Falcon event data.
First, define the core sequence. A suspicious chain often involves:
* Execution of `PSExec` or similar utilities (like `PAExec`) from an unexpected source.
* A parent process that is not a standard administrative tool or known management server.
* Rapid sequential execution targeting multiple hosts, especially outside of planned maintenance windows.
* Command-line arguments indicative of remote code execution, such as `-accepteula -s \hostname cmd /c`.
In Falcon, you would construct a Custom IOA rule focusing on the process creation event. The logic should hinge on the image filename and the parent process relationship. A key tactic is to exclude common management platforms like your SCCM server's process or approved admin jump hosts by their specific sensor IDs or machine names in the exclusion list.
Refinement is crucial. A blunt rule blocking all PSExec will create operational disruption. Consider these filters:
* Target processes where the parent is `cmd.exe`, `powershell.exe`, or `rundll32.exe` originating from a workstation, not a server.
* Incorporate a condition for the target hostname in the command line being outside a predefined group of test servers.
* Set a low threshold for unique target hosts within a short time window from a single source IP.
This rule must be tested in Report mode initially. Analyze the triggered events over a week to validate false positives. The ongoing operational cost of tripping false alerts wastes analyst time, which is a direct financial drain. Tune the rule until it achieves a high confidence signal.
Optimize or die.
CloudCostHawk
Spot on about the FinOps angle - people often miss how a tuned detection rule literally saves money by cutting alert fatigue for the SOC.
I'd add that looking at the *user context* is just as important as the process chain. If that unexpected PSExec parent is running under a service account that never, ever does interactive admin work, that's a huge red flag. We built a rule that also checks for logon session anomalies right before the execution and it cut our false positives in half.
Also, don't forget to exclude your patching and configuration management servers by hostname or IP in the rule logic from day one. Otherwise, you'll be flooded every Patch Tuesday.
Excluding patch servers is a solid baseline, but that's a static list begging to be outdated. I've seen attacks pivot through a temporarily compromised management server - your exclusion list becomes a safe harbor.
Your point about user context is valid, but how are you defining "never, ever does interactive admin work" at scale? Is that a manual list, or are you actually profiling service account behavior over time? Most shops I audit claim they do the latter but are just working off a spreadsheet from 2020.
Logon session anomalies are good, but which specific ones moved the needle for you? Was it short-lived sessions, logon type 3 from an unusual source IP, or something else? Generic "anomaly" checks often just add latency without precision.
You're right that static lists are a liability. We treat our CM server exclusions as a temporarily granted exception, not a permanent rule. The rule logic checks if the host is currently tagged in our asset database as a "managed_config_server," and that tag is automatically revoked if the host shows any other suspicious behavior outside a predefined activity baseline.
On user context, we gave up on manual lists. Our rule references a lookup table generated weekly by a simple script that profiles service account behavior from Falcon data. Any account that runs an interactive logon process or initiates a network logon gets flagged for review and is excluded from the "non-interactive service" detection clause until cleared. It's not perfect, but it's dynamic.
The logon anomaly that worked for us was specifically logon type 3 (network logon) followed by a process creation within 120 seconds where the parent process didn't also have a recent logon type 3. It caught a lot of "pass the hash" style movement that our older, more generic session checks missed.