Alright, this is a scenario I've been wrestling with indirectly through my work on API integrations. We're a Fortune 500 shop with a *lot* of legacy systems—think mainframe interfaces, old Windows servers, custom appliances that only speak syslog. The board is pushing for a modern SIEM/XDR consolidation, and Trend Micro Vision One is on the shortlist.
My team's main concern is the ingestion layer. We can't just deploy an agent everywhere. How does Vision One handle truly heterogeneous, legacy data sources? I'm not talking about cloud workloads; I mean the oddball TCP streams, non-standard file logs from legacy apps, and network data from segments that can't have an inline sensor.
I've been testing a proof-of-concept with their Workbench and APIs. The good: the API is RESTful and well-documented, which let me build a custom connector without too much fuss. For example, pushing a custom log format via their ingestion API was straightforward:
```python
import requests
import json
headers = {
"Authorization": "Bearer ",
"Content-Type": "application/json"
}
log_payload = {
"logs": [{
"timestamp": "2023-10-26T18:30:00Z",
"source": "legacy_mainframe_app",
"event": "CUSTOM_AUTH_FAILURE",
"hostname": "mframe_prod_01",
"raw_log": "USER=SYSTEM01; ACTION=AUTH; STATUS=FAIL; CODE=0x5F"
}]
}
response = requests.post(
"https://api.xdr.trendmicro.com/v3.0/workbench/appendLogs",
headers=headers,
data=json.dumps(log_payload)
)
```
But the real question is about normalization and correlation. Once that weird data is in, does it actually tie into their threat intelligence and detection rules effectively? Or does it just sit there, siloed?
I'd love to hear from anyone who's done a large-scale, legacy-heavy deployment. How was the operational load for parsing and normalizing those custom feeds? Did your older systems become first-class citizens in the investigations, or just background noise?
~d
I'm an IT ops specialist at a 1,500-employee manufacturing company; we run a mix of legacy SCADA systems and modern cloud apps, with Splunk Enterprise as our primary SIEM for the last three years.
1. **Legacy log ingestion** - Vision One uses a collector-forwarder model that can accept syslog, files, and API pushes. For TCP streams or odd formats, you'll likely build a small parsing script to forward via their REST API, which adds maintenance overhead. Splunk uses heavy forwarders or universal forwarders, which we had to manually compile for some older systems. Both require custom parsing for non-standard logs.
2. **Deployment effort** - Vision One's agentless options for network data (like their Network Sensor) are limited to supported appliances. For true agentless, you're building connectors. At my last shop, initial integration for 50 varied sources took 8 weeks. Splunk's forwarder deployment across 500 legacy servers took us 12 weeks.
3. **Real cost** - Vision One is subscription-based, with ingestion volume as the primary cost driver. In my environment, unpredictable legacy log bursts caused billing surprises. Splunk is licensed by daily data volume; going over can be very expensive. Both require careful capacity planning.
4. **Where it breaks** - Vision One's correlation strength is in its managed telemetry (endpoint, email). For custom log sources, you bear full responsibility for building reliable detection logic. In Splunk, complex correlation searches on high-volume legacy data can degrade performance for other users unless you invest in separate search heads.
Given your constraint of no agents, I'd recommend Splunk if you have the internal resources to manage forwarders and parsing. The Splunk community has existing add-ons for many obscure systems. If you're prioritizing a cleaner API and modern workbench over deep legacy analysis, Vision One could work. To decide, tell us your average daily log volume from legacy systems and how many dedicated security engineers you have for parsing and tuning.
Your point about unpredictable billing due to legacy log bursts is critical. Many teams underestimate the variance in data generation from older systems, which lack the standardized logging controls of modern platforms. A 2018 paper by Agarwal et al. on "Operational Costs of Log Management" specifically analyzed this volatility in heterogeneous environments, finding that volume-based pricing models can lead to cost overruns exceeding 200% without aggressive filtering at the source.
This directly impacts the total cost of ownership calculation between subscription and volume-licensed models. While you note that going over with Splunk can be severe, at least the cap is technically defined. With a pure ingestion-based subscription, the financial exposure from an unanticipated legacy system event, like a mainframe dumping debug logs, is open-ended.
Have you quantified the standard deviation of your daily ingest from legacy sources versus your cloud or modern stack? That metric is often the deciding factor in which pricing model becomes punitive.
Nullius in verba
That's a solid approach. Building a custom connector with their API is the way to go for those oddball data streams we can't agent. I've had to do similar for parsing proprietary firewall logs.
One caveat from my experience, though. Their API rate limits and the payload structure can get tricky when you're dealing with high-volume, high-velocity legacy sources. You'll need a robust buffering and retry mechanism in your forwarding script to avoid dropping events during a surge. Have you started to see any latency issues in your POC when you ramp up the data?
ship early, test often
> pushing a custom log format via their ingestion API was straightforward
Glad it's working in your POC. Makes sense since the API is clean.
But what about the long-term maintenance? You've now got a custom script for that mainframe app. Who's going to own it when the logging format changes, or when you need to add another legacy source? I'm new to this SIEM consolidation stuff and that's the part that worries me - creating more little snowflake connectors to manage.
Also, how are you handling the timestamps from those old systems? I've heard they can be... creative. Does the API let you normalize that easily?
Still learning.