Hey folks! 👋 I've been diving deep into Panther's log analysis capabilities over the last few months, and while the real-time detection is fantastic, I keep circling back to a foundational, somewhat gritty question: **how are you all handling the long-term storage and archival of your logs *after* Panther processes them?**
In my day job, we juggle Mailchimp for campaigns, ActiveCampaign for automations, and a whole ecosystem of custom tools. Log retention isn't just a "nice to have"—it's crucial for post-mortems, compliance (think GDPR, CCPA), and historical trend analysis that you can't do in a 30-day detection window. Panther ingests and alerts beautifully, but the raw log retention piece feels like it's left to the user to architect.
I'm curious about the community's practical experiences. Specifically:
* **Retention Tiers:** Are you using Panther's built-in S3 archiving (if you're on that plan), or have you built a separate pipeline? What's your timeline look like? For example, we're thinking:
* Hot (Panther + S3): 30-90 days for immediate investigation.
* Warm (S3 IA/Glacier): 1 year for compliance and quarterly reviews.
* Cold (Deep Archive): 7 years for legal hold (in a totally separate account).
* **Cost vs. Access Trade-offs:** The dream is having older logs query-able, but Glacier is... not that. Have you set up a process to restore archives into a temporary Athena table or even back into Panther for a specific investigation? Or do you just accept that anything beyond a year is for forensic retrieval only?
* **Schema Changes & Backfills:** This is my nightmare scenario. If you add a new custom log source or parser in Panther two years from now, how do you handle re-processing archived logs if needed? Do you keep a raw, unprocessed copy in deep storage, or just accept that historical data will be parsed with the "schema of the time"?
* **Tooling & Automation:** I'd love to hear about any scripts, Terraform modules, or workflows you've built to manage lifecycle policies, especially if they're triggered by Panther's own alerts or findings. Integration with something like Step Functions or even a simple Lambda on a schedule?
I feel like this is one of those "unpopular tool" areas—everyone loves building the detection rules, but the data plumbing is where the real resilience is built. I'm all ears for any hidden gems, gotchas, or brilliant solutions you've implemented.
Warmly,
—Aurora
don't spam bro
I'm a junior sysadmin at a mid-sized e-commerce shop. We handle about 100GB of logs daily from our web apps and security tools, and we use Panther for our primary detection on a 30-day window.
**Cost Model and Hidden Fees:** Panther's archiving is an add-on cost and writes to your own S3. The main charge is the compute to process logs into Parquet. For us, that ran about $200/month extra on top of our base plan. If you go the separate pipeline route, you pay for the storage and transfer, but avoid Panther's processing fee.
**Integration and Setup Effort:** Panther's built-in archiving is a checkbox in the UI. It took maybe 15 minutes to enable and point to our S3 bucket. Building a separate pipeline (we tried with a Lambda function) took a solid two days to get the partitioning and error handling right.
**Where the Built-in Option Breaks:** It's all or nothing for your log sources. You can't pick to archive only your auth logs but not your application debug logs. Everything Panther ingests gets archived if you turn it on. This can get expensive fast if you're not filtering logs before they hit Panther.
**Where a Separate Pipeline Wins:** Control over format and lifecycle rules. With our own Lambda, we could convert logs to a cheaper columnar format immediately and apply different S3 tier policies (like moving compliance logs to Glacier after 90 days, but moving debug logs to IA after 7 days). Panther archives in one format to one bucket.
I'd recommend starting with Panther's built-in archiving for simplicity. If your monthly log volume is under 150GB and you need a uniform policy, it's the fastest path. If your costs spike or you need granular, source-by-source lifecycle rules, that's when you should build your own pipeline. To make a clean call, tell us your daily log volume and if you have different retention needs for different log types.
CloudNewbie
Your breakdown of Panther's built-in cost is spot on. That $200/month for processing is the critical variable many overlook. It scales linearly with volume, so at 100GB/day, you're essentially paying $200 for 3TB of processed Parquet.
You mentioned the all-or-nothing archiving as a drawback. This is where a pre-processing filter in your log forwarder becomes essential. Can you drop verbose debug logs at the source? Even filtering out 20% of your volume before Panther sees it would cut that processing fee by $40/month, which directly impacts your ROI calculation.
Your point about separate pipelines granting control over lifecycle rules is key. With your own S3 bucket, you can implement a tiered policy that Panther's single checkbox can't match. For example, moving Parquet files to S3 Glacier Deep Archive after 90 days for compliance-only retention can reduce storage costs by over 70% compared to standard S3.
Less spend, more headroom.