Use Tetragon’s eBPF data to create dynamic security profiles that adapt to real workload behavior, reducing false positives and improving detection accuracy.

Why Observability Data Matters for Security Profiles

Tetragon’s eBPF hooks capture granular process activity—syscalls, socket connections, file operations—that static policies often miss. Real workloads include config reloads, maintenance tasks, and library calls that define “normal” behavior. Security profiles built from this data reflect actual runtime patterns, not idealized assumptions.

Actionable Workflow

1. Collect Baseline Data
   Deploy Tetragon with default rules to observe workloads for 72 hours (adjust based on workload variance).

   kubectl apply -f https://raw.githubusercontent.com/tetragon/tetragon/main/deploy/tetragon.yaml  
   

2. Analyze Syscall Patterns
   Use Tetragon’s syscall output or integrate with Falco to identify common syscalls per workload:

   kubectl logs -l app=tetragon -n tetragon | jq '.syscall' | sort | uniq -c  
   

3. Generate Minimal Profiles
   Create seccomp profiles allowing only observed syscalls. Example Falco rule snippet:

   - rule: Allow Docker Socket Access  
     desc: Permit docker.sock interactions for monitoring pods  
     condition: container.id != host and syscall=sysenter and not proc.name = "docker"  
     output: "Allowed docker.sock access (profile-based)"  
     log: true  
   

4. Deploy and Monitor
   Apply profiles via Kubernetes securityContext.seccompProfile. Validate with:

   kubectl exec -it <pod> -- /usr/bin/strace -s 120 -f -o /dev/stdout <command>  
   

Tooling

• Tetragon: eBPF-driven observability for syscall/network activity.
• Falco: Translates runtime data into enforceable rules.
• eBPF Tools: bpftrace, libbpf for deep analysis.
• Prometheus: Correlate Tetragon metrics with workload behavior.

Tradeoffs

• Performance Overhead: eBPF can add latency (mitigate with sampling: --ebpf-sampling-interval=1000ms).
• Profile Drift: Workloads evolve; automate periodic data collection (e.g., weekly).
• Noise vs. Signal: Overly permissive profiles may emerge if data collection includes anomalous activity.

Troubleshooting

• Missing Syscalls in Profiles:
  • Check Tetragon logs for dropped events (kubectl logs -l app=tetragon | grep "drop").
  • Extend data collection window to capture rare operations.
• False Positives:
  • Refine Falco rules with k8s.context fields to limit scope.
• Crashing Workloads:
  • Test profiles in staging; use seccomp: Unconfined as a fallback during debugging.

By grounding security profiles in observed behavior, you reduce reliance on static rules and align defenses with actual risk patterns. Adjust based on your workload’s variance and compliance needs.

Source thread: Question regarding Tetragon on Kubernetes: Why not use observability data to build Security Profiles?