Best eBPF Solutions for Security: A Practitioner’s Guide to Runtime Protection

Key Insights

What is the best eBPF security tool for Kubernetes? For detection-only, Falco. For detection plus enforcement, Tetragon or KubeArmor. For full-stack correlation across cloud, Kubernetes, container, and application layers, ARMO CADR. The right choice depends on whether you need basic visibility, policy enforcement, or complete attack story generation that reduces investigation time by 90%+.

Why do most eBPF security tools fail teams? They create more alerts, not better understanding. Each tool monitors its own layer—containers, Kubernetes, cloud—generating separate alerts that require hours of manual correlation. Four alerts about the same attack look like four unrelated incidents until someone connects the dots.

What’s the difference between detection and correlation? Detection identifies suspicious events. Correlation connects them into attack stories. Falco might flag a suspicious process; your CSPM notes a misconfigured IAM role; CloudTrail shows unusual API calls. Detection gives you three alerts. Correlation shows you one attack chain.

How does ARMO reduce investigation time by 90%? ARMO collects signals across cloud APIs, Kubernetes control plane, container runtime, and application code simultaneously. When related events occur, an LLM-powered correlation engine links them into a single attack story showing exactly how the attack progressed—eliminating manual correlation work.

Which eBPF tool has the lowest performance overhead? Tetragon leads with aggressive in-kernel filtering. ARMO achieves 1-2.5% CPU and 1% memory despite monitoring more layers than alternatives. Falco is low to moderate depending on rule complexity. Tracee has higher overhead (2-4x Tetragon) due to deep kernel tracing.

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Last month, your team spent 11 hours investigating what turned out to be a single attack. Falco flagged a suspicious process in a container. Your CSPM had already noted an overly permissive IAM role. CloudTrail showed unusual API calls. Kubernetes audit logs recorded a service account accessing secrets it shouldn’t touch.

Four alerts. Four dashboards. Four analysts working in parallel before someone finally asked: “Wait, are these all the same thing?”

They were. By the time the team connected the dots, the attacker had been inside for six hours.

This is the reality of Kubernetes runtime security in 2025. eBPF has become the foundation for modern runtime security because it provides kernel-level visibility without the overhead of traditional agents. But the technology is only as good as what you build on top of it. Most eBPF security tools excel at detection but leave teams with disconnected alerts that require hours of manual correlation.

The best eBPF security solution isn’t the one with the longest feature list. It’s the one that connects suspicious behavior across your entire cloud stack into a full attack story—showing you exactly what happened, how it progressed, and what to do about it.

This guide compares the leading eBPF security tools—Falco, Tetragon, Tracee, KubeArmor, and ARMO—and provides a framework for choosing based on what your team actually needs.

What Makes an eBPF Security Tool “Best”?

Before comparing specific tools, you need evaluation criteria that actually matter for production security—not marketing checkboxes.

Detection depth: Can the tool see into containers, applications, and code-level behavior? Some eBPF tools stop at syscall monitoring. Others trace all the way down to which function in which library made which call. For application-layer attacks like SQL injection or SSRF, syscall monitoring alone isn’t enough.

Correlation capability: Does it connect events across cloud, Kubernetes, container, and application layers? Detection without correlation just creates more alerts to investigate. This is where most tools fall short—they see their layer well but can’t connect it to what’s happening above or below.

Response options: Can you actually stop threats, or just watch them happen? There’s a significant gap between detection-only tools and those offering kill, stop, pause, or quarantine capabilities. Detection without response is expensive logging.

Behavioral vs. rule-based detection: Static signatures miss novel attacks. Adaptive baselines that learn what “normal” looks like for each workload catch anomalies that rule-based systems never see—including zero-days with no CVE to match.

Performance overhead: Security that degrades application performance doesn’t last in production. CPU and memory impact need to be predictable and minimal—ideally under 2% CPU.

Open-source foundation: Community validation and code transparency matter when you’re trusting a tool with kernel-level access. You should be able to inspect what’s running in your kernel.

The “Siloed Alert” Problem

Consider what happens when an attacker compromises a web application. Your application security tool might flag unusual database queries. Your container runtime monitor might notice a new process spawning. Your Kubernetes audit logs might show unexpected API calls. Your cloud provider might alert on unusual IAM activity.

That’s four alerts across four tools. Each one looks like noise in isolation. A security analyst has to manually correlate timestamps, cross-reference container IDs with pod names with cloud identities, and reconstruct the attack timeline. This takes hours—if it happens at all. Most of the time, alerts get triaged individually and the bigger picture never emerges.

Alert fatigue consistently ranks as the top challenge in cloud security surveys—teams have plenty of detection, but correlation is manual. The result: security teams spend more time investigating noise than responding to real threats.

Why Full-Stack Correlation Matters

The difference between good eBPF security and great eBPF security isn’t detection—it’s what happens after detection.

Full-stack correlation means connecting events across ADR (application detection and response), CDR (cloud detection and response), KDR (Kubernetes detection and response), and EDR (endpoint detection and response) into a unified view. This is the approach behind Cloud Application Detection and Response (CADR)—a category that unifies these traditionally separate tools.

When these layers talk to each other, four separate alerts become one attack story that shows: initial access through an application vulnerability, lateral movement through container escape, privilege escalation via Kubernetes RBAC, and data exfiltration through cloud APIs.

That story changes everything. Instead of investigating four alerts separately, an analyst sees the complete attack chain and can respond to the actual threat—not individual symptoms.

eBPF Security Tool Categories

Not all eBPF security tools solve the same problems. Understanding the categories helps you match tools to your actual needs.

Detection-only tools (Falco, Tracee): These monitor runtime behavior and generate alerts. They’re excellent for visibility and work well as a starting point. Limitation: alerts without response capabilities mean threats continue while you investigate.

Detection + enforcement tools (Tetragon, KubeArmor): These add policy enforcement to detection. You can block specific behaviors, not just alert on them. Limitation: still single-layer focused—they don’t correlate across your full stack.

Networking + security tools (Cilium): Primary focus is container networking with security features added. Strong network policy and observability, but security isn’t the core use case.

Full CADR platforms (ARMO): Cloud Application Detection and Response combines detection, correlation, and response across all layers. These tools aim to replace multiple point solutions with unified visibility and automated attack story generation.

The category you need depends on your current pain points. If you have no runtime visibility, detection-only tools are a solid start. If you’re already drowning in siloed alerts, you need correlation capabilities.

Comparing the Best eBPF Security Solutions

Here’s how each tool performs against these criteria.

ARMO Platform (with Kubescape)

ARMO takes a fundamentally different approach with Cloud Application Detection and Response (CADR). Rather than monitoring a single layer, ARMO correlates signals across cloud, Kubernetes, container, and application layers to generate complete attack stories.

The headline outcome: ARMO reduces investigation time by over 90%. Here’s how: instead of four separate alerts you have to correlate manually, ARMO’s LLM-powered correlation engine connects events across cloud APIs, Kubernetes control plane, container runtime, and application code into a single attack story. You see the complete chain—initial access through data exfiltration—with timestamps, affected assets, and recommended response actions.

This works because ARMO collects signals across layers that other tools treat as separate domains: ADR (application), CDR (cloud), KDR (Kubernetes), and EDR (endpoint). Application Profile DNA creates behavioral baselines for each workload, so anomaly detection catches threats that rule-based systems miss—including zero-days with no signature to match.

Additional capabilities: Response options include kill, stop, pause, and soft quarantine. Sees from code/function level to cloud API—not just container syscalls. Runtime reachability analysis identifies which vulnerabilities are actually loaded into memory and executed, typically reducing actionable CVE counts by 90%+.

Built on Kubescape: ARMO is built on Kubescape, a CNCF project trusted by 50,000+ organizations with 100,000+ deployments. This open-source foundation means you can inspect the detection logic rather than trusting vendor claims.

Best for: Teams drowning in siloed alerts who need complete attack visibility and faster investigation. Organizations ready to consolidate point solutions into unified runtime security. Security teams where investigation time is a critical bottleneck.

Performance: 1-2.5% CPU and 1% memory consumption—production-ready overhead that doesn’t compete with your applications, despite monitoring more layers than the alternatives.

Falco

Falco is the most widely adopted open-source runtime security tool, now a CNCF graduated project. It monitors system calls and generates alerts based on a rules engine.

What Falco does well: Mature detection engine with years of production hardening. Large library of pre-built rules for common threats. Strong community support and extensive documentation. Low barrier to entry—you can get basic visibility running within an hour.

Where Falco falls short: Detection only—no native response capabilities. High false positive rates out of the box; teams commonly report 80%+ of default Falco alerts are noise until rules are customized. Tuning requires deep knowledge of both your applications and Falco’s rule syntax, often taking weeks to get right. No cross-layer correlation; you see container events but not how they connect to application or cloud activity. Rules-based approach means novel attacks that don’t match existing signatures slip through.

Best for: Teams needing basic runtime threat detection as a starting point, especially those comfortable with rule customization and who have existing SIEM infrastructure for alert correlation.

Tetragon

Tetragon comes from Isovalent (the Cilium company, now part of Cisco). It provides eBPF-based security observability and runtime enforcement with tight Kubernetes integration.

What Tetragon does well: Very low performance overhead due to efficient in-kernel filtering. Kernel-level enforcement can actually block threats, not just detect them. Native Kubernetes awareness—events include pod, namespace, and label context automatically. TracingPolicy CRDs enable declarative security policies that fit GitOps workflows.

Where Tetragon falls short: Focused on container and process behavior—doesn’t see application-layer attacks like SQL injection or SSRF at the code level. No correlation with cloud events or Kubernetes control plane activity. Teams not already using Cilium face a learning curve—TracingPolicy CRDs require understanding Cilium’s data model, and troubleshooting policy failures means knowing how Cilium’s Hubble observability layer works. Enforcement policies require careful testing to avoid breaking applications.

Best for: Teams already heavily invested in the Cilium ecosystem who want to add runtime enforcement without adopting another vendor.

Tracee

Tracee, from Aqua Security, focuses on deep kernel tracing for security forensics and threat detection.

What Tracee does well: Comprehensive kernel event tracing—sees more low-level activity than most alternatives. Strong forensic capabilities for post-incident analysis. Signature and behavioral detection modes. Decent container and Kubernetes awareness.

Where Tracee falls short: Higher resource consumption than Tetragon or Falco due to tracing depth—teams report 2-4x the CPU overhead compared to Tetragon in high-volume environments. Primarily detection-focused—response capabilities are limited. Requires significant expertise to configure effectively; default configurations generate overwhelming event volume. No cross-layer correlation with cloud or application events.

Best for: Security researchers, forensics teams, and organizations where deep kernel visibility is worth the resource tradeoff.

KubeArmor

KubeArmor (CNCF Sandbox project) focuses on runtime protection through Linux Security Modules (LSM) integration, emphasizing workload hardening over threat detection.

What KubeArmor does well: Strong enforcement capabilities through LSM hooks (AppArmor, SELinux, BPF-LSM). Zero-trust workload hardening with least-privilege policies. Application-aware policies that understand workload context. Lightweight agent with minimal overhead.

Where KubeArmor falls short: More focused on enforcement than detection—better at blocking known bad behaviors than identifying new threats. Policy creation requires understanding your application’s legitimate behavior upfront, which means significant learning time per workload. No cross-layer correlation or attack story generation. Less mature than Falco for pure detection use cases.

Best for: Teams prioritizing workload hardening and least-privilege enforcement over broad threat detection.

Feature Comparison Table

Capability	Falco	Tetragon	Tracee	KubeArmor	ARMO
Detection	Rules-based	Policy-based	Signatures + behavioral	Basic	Behavioral baselines + rules
Response	Alert only	Block/enforce	Alert only	Block/enforce	Kill, stop, pause, quarantine
Layers Covered	Container syscalls	Container + process	Kernel events	Container + process	Cloud, K8s, container, app, code
Cross-Layer Correlation	No	No	No	No	Yes (attack stories)
Investigation Time	Manual correlation	Manual correlation	Manual correlation	Manual correlation	90%+ reduction
Performance	Low-moderate	Very low	Moderate-high	Low	Low (1-2.5% CPU)
Open Source	Yes (CNCF Graduated)	Yes	Yes	Yes (CNCF Sandbox)	Yes (Kubescape – CNCF)
App-Layer Attacks	Limited	No	Limited	No	Yes (SQLi, SSRF, etc.)
Enterprise Features	Via Sysdig	Via Isovalent	Via Aqua	Via AccuKnox	Native platform

How to Choose: Decision Framework

Your current pain points determine which category of tool you need. Here’s how to match problems to solutions.

If You Need Detection Only

Start with Falco. It’s the most mature open-source option with the largest rule library. Good for learning what runtime visibility looks like and identifying which threats matter most in your environment. Plan to invest time in rule tuning—default rules generate significant noise, and budget 2-4 weeks for initial customization.

Consider Tracee if you need deeper kernel visibility for forensics or incident response. Higher overhead but more comprehensive tracing.

If You Need Detection + Enforcement

Choose Tetragon if you’re already in the Cilium ecosystem. TracingPolicy CRDs provide declarative enforcement that fits GitOps workflows. Very low overhead makes it safe for performance-sensitive workloads.

Choose KubeArmor if workload hardening is your primary goal. LSM integration provides strong enforcement, and application-aware policies understand workload context better than generic rules.

If You’re Drowning in Siloed Alerts

This is where ARMO CADR changes the equation. If your team spends more time correlating alerts than responding to threats, single-layer tools just add to the problem. ARMO connects events across cloud, Kubernetes, container, and application layers automatically—turning scattered signals into coherent attack stories.

The practical impact: investigations that took hours now take minutes. Your analysts see the full attack chain, not fragments they have to piece together manually. Learn more about how runtime context transforms security investigations.

If Investigation Time Is Your Bottleneck

Attack story generation directly addresses this. ARMO’s LLM-powered correlation doesn’t just connect events—it builds a timeline that shows how the attack progressed, which assets were affected, and what needs to happen to contain it.

Teams using ARMO report 90%+ reduction in investigation and triage time. That’s the difference between containing a breach in minutes versus spending hours reconstructing what happened.

If Performance Is Critical

Security tools that degrade application performance don’t survive in production. Compare overhead carefully:

• Tetragon: Very low—aggressive in-kernel filtering keeps overhead minimal
• Falco: Low to moderate—depends on rule complexity and event volume
• ARMO: Low—1-2.5% CPU, 1% memory despite broader coverage
• Tracee: Moderate to high—deep tracing has cost; 2-4x Tetragon in high-volume environments

ARMO’s lean architecture is notable given that it monitors more layers than the alternatives. In-kernel filtering ensures only security-relevant events leave kernel space.

If You’re Replacing an Existing Cloud Security Platform

Teams moving off Sysdig, Aqua, or Palo Alto Prisma often cite two frustrations: tools that started as CSPM or container scanning and bolted on runtime later, and pricing that scales painfully with cluster growth.

If runtime-first architecture and full-stack correlation matter more to you than checkbox compliance features, ARMO is built for that use case. If you need a mature CSPM with some runtime capabilities added, staying with your current vendor may make sense. See how ARMO compares to alternatives in the threat detection space.

Attack Story in Action: A Real-World Scenario

Let’s walk through how the same attack looks in siloed tools versus full-stack correlation.

The attack: An attacker exploits a SQL injection vulnerability in a web application, escapes the container via CVE-2024-21626 (the runc vulnerability that affected containerd), moves laterally through Kubernetes, and exfiltrates data via cloud APIs.

With siloed tools, your team sees:

• Application WAF: “SQL injection attempt blocked — UNION SELECT pattern” (severity: medium, 1 of 847 alerts today)
• Container runtime: “Unexpected process spawned in payments-api container” (severity: high)
• Kubernetes audit: “Service account payments-api accessed secrets in customer-data namespace” (severity: medium)
• Cloud monitoring: “Unusual S3 GetObject volume from EKS workload” (severity: low)

Four alerts. Four dashboards. Four different analysts might see them. None of them obviously connects to the others. The SQL injection alert alone doesn’t seem critical—you get hundreds of similar attempts. The S3 alert is low severity. By the time someone correlates all four signals, the attacker has already exfiltrated data.

With ARMO CADR, your team sees:

One attack story showing the complete chain: SQL injection in the payments-api service (13:42:07) → container escape via CVE-2024-21626 (13:42:23) → lateral movement using compromised service account (13:43:01) → data access in customer-data S3 bucket (13:44:15). The timeline shows exactly when each step happened, which function calls and syscalls were involved, and which cloud APIs were accessed.

Your analyst doesn’t need to reconstruct anything. They see the attack, understand the scope, and can take response actions immediately—block the source, quarantine the compromised workload, revoke the stolen credentials. For a similar real-world example, see how ARMO detected the Kong Ingress Controller supply chain attack.

This is the difference full-stack correlation makes. Not more detection. Better understanding.

Choosing the Right eBPF Security Tool

eBPF has transformed what’s possible for runtime security. Kernel-level visibility, low overhead, container-native awareness—these aren’t aspirational features anymore, they’re baseline expectations.

But eBPF is just the foundation. What you build on it determines whether you get useful security or just more alerts.

If you’re starting from zero: Falco provides solid detection with low barrier to entry. You’ll learn what runtime visibility looks like and identify where you need to go next. Budget 2-4 weeks for rule tuning.

If you need enforcement: Tetragon and KubeArmor add the ability to actually block threats, not just detect them. Choose based on your existing ecosystem—Tetragon for Cilium users, KubeArmor for LSM-based hardening.

If siloed alerts are your problem: You need full-stack correlation. ARMO CADR connects signals across cloud, Kubernetes, container, and application layers into complete attack stories. Investigations that took hours drop to minutes. Scattered alerts become actionable intelligence.

The best eBPF security solution isn’t the one that detects the most—it’s the one that helps your team respond the fastest.

See what full-stack correlation looks like for your environment. Book a demo of ARMO Platform to see how attack stories transform investigation from hours to minutes.

Frequently Asked Questions

What is the best eBPF tool for Kubernetes security?

For detection-only, Falco. For detection plus enforcement, Tetragon or KubeArmor. For full-stack correlation across cloud, Kubernetes, container, and application layers, ARMO CADR. The right choice depends on whether you need basic visibility, policy enforcement, or complete attack story generation.

How does eBPF-based security compare to traditional agents?

Traditional agents run in user space and compete with applications for resources. eBPF programs run in kernel space with minimal overhead and filter events before they leave the kernel. This makes eBPF-based tools better suited for container environments where performance and resource efficiency matter.

Can eBPF security tools detect zero-day attacks?

Rule-based tools like Falco detect known patterns only. Behavioral tools like ARMO detect anomalies based on deviation from established baselines—identifying zero-day exploits when applications behave in ways they never have before, even without a CVE or signature to match.

What’s the difference between detection and response in eBPF security?

Detection identifies threats and generates alerts. Response takes action—killing processes, blocking network connections, quarantining workloads. Falco and Tracee are detection-only. Tetragon, KubeArmor, and ARMO include response capabilities.

How does ARMO’s attack story generation reduce investigation time by 90%?

ARMO collects telemetry from cloud APIs, Kubernetes control plane, container runtime, and application code simultaneously. When related events occur, ARMO’s correlation engine links them based on causal relationships—which process spawned which syscall, which syscall led to which network connection. An LLM generates a human-readable attack timeline showing exactly how the attack progressed, eliminating manual correlation work.

Is open-source eBPF security good enough for production?

Open-source tools like Falco, Tetragon, and Kubescape are production-ready and used by thousands of organizations. The question is whether detection-only tools meet your needs or whether you need cross-layer correlation, response automation, and enterprise support that commercial platforms provide. See our guide to open-source Kubernetes security tools for detailed comparisons.