Runtime Security Monitoring for Kubernetes: Comparing eBPF, Seccomp, and Behavioral Analysis

Kubernetes runtime security monitoring has no single right answer. The options—eBPF-based tools like Falco and Cilium, seccomp profiles, and behavioral baseline analysis—each address different parts of the threat model. Each has specific blind spots. And the organizations that have the most complete runtime security coverage use multiple approaches in combination, not any single tool as the complete solution.

Understanding what each approach actually does and doesn’t do is prerequisite to building a monitoring stack that covers the threat model you actually face.

eBPF-Based Detection (Falco, Cilium)

eBPF monitoring attaches to kernel events—system calls, network events, file system operations—and evaluates them against rules defined by the security team. When a running container makes a system call that matches a detection rule, the tool fires an alert.

Strengths: Low overhead at the kernel level, able to detect specific system call sequences associated with known attack patterns, covers all containers on the node without per-pod configuration.

Limitations: Rules are only as good as the threat intelligence they encode. Novel attack techniques that don’t match existing rules are invisible. Rule management is ongoing operational work—rules need to be written, tuned, and updated as both the application and the threat landscape evolve. High-volume environments produce significant alert noise that requires tuning to make actionable.

Seccomp Profiles

Seccomp allows defining which system calls a container is permitted to make and blocking all others. A tightly defined seccomp profile dramatically limits what a compromised container can do: it cannot make system calls that aren’t in the allowlist.

Strengths: Prevents specific exploitation techniques that require unusual system calls. Relatively low overhead. Provides a hard enforcement boundary, not just detection.

Limitations: Seccomp profiles are difficult to build correctly for complex applications. Most teams use the default Kubernetes seccomp profile (RuntimeDefault) rather than application-specific profiles, which limits the protection significantly. Application updates can break seccomp profiles if the update requires new system calls.

Behavioral Baseline Analysis

Behavioral baseline analysis builds a profile of expected container behavior during a controlled profiling run, then monitors for deviations in production. Instead of matching against known-bad signatures, it alerts when behavior deviates from the established normal.

Strengths: Catches novel behavior that doesn’t match any existing signature. Effective against post-exploitation activity where the attacker is using legitimate tools in unexpected ways. Baselines on minimal containers produce tight profiles with low false positive rates.

Limitations: Requires upfront profiling work to establish baselines. Baseline quality determines alert quality. Requires maintenance when application behavior legitimately changes.

No single approach covers the full threat model. eBPF rules catch known attack techniques. Seccomp blocks specific system call abuse. Behavioral analysis catches novel behavior that neither can detect.

Where Each Approach Has Blind Spots?

eBPF rules catch what the rule author anticipated. Seccomp blocks what the profile author listed. Behavioral analysis misses attacker behavior that falls within the established baseline—if a normal part of the application makes the same system call an attacker would use, behavioral analysis cannot distinguish them.

The complementary coverage is the point: the detection gaps of each approach are partially covered by the others.

• eBPF without behavioral analysis: catches known patterns, misses unknown attacker techniques that don’t match signatures
• Seccomp without behavioral analysis: prevents specific abuse, doesn’t detect how an attacker uses permitted calls
• Behavioral analysis without eBPF: catches deviations from baseline, may be slower to detect if the baseline accidentally includes an attacker’s technique

Attack surface reduction as the foundation

Container image tool hardening that removes unused packages reduces the behavioral footprint that all three monitoring approaches must cover. A container with 20 packages has fewer system calls, fewer network behaviors, and fewer filesystem access patterns than a container with 200 packages. Behavioral baselines are tighter. Seccomp profiles are more accurate. eBPF rule tuning is simpler because there’s less legitimate behavior to distinguish from malicious behavior.

Container vulnerability scanner outputs also directly affect the eBPF coverage problem: known CVEs in container packages provide intelligence for writing detection rules. Containers with near-zero CVEs have fewer known exploitation paths requiring eBPF rules.

Practical Steps for Building a Layered Monitoring Stack

Start with Kubernetes RuntimeDefault seccomp profile for all workloads, immediately. This is a baseline that requires minimal effort and provides real protection. The RuntimeDefault profile blocks syscalls that no standard application needs and that many exploits rely on.

Deploy Falco with the default ruleset, then tune based on your environment. Default Falco rules are a starting point, not an endpoint. Two weeks of running with defaults reveals the false positive rate for your specific workloads. Tune rules to eliminate noise before adding more rules.

Build behavioral baselines for your highest-priority workloads. Prioritize the containers that process sensitive data or have the highest blast radius if compromised. Behavioral analysis on these containers provides the complementary detection that eBPF rules and seccomp don’t cover.

Correlate alerts across all three monitoring layers. A container triggering Falco rules, violating a seccomp profile, and showing behavioral drift simultaneously is a high-confidence compromise signal. Correlated alerts across monitoring layers warrant immediate response; single-layer alerts warrant investigation.

Review coverage after red team exercises. The most useful information for tuning a monitoring stack comes from offensive security exercises. Red team findings that weren’t detected reveal which monitoring approaches have coverage gaps in your specific environment.

Frequently Asked Questions

What is eBPF-based Kubernetes runtime security monitoring and what are its limitations?

eBPF monitoring attaches to kernel events—system calls, network events, file system operations—and evaluates them against detection rules. Tools like Falco use eBPF to fire alerts when running containers match known-bad system call patterns. The core limitation is rule dependency: eBPF rules catch attack techniques that the rule author anticipated. Novel attack techniques that don’t match existing signatures are invisible. Rule management is also ongoing operational work, requiring continuous tuning as applications evolve and threat intelligence updates.

How does behavioral baseline analysis complement eBPF and seccomp for Kubernetes runtime security monitoring?

Behavioral baseline analysis catches what eBPF rules and seccomp profiles cannot: attacker behavior that uses legitimate system calls or permitted operations in unexpected ways. Where eBPF matches against known-bad signatures and seccomp blocks specific call abuse, behavioral analysis detects deviation from established normal behavior—regardless of whether the deviation matches any existing rule. An attacker performing post-exploitation activity using the container’s own permitted tools will produce behavioral drift visible to baseline analysis while potentially evading eBPF rules and operating within seccomp boundaries.

What is the recommended approach to deploying runtime security monitoring for Kubernetes?

The recommended approach layers all three methods. Start with the Kubernetes RuntimeDefault seccomp profile for all workloads immediately—it requires minimal effort and blocks system calls no standard application needs. Deploy Falco with default rules and spend two weeks tuning against your specific workload false positive rate before adding more rules. Build behavioral baselines for highest-priority containers to provide the complementary detection that eBPF and seccomp don’t cover. Correlate alerts across all three layers: simultaneous alerts from multiple monitoring approaches are high-confidence compromise signals.

How does container hardening improve the effectiveness of Kubernetes runtime security monitoring?

Container hardening that removes unused packages reduces the behavioral footprint that all three monitoring approaches must cover. A container with 20 packages makes fewer system calls, establishes fewer network patterns, and accesses fewer filesystem paths than a container with 200 packages. This smaller footprint makes behavioral baselines tighter with fewer false positives, seccomp profiles more accurate because there are fewer legitimate call patterns to distinguish from malicious ones, and eBPF rule tuning simpler because there is less normal behavior overlapping with attack signatures.

The Coverage That Combines Them

Organizations that have implemented all three approaches describe a qualitative improvement in confidence: they know they’re catching novel attacks (behavioral analysis), known attack techniques (eBPF), and preventing abuse of permitted system calls (seccomp). The monitoring gaps that exist in each approach individually are partially covered by the others.

This complementary coverage is the argument for the layered approach. Any single tool’s blind spots are significant. The combination’s blind spots are smaller and better understood.

Smaller attack surface feeds all three approaches with better signal: fewer legitimate behaviors make detection more precise, seccomp profiles more accurate, and behavioral baselines more reliable. The monitoring investment and the hardening investment reinforce each other.