Autonomous Runtime Defense
A Kubernetes sidecar that learns your workload's normal behavior and detects when something goes wrong. No rules. No configuration. Just deploy.
A Kubernetes sidecar that learns your workload's normal behavior and detects when something goes wrong. No rules. No configuration. Just deploy.
Traditional runtime security tools require you to write detection rules for every threat you want to catch. If you don't write the rule, you miss the attack.
PandoCore takes a different approach. It automatically learns what normal looks like for each workload, then continuously monitors for deviations. When anomalous behavior is detected, it responds with a graduated action chain: alert, isolate, or terminate.
Automatically learns normal patterns and detects anomalies
Graduated response: alert, network isolation, terminate
No rules to write, works out of the box
~5-6 MiB memory footprint, measured across 20,000+ continuous pod-hours on GKE
PandoCore installs an admission webhook in its own namespace and injects a sidecar only into the pods you label, leaving the rest of your cluster untouched.
Not a replacement. No new tooling. PandoCore operates transparently alongside your existing stack, monitoring automatically at runtime.
Our approach to security and reliability is built on fundamental principles:
Designed to detect and respond to common attack vectors including debugging attempts, memory inspection, code modification, and timing analysis. The system actively monitors execution to identify anomalous conditions.
Cybersecurity requires multiple mechanisms working in concert. We encourage a layered approach where PandoCore works in tandem with encryption, authentication controls and observation. This ensures that compromise of any single element doesn't undermine overall protection.
Validated across more than 20,000 continuous pod-hours on GKE, running production-representative synthetic workloads, with 2 false isolations and zero false terminations. Every capability is backed by measured results from continuous soak testing against diverse production-representative applications.
PandoCore has been deployed alongside 10+ real-world workload types across extended continuous soak tests. No manual configuration or tuning was performed. All workloads ran with default settings.
Two false isolations and zero false terminations across more than 20,000 continuous pod-hours on GKE, against production-representative synthetic workloads rather than customer traffic, means PandoCore's automated responses can run without disrupting legitimate workloads. The sidecar profiles each workload automatically across web servers, databases, and JIT and interpreted runtimes, with no manual tuning required.
We're working with teams to bring behavioral runtime detection and response to production Kubernetes clusters. If you run sensitive workloads on Kubernetes, let's talk.