Just finished setting up an automated playbook in TheHive to quarantine suspicious endpoints using Elastic Security. I'm always tinkering with workflows between platforms, and this one felt like a major win for cutting down response time.
The trigger is an Elastic alert with specific severity and tags. The playbook then:
* Pulls the endpoint hostname from the alert.
* Uses the Elastic Security API to initiate an isolation request.
* Updates the case in TheHive with the action taken and waits for a confirmation status from Elastic before closing the task.
I'm curious how others have approached this. The integration is solid, but I found the API documentation for the isolation endpoint a bit sparse compared to, say, Salesforce or HubSpot's API guides. Had to do some trial and error with the JSON payload.
Has anyone else built similar automated containment? I'm wondering about:
* Handling false positives – do you have a manual approval step, or is it fully automated based on alert confidence?
* What other automated actions are you triggering from Elastic alerts? (Besides the usual ticket creation in ServiceNow or Jira).
* How does the isolation performance feel in practice? Any noticeable lag on your endpoints?
Thinking about expanding this to auto-collect additional forensic data from the endpoint before quarantine, but don't want to overcomplicate the playbook.
Still looking for the perfect one
That's a solid integration. The Elastic Security API documentation can indeed be sparse, especially for newer endpoints.
On false positives, we run fully automated isolation only for alerts with a confidence score above 90%. Anything below that routes to a manual-approval task in the same playbook before any action is taken. It's a necessary gate to prevent operational disruption.
For other actions, we've had success with automated evidence collection. The playbook triggers a memory dump and disk artifact collection via Elastic's Live Response API immediately after isolation, preserving data for analysis. The isolation latency we've measured averages between 8-12 seconds from alert to confirmed endpoint state, which is acceptable for our thresholds.
BenchMark
That 8-12 second latency figure is telling. I've seen similar numbers with Elastic, but the real bottleneck often isn't the API - it's the endpoint agent's polling interval for new actions. If you're not already, consider tagging those isolated hosts in your Kubernetes or CMDB system immediately. That way your service mesh or ingress controllers can start rerouting traffic away from them while you wait for the physical isolation to confirm, adding a layer of containment at the network layer.
On false positives, a confidence threshold is a good start, but we also added a system-context check. The playbook first queries our inventory to see if the endpoint is, for example, a CI/CD runner or a developer's sandbox node. If it is, it requires manual approval regardless of confidence score. The JSON payload pain is real; Elastic's API feels like an afterthought compared to their query DSL.
Beyond evidence collection, we auto-scale down the relevant Kubernetes deployments to zero replicas if the isolated endpoint is a node. It's a blunt instrument, but it stops any pod evictions from rescheduling compromised workloads elsewhere. Have you looked at tying the playbook outcomes back into your observability stack, like creating a dedicated Grafana panel for mean-time-to-contain derived from these alert timelines?
Nice setup! I definitely feel your pain about the sparse API docs. The isolation endpoint's response format especially had me scratching my head for a bit.
On your questions: We run fully automated for a shortlist of "smoking gun" alert rule IDs we really trust. Everything else gets a 2-minute timer in TheHive for an analyst to cancel before isolation kicks off.
We also auto-trigger a snapshot of the endpoint's recent network connections from Elastic right after the quarantine request. It helps connect the dots faster for the investigation phase.
Pipeline Pilot
Solid work on the playbook. That waiting step for the confirmation status before closing the task is a great practice. It creates a clean audit trail.
On handling false positives, I like the approach shared earlier about checking the system's role from an inventory first. I'd add that you should also consider the time of day. Automating isolation overnight for an endpoint on a finance user's laptop might have less operational impact than during market hours. A simple time-check in the playbook can add useful context.
For other actions, we've tied similar alerts to automated, temporary elevation of logging verbosity on the affected endpoint for a set period. It gives the subsequent investigation much richer data to work with.
Quality over quantity.
Building that confirmation wait step into the playbook is the smartest thing you did. It forces the audit trail to be accurate. Without it, you have no guarantee the action succeeded.
On false positives, I'd caution against fully automated isolation, even with high confidence scores. Everyone thinks their thresholds are safe until they quarantine a CEO's laptop during a quarterly report. Start with a short, timed approval window like user122 mentioned, or incorporate a system role check as others have suggested.
The performance question is key. Don't just measure API latency. Track the full cycle from alert generation to the endpoint actually being unreachable from your network. That's your real containment time.
—AF
That's a great addition about time of day context. It's an extra layer I hadn't thought about, but it makes so much sense for minimizing user disruption. It could be as simple as a lookup to a 'business hours' table for different departments.
I'm curious, do you run into any issues with timezones for remote teams? We have folks scattered across the globe, so our automated 'overnight' would have to be location-aware to be useful.
Always testing.
I've built a few similar chains. Your point about sparse API docs is dead on, especially for the confirmation polling. That wait step is critical, but you need to build in a timeout and a failure path. The API might just hang.
On false positives, I'd avoid full automation for isolation, period. Even a 60-second delay for an analyst to cancel can save you from a major outage. We route these playbooks through a dedicated Slack channel with a button to block the action.
For other actions, we auto-trigger a parallel evidence collection workflow. While isolation is pending, we kick off a job to pull the endpoint's process list and recent logs into cold storage. It gives the investigation a head start without slowing the containment.
garbage in, garbage out
The "smoking gun" list approach is smart - we use something similar but had to build a review process for that list itself. We treat it like a change control board, because a rule that's safe today might generate chaos after a new software deployment.
That 2-minute timer is a lifesaver. We pair it with an alert in our ops chat, and we track how often analysts cancel. If a rule gets cancelled more than, say, 30% of the time, it gets pulled from the automated list for review. It creates a nice feedback loop.
Snapping the network connections is brilliant for context. Do you feed that snapshot back into your SIEM as an observable, or just attach it to the TheHive case?
Implementation is 80% process, 20% tool.