Hey everyone! I've been tasked with researching our cloud security posture management (CSPM) and SIEM options, and Panther keeps coming up. I like what I've seen about their Python-native detections and the data lake aspect really clicks with my interests.
However, my lead specifically asked me to look at alternatives that are **not** Wiz or CrowdStrike. We've already evaluated those two, and they're not the right fit for our team's workflow. The problem is, every other article or comparison I find seems to only talk about those three!
Could you help me fill in the blanks? I'm looking for platforms that, like Panther, have strong:
- Detections-as-code (or at least very flexible rule creation)
- Good data lake integration or storage options
- A focus on cloud environments
I've seen names like Lacework, Orca Security, and Datadog's security offerings float around, but it's hard to gauge how their engineering experience compares. Is the developer experience as central for them? Also, how do they handle orchestration and CI/CD for detection rules? That's a big selling point of Panther for us.
Any hands-on experience or even just architectural insights would be super helpful. I'm still getting my head around the whole data pipeline side of security tools!
-- rookie
rookie
I've spent a good chunk of time with both Lacework and Orca, and I can tell you the developer experience gap is real. Panther's Python-native rule engine and Git-based CI/CD pipeline are the gold standard here.
Lacework's polygraph technology is impressive for anomaly detection, but their rule authoring is more of a YAML-based DSL with a limited set of operators. You can't really do arbitrary Python logic in detections. Their CI/CD integration exists but feels bolted on - you push a YAML file and hope it validates. Orca is even worse on that front: agentless scanning is great for inventory, but their custom rules are basically JSON filters with a few conditionals. You're not going to get the same expressiveness.
Datadog's security suite (Cloud SIEM + CSPM) is actually pretty good if you're already in the Datadog ecosystem. Their detection rules are editable via UI or API, and they support case templates and automated workflows. But the "as-code" part is weak - you can export rules as JSON, but there's no native Git-driven pipeline. You'd have to build your own CI/CD around their API. Their data lake is just their standard log pipeline, which is fine, but you don't get the same control over storage costs or retention as Panther's Snowflake/Athena backend.
If detections-as-code and CI/CD are your top priority, I'd actually look at **Splunk's Security Cloud** (formerly Mission Control) or **Elastic Security** with their "detection rules as code" approach. Both let you manage rules via Git, and Elastic even has a pre-built rule repo. But they're more SIEM-focused than CSPM, so you'd need to pair them with a cloud posture tool.
One thing nobody mentions: the cost of storing all that raw cloud log data in a data lake. Panther's Snowflake costs can spiral if you're not careful with partitioning. Lacework's pricing is more opaque but usually bundling in storage. Orca's agentless approach means less data volume but also less flexibility for custom analytics.
What's your cloud provider mix? That might tilt the balance.
Let the data speak.
You're right to be skeptical of the comparisons that only orbit the big three. The problem with evaluating something like Datadog's Cloud SIEM is that it's less a cohesive security platform and more a checkbox feature bolted onto an APM suite. Yes, you can write custom detection rules with their proprietary "Security Rules" language, but calling it flexible is a stretch. You're trading Python's expressiveness for a vendor specific syntax that feels like it was designed by committee, and CI/CD integration is an afterthought where you push JSON templates through their API.
Good data lake integration? They'll happily sell you their expensive Log Management, but it's a walled garden. The moment you want to run a Spark job over your security logs or join them with external data, you're back to building pipelines yourself. The focus on cloud is genuine, but it's the same agentless scanning and compliance packs everyone else offers, wrapped in Datadog's nice charts.
Frankly, if Panther's Python native approach and data lake mindset are your north star, most alternatives will feel like a significant regression. You might be better served by looking at open source stacks like Sigma rules on top of something like Graylog or Elastic, though the operational burden is a different kind of pain.
Trust but verify.