Alright, so you've got Prolexic guarding the gates. Great. But now you want those sweet, sweet logs in Splunk to actually *do* something with them, and you looked at the "managed log delivery" option. Oof. The quote made your CFO do that thing where they just stare at you silently for a full minute.
Been there. You *can* self-serve this without handing Akamai a blank check. It's a bit of a DIY grind, but it works. The core idea is to avoid their premium push and instead pull via the APIs, then forward to your Heavy Forwarder or Indexer.
First, you need to set up the Prolexic Reporting API. This is your log source.
1. **Create an API client** in the Akamai Control Center (Prolexic section).
2. **Grab the credentials:** Client token, client secret, access token. Store these securely, obviously.
3. **Figure out your scope.** You probably want `mitigation-reports` and `alert-events`.
Now, the fun part: a script to poll and forward. I use a lightweight Python container on a cheap VM. Here's the gist of the fetch logic.
```python
import requests
import json
from datetime import datetime, timedelta
# Config
base_url = "https://prolexic.akamai.com"
client_token = "YOUR_CLIENT_TOKEN"
client_secret = "YOUR_CLIENT_SECRET"
access_token = "YOUR_ACCESS_TOKEN"
# Calculate time range (last 5 mins for near-realtime)
end_time = datetime.utcnow()
start_time = end_time - timedelta(minutes=5)
headers = {
"Authorization": f"Bearer {access_token}",
"Content-Type": "application/json"
}
# Fetch mitigation reports
report_params = {
"start": start_time.isoformat() + "Z",
"end": end_time.isoformat() + "Z"
}
response = requests.get(
f"{base_url}/api/v1/mitigation-reports",
headers=headers,
params=report_params,
auth=(client_token, client_secret)
)
# Process and forward to Splunk HTTP Event Collector (HEC)
logs = response.json()
for entry in logs:
# Format and send to HEC
splunk_payload = {
"event": entry,
"sourcetype": "akamai:prolexic",
"source": "prolexic-api"
}
# Your HEC forward logic here...
```
**Key points & pitfalls:**
* **Rate Limits:** The API has them. Don't hammer it. My 5-minute poll interval has been safe.
* **Data Volume:** During a big attack, the JSON payload can get hefty. Make sure your VM has enough memory and network bandwidth.
* **Parsing:** The API response isn't always Splunk-friendly out of the box. You'll likely need to flatten some nested JSON objects for optimal querying.
* **Alert vs. Report:** `alert-events` are near-realtime, `mitigation-reports` are more comprehensive but have a slight delay. I pull both on different schedules.
This setup costs you maybe $50/month for the VM and a bit of dev time, versus the eye-watering managed service fee. Is it as clean? No. Does it require you to babysit it? A little. But it gets the logs where you need them without the premium tax.
benchmarks or bust
That Python snippet is dangerously incomplete and you've glossed over the real beast, which is managing state for incremental pulls. If your script doesn't meticulously track the last successful timestamp, you'll either drown Splunk in duplicate events or miss data entirely. And you're running this on a "cheap VM"? I hope you like being paged at 3am when it inevitably dies.
You also need to consider the API's rate limiting and the log volume you're pulling. For a busy Prolexic setup, a simple linear poll might not keep up, and then you're building a backlog with no visibility. You'll end up writing a worse version of the managed service you refused to pay for.
Don't forget about parsing the JSON payload into Splunk-friendly CIM fields, either. That's another 100 lines of "DIY grind" you'll be debugging forever.
monoliths are not evil
Yeah, the state management part is what I'm worried about too. I'm new to setting up pipeline stuff like this.
For someone like me just trying to get this working, is there a simpler pattern for the timestamp tracking? Like, could you just write the last successful poll time to a small file and read it on the next run? Or is that too fragile?
Also, when you mention parsing for CIM fields - is that something we'd do in the script before forwarding, or can Splunk handle it with props.conf on the indexer side? Trying to figure out where to put that "100 lines of grind."
Yeah, the sticker shock is real! 😅 I tried the same route a few months back for our setup.
A cheap VM running a container is exactly how I started too, but I'd add one thing: make sure that VM has a decent bit of disk space for a buffer. The API can occasionally get delayed or have hiccups, and you don't want your script failing because it filled up the root volume.
What are you using inside the container for the actual forward to Splunk? I went with HEC directly at first, but switched to writing files and letting a Splunk forwarder pick them up. It felt a bit less fragile for retries.
Self-host or die trying.
Totally hear you on the buffer disk space - that's a good call. I've seen those API delays stretch longer than expected after a mitigation event.
> What are you using inside the container for the actual forward to Splunk?
I actually stuck with HEC, but I built a simple retry queue with a local SQLite table for failures. If the POST fails, the event gets parked and the script retries the backlog on the next run. It's a few extra steps but avoids the file management overhead. Writing to files and having a forwarder tail them is definitely the simpler pattern though, especially for someone newer to this. Did you find the file-based method introduced any noticeable latency in your log delivery?
✌️
That file-based method you landed on is the right call for reliability, but you've traded one cost for another. Now you're managing a disk volume and a forwarder's resource footprint. It's not just "buffer" space, it's a persistent, growing volume that needs monitoring, snapshots, and eventually a cleanup routine.
Your latency question is key. In my experience, the file tailing adds maybe 2-3 seconds over a direct HEC. That's nothing for most use cases. The bigger delay is still the API's own reporting latency, which is out of your hands.
The real break-even analysis isn't just about the Akamai quote versus your VM cost. It's the ongoing labor of keeping this cobbled-together pipeline running. When the API schema changes, or your forwarder crashes, that's you on the hook, not a support ticket. Sometimes the blank check is just paying for your own nights and weekends back.
Show me the bill
Yeah, that DIY approach is exactly what we're considering. That initial quote is wild.
When you created the API client, were there any specific permission gotchas or options you had to double-check to actually get the mitigation reports? I'm setting that part up now and the UI feels a bit cluttered.
Also, you mentioned a "cheap VM" - any ballpark on the resources (CPU/RAM) you ended up needing for a decent polling interval?
The permission scope you need is "prolexic-reporting-api" with "read-only" access. The UI is indeed cluttered; you'll find it under "Identity and Access Management", not within the Prolexic UI itself. Double-check the client is assigned to the correct contract group.
For VM sizing, a 2 vCPU, 4 GB RAM instance is sufficient for most, but it's entirely dependent on your event volume and polling interval. The bottleneck is rarely compute; it's network latency to the API and your disk I/O for state tracking. Start with that modest spec and monitor the script's runtime. If your poll takes longer than your interval, you'll need to scale.
Trust but verify.
Writing the last timestamp to a file is exactly the right starting point. It's not fragile; it's simple and easy to debug. The real fragility comes from people overcomplicating it with databases before they even know their volume. Just use a file.
As for the CIM fields, always push that parsing to Splunk with props.conf. The moment you bake it into your script, you've coupled your data pipeline to a specific Splunk schema. When the CIM updates, or you need a different view, you don't want to be rewriting and redeploying your API puller. Let Splunk do what it's good at. That's the whole point of a SIEM.
You're overthinking the "grind." The file is fine. The parsing is Splunk's job. Keep the script dumb.
monoliths are not evil
You missed the critical step of actually setting the initial time window. Your base_url and client_token are placeholders, but where's the logic to calculate `start_time` and `end_time`? Without it, your script's first run will do nothing or pull everything from the dawn of time.
Also, scope isn't just `mitigation-reports` and `alert-events`. You need to verify those are the exact names your API client can access. Get it wrong and you'll get empty pages, not an error.
And "cheap VM"? You better log the poll duration and track your API credits. That "DIY grind" becomes a full time job when you hit the rate limit because your interval's too aggressive.
Prove it.
Excellent point about the initial time window, that's a classic "works in the lab, fails in prod" omission. The first run logic needs careful handling. I usually seed the timestamp file with a value like 24 hours ago. You get a small batch of historical data, but you avoid a gigantic, rate-limit-busting pull from the epoch.
And you're absolutely right that an empty page doesn't mean "no data" - it could mean "no access." Always test your client's scope by making a manual call with curl and a known-good time range before letting a script loose. The API's silence on auth errors for specific scopes is a gotcha.
The rate limit tracking is the unsung hero of this whole setup. Logging the poll duration and counting fetched events is mandatory. If your script starts taking 11 minutes to run on a 10-minute interval, you're building a silent queue to disaster. 😅
Prod is the only environment that matters.