Panther or Sumo Logic for real-time threat detection in finance?

Hi everyone,

I've been neck-deep in a project to overhaul our real-time threat detection pipeline for a mid-sized fintech, and the core decision came down to Panther and Sumo Logic. We've now done extensive POCs with both, and I wanted to share a detailed, hands-on comparison from an integration and operations standpoint, as the marketing materials often gloss over the day-to-day realities.

Our core requirements were:
* Real-time parsing and alerting on transaction logs, IAM events, and cloud audit trails (AWS mostly).
* Ability to model complex, multi-source threat scenarios (e.g., a login from a new device followed immediately by a high-value transfer).
* A workflow that allows our security analysts to quickly iterate on detection logic without constant developer tickets.
* Cost predictability, given our log volume can be spikey.

**Panther's Customization Edge**
Where Panther truly shines is in its developer-first approach. Writing detection logic in Python gives you immense flexibility. For instance, we needed a rule that correlated a failed database authentication attempt from a specific IP with a subsequent, successful but anomalous API call from the same user session. In Panther, this was a relatively straightforward Python function querying against its built-in data lake.

```python
def rule(event):
# Example simplified logic
if event.get('eventType') == 'AWS.CloudTrail' and event.get('errorCode') == 'Client.AuthenticationFailure':
# Store or correlate with later events in a helper function
return True
return False

def dedup(event):
return event.get('sourceIPAddress')
```

The ability to use Python libraries and custom data models let us build very precise, context-aware detections that would require significant workarounds in other platforms.

**Sumo Logic's Operational Strengths**
Sumo Logic, conversely, feels like a fully managed, polished appliance. Its query language is powerful for those familiar with SQL-like syntax, and the out-of-the-box dashboards and compliance apps for finance are excellent. The ingestion pipeline is robust and requires less tinkering. However, we found its ability to handle truly custom, stateful correlation across disparate log sources to be more constrained. You often have to structure your queries and data around its paradigm, and more complex multi-step logic can become cumbersome and expensive.

**The Integration & Workflow Verdict**
For our team, the deciding factor was the **workflow**. Panther's model—code in a Git repository, CI/CD pipelines for testing detections, and a unified data lake—aligns with how our engineering and SecOps teams already operate. It feels like building a custom integration, which gives us control. Sumo Logic is more of a sealed, powerful box; it works brilliantly if your use cases fit neatly inside it, but you hit walls faster when they don't.

If you have a team comfortable with code and need to build highly specific, evolving detection logic, Panther is a compelling choice despite its steeper initial setup. If you prioritize out-of-the-box analytics, robust ingestion, and have more standard detection needs, Sumo Logic will get you live faster.

Would love to hear if others have tackled similar comparisons, especially around managing false positives or automating response actions via webhooks from these platforms.

api first