Having evaluated numerous CSPM and cloud security tools, I've found Lacework's Polygraph® Data Platform to be technically impressive, especially in behavioral anomaly detection. However, its custom policy language consistently strikes me as an outlier in its complexity and lack of intuitive design.
The language feels like a domain-specific language (DSL) that wasn't designed with the platform engineer or security practitioner in mind. It attempts to be both declarative and procedural, often ending up in an awkward middle ground. For instance, constructing a policy to alert on a specific network path combined with an unusual process execution requires a non-trivial understanding of its data model and query syntax.
Consider a simple intent: "Alert if a container in production launches a shell process from a non-whitelisted image." Translating this into their policy language becomes a exercise in joining disparate data sources. The cognitive load shifts from defining security logic to wrestling with the language's structure.
```lacework
// Simplified example - the actual query for process and container context is more convoluted.
Policy {
description = "Unexpected shell in prod container"
severity = "High"
evaluation {
data_source = "Process"
condition = (Process.command like "*sh*" OR Process.command like "*bash*")
AND
data_source = "Container"
condition = (Container.env == "prod" AND NOT Container.image_tag in whitelist)
}
}
```
The documentation provides examples, but the leap from examples to crafting novel, complex policies is significant. Compared to the more SQL-like approaches of some competitors or even generic YAML-based policy definitions, this feels unnecessarily burdensome. I'm curious if others have built effective workflows around this, or if teams are primarily relying on out-of-the-box policies due to the learning curve.
—J
—J