Deep Observability in Air-Gapped Environments with eBPF and Pixie

[Himanshu_GCC]

In modern enterprises, observability isn’t just about dashboards and logs—it’s about understanding system behavior in real time, especially in secure, restricted, or regulated environments. For organizations operating in air-gapped environments—where external connectivity is limited or prohibited—this challenge becomes even greater.

Traditional monitoring tools often rely on cloud-based agents, application instrumentation, or sidecar containers, all of which increase operational complexity or may not be viable in isolated environments.

This is where eBPF and Pixie offer a compelling alternative: deep, high-fidelity observability from inside the kernel—without needing internet access, code changes, or sidecars.

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What Is eBPF, and Why Should Enterprises Care?

eBPF (extended Berkeley Packet Filter) is a Linux kernel technology that allows you to run lightweight programs in response to system-level events like syscalls, network packets, and function calls. These programs can be dynamically attached to kernel hooks, enabling visibility into the system without modifying applications or restarting processes.

For observability, this unlocks several critical advantages:

• Real-time, low-latency insights from deep within the OS

• System-wide visibility, including user space, kernel space, and network stack

• Security and safety, as eBPF programs are sandboxed and verified before execution

• Minimal performance overhead, as most processing happens in kernel space

These traits make eBPF especially suitable for air-gapped and secure environments where performance, isolation, and compliance matter.

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Pixie: eBPF-Powered Observability for Kubernetes

Pixie is an open source observability platform that uses eBPF to automatically collect telemetry data from Kubernetes clusters. It operates entirely within the cluster, making it ideal for disconnected, air-gapped, or high-security deployments.

Unlike traditional APM or tracing tools, Pixie doesn’t require any application instrumentation or network egress. It uses eBPF to passively observe traffic and system events, building a detailed picture of how applications are behaving in real time.

Key Features of Pixie

• Automatic tracing of HTTP/gRPC traffic: Understand request flows, latencies, and failure rates without touching the application code.

• Live CPU profiling with flamegraphs: Identify resource-intensive functions inside running services.

• DNS and TCP-level inspection: Track failed lookups, dropped packets, and network anomalies at the node level.

• Pod-level metrics: Observe resource usage (CPU, memory), startup behavior, and lifecycle events.

Everything Pixie collects is processed and stored locally within the cluster. No data needs to leave the environment.

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Enterprise Use Cases in Air-Gapped Environments

1. Real-Time Application Tracing Without Instrumentation

In regulated industries like finance, healthcare, or government, introducing tracing libraries into codebases is a non-starter—too risky, too slow to validate, and often incompatible with legacy software.

Pixie sidesteps this by passively observing system calls and network flows at the kernel level. Enterprises can trace full request/response lifecycles, see which services are slow, and debug issues in real time—without modifying any applications or relying on an internet connection for data processing.

2. Security and Compliance Monitoring Without Agents

In air-gapped environments, installing external agents can introduce compliance challenges or expand the attack surface. Pixie uses eBPF to monitor DNS queries, TCP connections, and pod behaviors directly from the kernel, giving security teams visibility into:

• Unauthorized outbound connections

• DNS failures that may signal misconfigured workloads

• Sudden spikes in system calls or unusual network activity

This native, zero-instrumentation approach aligns well with zero-trust principles and regulatory frameworks that prioritize minimal software footprint.

3. Observing Short-Lived and Ephemeral Workloads

Pixie’s always-on nature means it automatically captures data from containers as they start, run, and exit—even if they only live for seconds. In air-gapped CI/CD pipelines or batch processing environments, this ensures nothing slips through the cracks.

Unlike traditional logging agents that rely on persistent sidecars or code hooks, Pixie can inspect pod activity from the outside, including:

• Crash loops and container restarts

• Cold start latencies

• Network connection attempts before readiness probes succeed

This is particularly valuable for debugging environments with high churn or automated job execution.

4. Continuous Performance Profiling at Scale

Resource optimization is critical in air-gapped data centers and edge locations where compute is limited. Pixie enables continuous CPU profiling at the function level, allowing engineers to pinpoint inefficiencies without introducing runtime overhead or cloud-based profilers.

Use cases include:

• Identifying performance regressions in deployed binaries

• Detecting tight loops or blocking calls in multithreaded applications

• Profiling system services running on constrained infrastructure (e.g., telco or manufacturing edge)

Flamegraphs generated from Pixie data help guide optimization without disrupting workloads or needing developer intervention.

5. Internal Developer Self-Service

In many enterprise environments, developers lack visibility into production workloads due to compliance or access restrictions. Pixie enables a model where internal developers can access live observability data via secured dashboards or queries—without requiring SSH access or debug privileges.

This accelerates debugging, reduces dependency on platform teams, and enables controlled observability that respects operational boundaries.

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Designed for Enterprise-Grade Environments

Pixie’s architecture is inherently compatible with enterprise needs:

• Fully in-cluster operation: No need for cloud services, internet access, or external storage

• Minimal system overhead: eBPF ensures lightweight execution without performance impact

• No code changes or application restarts: Ideal for legacy or black-box workloads

• Strong security posture: Sandboxed kernel programs with enforced verifiability

• Built-in data retention policies: Observability data remains internal, supporting data sovereignty

In air-gapped environments where operational control, performance, and compliance are non-negotiable, Pixie offers a way to deliver rich observability with minimal intrusion.

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Final Thoughts

In an era of distributed applications and growing regulatory pressure, observability must evolve. eBPF represents a fundamental shift: instead of bolting on observability, we now have the ability to observe from within.

Pixie turns this capability into a practical, developer-friendly solution—enabling enterprises to monitor, debug, and optimize their workloads securely, even when the outside world isn’t allowed in.

For enterprises operating in air-gapped environments—whether for security, compliance, or infrastructure isolation—Pixie is more than a tool. It’s a blueprint for modern, resilient observability.

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