Linux endpoint visibility has long been a trade-off between signal fidelity and performance cost — but eBPF changes the math entirely. This episode of Cybersecurity explores how detection engineers can harness kernel-level instrumentation to build sensors that are precise, lightweight, and operationally sustainable. Drawing from this practical deep-dive on eBPF for Linux detection engineering, the episode walks through everything from kernel hook fundamentals to production rollout discipline.

Here's what the episode covers:

• eBPF fundamentals: How safely sandboxed bytecode runs inside the Linux kernel — attached to kprobes, uprobes, tracepoints, and network hooks — without kernel modules, reboots, or upstream patches.
• Hook selection strategy: Why the choice of hook type (process syscalls, uprobes for userland, XDP and socket hooks for network traffic) shapes everything downstream, and how to balance fidelity against resource cost across each domain.
• Kernel-to-userspace data pipeline: How eBPF maps — hash maps, LRU maps, per-CPU ring buffers — serve as the bridge between kernel-captured events and a lightweight user-space agent, and why keeping heavy lifting out of the kernel is critical.
• CO-RE portability: How Compile Once, Run Everywhere (using BTF type information and libbpf) eliminates the painful matrix of per-distribution kernel builds and simplifies rollouts.
• Performance as a feature: Practical techniques — bounded stack traces, per-CPU maps, selective sampling — that keep sensor CPU overhead well under one percent and prevent the security tool from becoming the performance incident.
• Signal design and enrichment: Why targeted, high-signal instrumentation beats firehose telemetry, and how enriching raw events with container metadata, package ownership, and process lineage shortens the path from alert to understanding.

The episode also addresses operational realities: using bpftool for staged validation, feature-flagging individual probes for fast rollback, growing instrumentation incrementally across process, file, and network domains, and — crucially — treating detection rules like software with versioning and clear intent documentation. A measured rollout strategy and tight analyst feedback loops are presented as the difference between a trusted sensor and a noisy one that breeds alert fatigue. For more on mapping and protecting sensitive data at scale, check out the episode DSPM in Practice: How to Map Sensitive Data at Scale.

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