Best Container Security Solutions for 2026: From Scanning to Runtime Protection

Key Insights

What’s the difference between container scanning and container security? Scanning finds vulnerabilities in images before deployment—it’s container auditing, not container security. Real security requires runtime visibility: seeing what processes execute, what network connections occur, and what files get accessed while containers run. Most teams have scanning covered. Most teams are blind at runtime.

Why do 90% of CVE alerts not matter? Static scanners flag every vulnerable library in an image, regardless of whether that code ever executes. A typical cluster scan returns 3,000+ CVEs. Runtime reachability analysis reveals most are in packages that never load into memory, code paths that never run, or are blocked by existing network policies. You might have 30-50 that warrant immediate action—but without runtime context, you can’t tell which ones.

What is CADR and why does it matter? Cloud Application Detection and Response connects signals across cloud APIs, Kubernetes control plane, container runtime, and application code into complete attack stories. Instead of three separate alerts from three tools sent to three teams, you see one incident timeline showing how an attacker got in, moved laterally, and what they accessed. It’s the difference between “suspicious process on node-7” and “SQL injection → container escape → credential theft → S3 exfiltration.”

Which tool type fits which problem? Alert fatigue and false positives → runtime-first platforms (ARMO, Upwind). Compliance and audit prep → policy engines (Anchore) or platforms with built-in frameworks (ARMO, Prisma). Shift-left and CI/CD integration → scanners (Snyk, Trivy). One vendor for everything → CNAPPs (Wiz, Prisma Cloud). Active threat detection → CADR and runtime security (ARMO, Sysdig, SentinelOne).

How does ARMO Platform compare to alternatives? ARMO is the only runtime-first platform built on an open-source foundation (Kubescape, 50K+ organizations). It reduces CVE noise by 90%+ through runtime reachability, generates complete attack stories across the full stack, and provides smart remediation that accounts for actual workload behavior. Performance overhead is 1-2.5% CPU and ~1% memory. Most CNAPPs are strong on posture management but weak on runtime detection—ARMO inverts that priority.

Introduction

Scanning containers isn’t container security—it’s container auditing.

Your scanner found 3,400 CVEs across your clusters last night. Your CISO wants a remediation plan by Friday. Your platform team is already pushing back because last month’s “critical” patches broke two production services. And you’re sitting there knowing that most of those 3,400 findings don’t actually matter—but you can’t prove which ones.

This is the state of container security for most teams in 2026. 87% of container images in production contain critical or high-severity vulnerabilities. Security vendors love that statistic. What they don’t mention: the majority of those vulnerabilities aren’t exploitable at runtime. The library is present but never loaded. The vulnerable function exists but never executes. The attack path requires network access your policies block.

The real problem isn’t finding vulnerabilities. You have plenty of tools for that. The problem is knowing which ones matter, detecting actual attacks when they happen, and fixing issues without breaking production.

This guide covers the full spectrum of container security solutions—from image scanners to runtime detection platforms. We’ll cut through the acronym soup (CNAPP, CSPM, CWPP, CADR), explain which tool types solve which problems, and help you make a decision that actually improves your security posture instead of just adding to your alert queue.

If you’re drowning in findings from tools that can’t distinguish real risk from theoretical risk, this is for you.

What Makes Container Security Different (And Why Most Tools Fail)

Traditional security tools were built for servers that lived for years and changed quarterly. Kubernetes is the opposite.

Containers are ephemeral—most live less than five minutes. Your attack surface isn’t a fixed set of IP addresses; it’s a constantly shifting constellation of pods, services, and network connections. By the time your weekly scan completes, the infrastructure it scanned no longer exists.

Kubernetes adds orchestration complexity on top. RBAC policies, service accounts, network policies, admission controllers—a vulnerability in your container image is one thing. That same vulnerability combined with an overprivileged service account, missing network segmentation, and a misconfigured RBAC role is something else entirely.

This is why tools built for traditional infrastructure fail in Kubernetes:

Endpoint agents don’t understand Kubernetes primitives. They see a process ID on a Linux host. They don’t know which pod, which namespace, which deployment, or which team owns that workload. When your EDR fires “suspicious process detected on node-7,” and node-7 runs 200 containers across 47 pods, you’re starting an investigation with almost no context.

Point-in-time scanners create false confidence. Your scan said “compliant” on Tuesday. By Friday, CI/CD pushed 47 configuration changes, autoscaling created 200+ new pods, and someone added a permissive network policy for “temporary” debugging. Monday’s audit asks for proof of continuous compliance—your Tuesday scan is useless.

Generic remediation breaks production. The scanner says “apply this fix.” But it doesn’t know that your application depends on that specific behavior, that the fix conflicts with your service mesh, or that applying it will cause a cascade of failures across dependent services.

Container security evolved into specialized categories to address these gaps. Understanding the categories helps you avoid buying tools that solve problems you don’t have.

The Container Security Landscape: Cutting Through the Acronym Soup

Vendors exploit category confusion. They’ll call anything a “CNAPP” because the term is broad enough to cover almost anything. Here’s how to cut through the marketing:

CNAPP (Cloud-Native Application Protection Platform) is the umbrella analyst term for platforms combining multiple capabilities—posture management, workload protection, vulnerability scanning, sometimes runtime detection. When a vendor says “CNAPP,” ask which specific capabilities they’re strong in. Breadth often means some capabilities are weaker than best-of-breed alternatives.

CSPM (Cloud Security Posture Management) focuses on configuration. It scans cloud accounts and Kubernetes clusters for misconfigurations—public buckets, overprivileged IAM roles, missing encryption. CSPM tells you what’s wrong with your setup. It doesn’t tell you what’s being attacked right now.

CWPP (Cloud Workload Protection Platform) focuses on workloads—containers, VMs, serverless. This includes vulnerability scanning, malware detection, sometimes basic runtime monitoring. More application-focused than CSPM.

Container Scanning analyzes images for known vulnerabilities, exposed secrets, and misconfigurations before deployment. This is shift-left security. Necessary, but it only sees what’s packaged—not what executes.

Runtime Security monitors actual behavior during execution. System calls, network connections, file access, process execution. This is where you detect active attacks, behavioral anomalies, and zero-days with no CVE signature.

CADR (Cloud Application Detection and Response) is the emerging category that connects signals across the entire stack—cloud APIs, Kubernetes control plane, container runtime, application code—into complete attack stories. Instead of siloed alerts, you see how an attack progressed from initial access to objective.

The key distinction: Scanning and posture management tell you what could be vulnerable. Runtime security tells you what is being exploited. Most organizations have the first covered. Most are blind to the second.

The 90% Problem: Why Your Scanner Creates Noise, Not Security

Here’s what happens in a typical enterprise environment:

Your static scanner runs against a production cluster with 847 container images. It returns 3,412 CVEs—289 critical, 1,247 high severity. Your security team now has a “critical vulnerability backlog” that would take months to remediate.

But watch what happens when you add runtime context:

2,890 CVEs are in packages that never load into memory. The library exists in the image but your application never imports it.

341 CVEs are in code paths that never execute. The vulnerable function exists but your application’s logic never calls it.

127 CVEs are blocked by existing network policies. The exploit requires inbound connections your segmentation prevents.

21 CVEs are in containers that aren’t even running—they’re in your registry but never deployed.

You’re left with 33 CVEs that are actually loaded, executed, and network-reachable. Those 33 warrant immediate attention. The other 3,379 are noise—and without runtime visibility, you’d be triaging them for months while actual risks go unaddressed.

This is the 90% problem. Static scanners flag everything because they have no visibility into what actually runs. They’re auditing your images, not securing your workloads.

Runtime-aware tools close this gap by observing actual process execution, memory loading, and network behavior. They can tell you: “This CVE is in a library that’s loaded, in a function that executes, in a container that’s exposed to the internet. Fix this first.”

What a Real Attack Looks Like (And What Different Tools See)

In March 2025, ARMO’s research team deployed a honeypot Kubernetes cluster with intentionally vulnerable Apache Druid workloads. Within days, they observed two distinct cryptojacking campaigns exploiting CVE-2021-25646. 

Here’s how the attack chain unfolded and what different tool types would have seen:

Initial access: Automated scanners discovered the exposed Druid service and sent exploit payloads via POST requests to the Druid indexer API.

What a scanner sees: Nothing. The attack is happening to a running workload. What runtime security sees: Suspicious HTTP payload hitting the application API.

Execution: The exploit executed arbitrary Java code, spawning a shell process inside the container.

What a scanner sees: Nothing. What runtime security sees: Container that normally runs Java suddenly spawning /bin/sh. Immediate anomaly detection based on behavioral baseline.

Payload delivery: The shell downloaded cryptomining malware from external command-and-control infrastructure.

What a scanner sees: Nothing. What runtime security sees: Unexpected outbound connection to unknown external IP. Curl/wget execution not in the application profile. File written to an unexpected location.

Impact: XMRig cryptominer executing, consuming cluster resources.

What a scanner sees: Nothing. What runtime security sees: Unknown process consuming abnormal CPU. Network connections to mining pools. Complete attack timeline from initial exploit to crypto execution.

With CADR specifically, all of these signals—the suspicious API payload, the shell spawn, the external download, the crypto execution—correlate into a single attack story with a timeline, showing exactly how the attacker got in and what they did. One incident ticket, not four separate alerts sent to different teams.

The lesson: The Druid vulnerability existed in the image. A scanner would have flagged it—along with dozens of other CVEs. But the scanner couldn’t tell you it was actually being exploited. Only runtime visibility caught the active attack.

Read more about this attack here

6 Capabilities That Separate Signal from Noise

Before evaluating specific tools, know what capabilities actually matter. These six separate tools that reduce your workload from tools that add to it.

1. Runtime Visibility: Can the tool see what’s happening inside running containers? This means monitoring system calls, network connections, file access, and process execution—ideally using eBPF for kernel-level visibility without sidecar overhead. Without runtime visibility, you’re triaging theoretical risks while missing actual attacks.

2. Risk Prioritization: Does it distinguish exploitable vulnerabilities from theoretical ones? Look for reachability analysis: is the vulnerable code loaded into memory, does it execute, is it network-accessible? Without prioritization, you’re triaging 3,000 CVEs manually.

3. Kubernetes-Native Design: Was this built for Kubernetes, or ported from VM/endpoint security? Native tools understand pods, Deployments, namespaces, service accounts, and RBAC. They map alerts to specific workloads and owners—not just container IDs.

4. Alert Quality: Context-rich alerts or raw CVE dumps? Good alerts tell you what happened, where, what the blast radius might be, and what to do about it. Bad alerts are log lines that require hours of investigation to understand.

5. Remediation That Doesn’t Break Production: Does it account for actual runtime behavior when recommending fixes? The best solutions observe what your application does—which network connections it makes, which system calls it uses—and recommend policies that won’t disrupt legitimate behavior.

6. Performance Overhead: What CPU and memory footprint does the agent consume? Production-acceptable is 1-3% CPU, minimal memory. Heavier agents affect application performance and create friction with platform teams.

The Solutions: Organized by What Problem They Actually Solve

Runtime Security and CADR: Detecting What Scanners Miss

Best for: Teams that need to detect actual threats—not just vulnerabilities—and understand that posture management alone creates false confidence.

ARMO

ARMO Platform takes a runtime-first approach built on Kubescape—an open-source Kubernetes security project used by 50,000+ organizations with 11,000+ GitHub stars and now a CNCF incubating project.

Where most tools focus on one layer, ARMO connects signals across cloud, cluster, container, and application code. The platform deploys eBPF sensors that watch container behavior at the kernel level—without sidecars. These sensors build behavioral baselines (Application Profile DNA) for each workload: which processes normally run, which files get accessed, which network connections occur.

Here’s what this looks like in practice:

Your cluster has 3,400 CVEs from static scanning. ARMO’s runtime reachability analysis shows 3,100 are in code that never loads. Another 250 are in paths that never execute. You’re left with 50 that are actually running and exposed. That’s your priority list—and you can explain exactly why to your CISO.

When something deviates from baseline—a container that never runs shells suddenly spawning bash, an unexpected outbound connection to an unknown IP—ARMO’s Cloud Application Detection and Response (CADR) engine flags it. But instead of isolated alerts, you get a complete attack story: “Suspicious API payload at 14:23 → shell spawn at 14:24 → external download at 14:25 → crypto process at 14:27.” One incident timeline instead of four separate alerts.

For remediation, ARMO generates network policies and seccomp profiles based on observed behavior—not theoretical best practices. If your application never makes outbound connections to port 443, the recommended policy blocks it. If your container never needs to spawn child processes, the seccomp profile prevents it. These recommendations account for what your workload actually does, so they don’t break production.

Performance overhead is 1-2.5% CPU and approximately 1% memory. Supports EKS, AKS, GKE, OpenShift, Rancher, and air-gapped environments.

For teams drowning in alerts from tools that can’t distinguish real risk from theoretical risk, ARMO is designed to surface what actually matters.

Sysdig

Sysdig was founded by the team that created Falco, the open-source runtime security project. Deep expertise in runtime detection, strong Kubernetes community presence. Their Prometheus integration appeals to teams already using Prometheus for monitoring.

The trade-off: while Sysdig has expanded beyond runtime, their posture management capabilities are less comprehensive than CSPM-focused alternatives. If runtime detection is your primary concern and you’re already in the Prometheus ecosystem, Sysdig is worth evaluating.

SentinelOne Singularity Cloud

SentinelOne Singularity Cloud brings AI-powered endpoint detection to cloud workloads with autonomous response capabilities—automated containment without human intervention.

The trade-off: autonomous response requires trust in the detection logic. False positives that trigger automatic containment can disrupt production. This is a significant risk in environments where availability matters.

Comprehensive CNAPPs: One Vendor for (Almost) Everything

Best for: Large enterprises standardized on a single security vendor, with budget to match and acceptance that some capabilities will be weaker than best-of-breed.

Wiz

Wiz pioneered agentless scanning—using cloud APIs to inventory environments without deploying agents. Fast to deploy, broad coverage, excellent attack path visualization. Wiz excels at CSPM and vulnerability management.

The trade-off is fundamental: agentless means no runtime visibility. Wiz can tell you a vulnerability exists. It can’t tell you if someone is exploiting it right now. For organizations that prioritize posture management over threat detection, this might be acceptable. For teams facing active threats, it’s a significant gap.

Prisma Cloud

Prisma Cloud is Palo Alto’s assembled platform (Twistlock acquisition plus multiple others)—CSPM, CWPP, code security, identity management in one console. The breadth appeals to organizations standardized on Palo Alto’s ecosystem.

The trade-off: breadth comes at the cost of depth. Individual modules are often less capable than focused alternatives. The complexity of managing this breadth creates operational overhead. Teams frequently report that Prisma tries to do everything but excels at nothing.

CrowdStrike Falcon Cloud Security

CrowdStrike Falcon Cloud Security brings endpoint detection heritage to cloud workloads. Strong threat intelligence, AI-powered detection, experience catching malicious behavior.

The trade-off: CrowdStrike’s DNA is endpoints, not Kubernetes. The approach can feel VM-centric to teams deeply invested in cloud-native tooling. If you’re already a CrowdStrike shop, it’s worth evaluating. If you’re not, there are more Kubernetes-native options.

Scanning and Shift-Left: Catching Issues Before Production

Best for: Teams focused on CI/CD integration and developer experience, or looking for scanning to complement runtime tools.

Snyk Container 

Snyk Container built its reputation on developer-friendly security. Integrates into IDEs, Git repos, and CI/CD pipelines—making security feel like part of development rather than a gate. Snyk’s strength is developer experience with clear remediation guidance and automated fix PRs.

The trade-off: Snyk focuses on finding vulnerabilities, not detecting runtime threats. You need complementary runtime visibility.

Trivy

Trivy (Aqua Security) is the most widely used open-source scanner—simple, fast, flexible. Scans images, repos, and Kubernetes manifests. Integrates with GitHub Actions, GitLab CI, Jenkins. Generates SBOMs in standard formats.

As the foundation of many security toolchains, Trivy pairs well with runtime tools that add behavioral context. Start here if you’re building your own stack.

Anchore

Anchore specializes in policy-driven image analysis—define rules like “block critical CVEs” or “only allow approved base images” that integrate with Kubernetes admission controllers.

For compliance-driven environments (SOC 2, HIPAA, PCI-DSS), the ability to prove policies are enforced automatically has audit value.

Decision Matrix: Matching Problems to Solutions

If your primary problem is…	Consider…	Why
Alert fatigue / false positives	ARMO, Upwind	Runtime context filters 90%+ of theoretical vulnerabilities
Compliance and audit prep	Anchore, ARMO	Policy enforcement and framework mappings (CIS, SOC 2, etc.)
Shift-left / developer experience	Snyk, Trivy	CI/CD integration without friction
Single vendor for everything	Wiz, Prisma Cloud	Breadth across CSPM, CWPP, scanning (with depth trade-offs)
Active threat detection	ARMO, Sysdig, SentinelOne	Runtime visibility and behavioral detection
Budget constraints	Kubescape + Falco + Trivy	Open-source foundation you can augment later
Kubernetes-native depth	ARMO, Sysdig	Built for K8s primitives, not ported from VM/endpoint

Questions to Ask Before You Buy

These questions expose capability gaps that vendor demos won’t reveal:

1. “Show me what percentage of our CVEs are actually exploitable at runtime.” Run a proof-of-concept against your actual workloads. If the vendor can’t demonstrate meaningful reduction through runtime context, their “prioritization” is just CVSS scores.

2. “Walk me through investigating an incident from alert to root cause.” Time the demo. If it requires jumping between multiple consoles, correlating timestamps manually, or “checking with another team,” multiply that by every incident you’ll investigate.

3. “What happens if we apply your recommended remediation?” Ask specifically about production impact. Do they test recommendations against actual runtime behavior, or are they generic best practices that might break your application?

4. “What’s the real performance overhead?” Vendor claims and reality differ. Benchmark during POC with production-representative workloads. Anything over 3% CPU will create friction with your platform team.

5. “How does this integrate with our existing workflow?” GitOps? Make sure it supports ArgoCD/Flux. PagerDuty? Verify the integration works. Slack? Check that alerts include actionable context, not just links to another console.

Moving from Scanning to Actual Security

Your scanner isn’t broken. It’s doing exactly what it was designed to do: audit images for known vulnerabilities.

The problem is that auditing isn’t security. Security means seeing what’s actually happening—which processes are running, which network connections are occurring, which files are being accessed. Security means detecting attacks as they unfold, not discovering them in next week’s scan. Security means fixing issues without breaking the applications your business depends on.

The best container security connects signals across your entire stack: cloud API events, Kubernetes audit logs, container system calls, application-layer behavior. When these signals correlate instead of silo, you get attack stories instead of alert queues. You understand how threats progress instead of guessing at connections between isolated events.

If you’re spending more time triaging alerts than responding to threats, the gap isn’t more scanning. It’s runtime context that tells you which alerts matter and detection capabilities that catch threats your scanner never anticipated.

Teams ready to move beyond alert fatigue can start with ARMO’s —runtime-first security that reduces thousands of theoretical vulnerabilities to the focused set that actually puts your applications at risk.

Frequently Asked Questions

What is the difference between container scanning and runtime security?

Container scanning examines images before deployment, comparing installed packages against CVE databases to identify known vulnerabilities. It’s an audit of what’s packaged inside your containers—necessary for basic security hygiene and compliance requirements.

Runtime security monitors what actually happens when containers execute: which processes run, which system calls occur, which network connections are made, which files are accessed. This is where you detect active attacks, behavioral anomalies, zero-day exploits with no CVE signature, and lateral movement between workloads.

The distinction matters because most vulnerabilities flagged by scanners aren’t exploitable in your specific environment. Runtime visibility reveals which vulnerabilities are actually reachable—and detects attacks that exploit vulnerabilities your scanner doesn’t even know about yet.

Most mature security programs use both: scanning in CI/CD to catch known-bad before deployment, runtime monitoring to detect threats that slip through and respond to active attacks.

How do you reduce false positives in container security?

False positives in container security typically come from static scanners that flag every vulnerable package regardless of whether it’s actually used. The solution is runtime reachability analysis—determining which vulnerabilities are in code that’s actually loaded, executed, and network-accessible.

eBPF-based sensors observe which libraries load into memory, which functions execute, and which network paths are open. A CVE in a package that never loads isn’t exploitable. A vulnerability in a code path that never runs can’t be attacked. This approach typically reduces actionable CVE counts by 90% or more.

ARMO’s runtime-based vulnerability management demonstrates this approach: see how it reduces CVE noise.

What is CADR and how is it different from traditional CNAPP?

CADR (Cloud Application Detection and Response) is an emerging category focused specifically on detecting and responding to threats with full-stack correlation. It connects signals from cloud APIs, Kubernetes control plane, container runtime, and application code into complete attack stories.

Traditional CNAPP (Cloud-Native Application Protection Platform) is a broader category that typically emphasizes posture management and vulnerability scanning. Many CNAPPs are strong on identifying misconfigurations and flagging CVEs but weak on detecting active threats.

The practical difference: CNAPP tells you what could be vulnerable. CADR tells you what is being attacked—and shows the complete attack chain from initial access to objective.

Learn more about CADR and how it transforms incident investigation.

Do I need agents for container security?

It depends on what visibility you need. Agentless approaches use cloud APIs to scan configurations without deploying anything to workloads—fast deployment, no overhead, but can’t see inside running containers. Agent-based approaches deploy sensors that observe workload behavior at the kernel level—runtime visibility, behavioral detection, but requires deployment.

Most mature security programs use both: agentless for posture management, agents for runtime detection. The key question is whether you’re trying to audit your security posture or detect active threats. Auditing can be agentless. Detection requires runtime visibility.

Modern eBPF-based agents minimize overhead while providing kernel-level visibility. Performance impact of 1-3% CPU is typically acceptable for production workloads.

How do I secure containers without breaking production?

The solution is runtime-aware remediation—observing actual workload behavior before recommending changes. Runtime sensors monitor which network connections your application makes, which system calls it uses, which files it accesses. Recommendations are then generated based on this observed behavior.

If your application never makes outbound connections to external IPs, a network policy blocking that traffic is safe to apply. If your container never spawns child processes, a seccomp profile preventing it won’t cause problems. This is fundamentally different from generic best-practice recommendations.

ARMO’s smart remediation takes this approach: see how runtime-aware remediation works.

What compliance frameworks matter for Kubernetes security?

CIS Kubernetes Benchmark is the most widely used standard—comprehensive controls covering cluster configuration, node security, policies, and secrets management. Most auditors expect CIS compliance as a baseline.

NSA/CISA Kubernetes Hardening Guide provides government-grade recommendations for regulated industries. SOC 2 focuses on security controls for SaaS and service providers. PCI-DSS applies to payment card handling. HIPAA covers healthcare organizations.

The challenge: Kubernetes changes constantly. Point-in-time compliance scans create false confidence. Look for tools with continuous compliance monitoring—violations detected as they happen, not at the next scheduled scan. ARMO includes 260+ Kubernetes-native controls mapped to these frameworks with continuous drift detection.

What’s the difference between CSPM and runtime security?

CSPM (Cloud Security Posture Management) scans configurations at a point in time. It answers: “Is our environment configured according to best practices?” CSPM finds misconfigurations—public buckets, overprivileged roles, missing encryption.

Runtime security monitors behavior continuously. It answers: “What is actually happening right now?” Runtime security detects threats—malicious processes, suspicious connections, unauthorized access.

Think of it this way: CSPM is like a home inspection checking if your locks are installed correctly. Runtime security is like a security camera watching for intruders. Both matter, but they solve different problems.

How do I evaluate container security tools for my organization?

Start by identifying your primary problem—this determines which tool category matters most. Alert fatigue? Prioritize runtime context. Compliance? Verify framework support. Active threats? Focus on detection and response. Developer friction? Emphasize CI/CD integration.

During proof-of-concept: test with production-representative workloads, measure actual performance overhead, verify integrations work with your existing tools, time incident investigation end-to-end, and ask your platform team if they’d accept the deployment model.

The best tool is the one your team will actually use—capabilities don’t matter if operational friction prevents adoption.