Ephemeral Containers, CNAPP Blind Spots, and the Case for Real-Time Cloud Security

Introduction

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Modern enterprise SaaS platforms, particularly in the United States, increasingly rely on ephemeral containerized workloads orchestrated through Kubernetes. This architectural trend introduces fundamental security visibility challenges for legacy Cloud Native Application Protection Platform (CNAPP) solutions. Specifically, the prevalent reliance on snapshot-based scanning approaches leaves material blind spots in runtime coverage. These gaps, in turn, elevate systemic risk and create exploitable opportunities for attackers.

This research outlines a data-driven argument that event-driven, real-time CNAPP capabilities are becoming essential for maintaining accurate security posture in these environments. Particular emphasis is placed on sectors with stringent regulatory and financial risk profiles, including financial services, healthcare, and critical SaaS infrastructure.

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Container Deployment Dynamics in Modern Kubernetes Environments

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The operational dynamics of containerized environments are defined by rapid deployment cycles, high workload churn, and short container lifespans. These factors directly undermine the effectiveness of periodic security scanning.

Empirical data from the CNCF and Sysdig confirm the following trends:

1. A significant proportion of organizations deploy containers multiple times per day. More than 30% of respondents report daily or more frequent deployments, driven by CI/CD automation and GitOps practices. This high velocity means new container images and pods are constantly spun up across development, staging, and production.
2. More than 72% of containers exhibit lifespans shorter than five minutes. The median lifespan distribution skews heavily toward ephemeral execution, with containers commonly spun up for short-lived batch jobs, autoscaling events, and progressive delivery tests. Only a minority of containers run for days or longer.

For example, progressive delivery techniques (blue/green or canary releases) create temporary new pods for testing, and serverless container platforms (e.g. AWS Fargate, Google Cloud Run) spin up containers on-demand and kill them when done.

1. Container adoption scales with business growth. The average number of containers per organization exceeds 2,300 in 2024, a figure that has doubled in recent years.

Case studies like Netflix or fintech trading platforms show container deployment volume roughly correlates with usage scale — more microservices and more deployments as the business grows.

1. Deployment frequency is influenced by modern tooling, including ArgoCD, Flux, Helm, and Terraform. These frameworks enable declarative infrastructure and continuous delivery patterns that result in frequent and automated container churn.

Snapshot-based security scans struggle in this environment. When containers come and go within minutes, a scan that happens once a day (or even once a shift) will miss the majority of ephemeral containers entirely. High deployment velocity means new code (and potential new vulnerabilities) is constantly entering production. Traditional weekly or daily assessment cannot keep up with dozens of deployments a day. In short, modern SaaS/Kubernetes ops create an incredibly fluid attack surface that demands equally continuous monitoring.

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Vulnerability Dynamics in Container Images

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Container images exhibit substantial vulnerability density across base layers and application dependencies. The aggregate attack surface introduced by container sprawl is non-trivial.

Quantitative findings include:

1. A typical container image incorporates more than 600 known vulnerabilities on average, across approximately 389 component packages. This “vulnerability pile-up” spans all layers — from base OS (often containing outdated libs) to application dependencies and even unused packages bundled in the image.
2. In production environments, approximately 87% of container images contain at least one high or critical vulnerability. Many organizations knowingly deploy images with residual CVEs due to operational tradeoffs and the complexity of dependency management.
3. New critical vulnerabilities are introduced into public container images at a rapid pace. The flow of new vulnerabilities is accelerating. 2024 is on track to set a record for CVE disclosures — by September 2024 over 28,000 new vulnerabilities had been published, surpassing the prior year.

Public image registries (Docker Hub, Artifact Hub) constantly see newly discovered vulnerabilities in popular images — often multiple critical CVEs each month that maintainers scramble to patch. This means dev teams can unknowingly pull images that have become vulnerable overnight.

1. Median patch latency remains a concern. While 66% of organizations report patching critical open-source vulnerabilities within one day in staging environments, promotion to production is often delayed. More than 70% of container vulnerabilities with available fixes remain unpatched in running environments. This gap between “fix available” vs “fix deployed to prod” leaves windows of exposure.
2. Image sprawl contributes to persistent visibility gaps. Organizations frequently operate numerous variants of base images and application containers, which exacerbates monitoring complexity and introduces untracked risk across clusters.

The complexity of microservices (each with its own image) means security teams struggle to monitor all image versions in use. Image sprawl multiplies the chance that some services run an outdated, vulnerable image unbeknownst to DevSecOps.

Containers bring a huge vulnerability management challenge. High CVE density means virtually every image has issues that need prioritization. New critical CVEs (e.g. Log4Shell) can be introduced into many images faster than traditional scanning cycles detect them. If snapshot scans are infrequent, an image could go from “clean” to “critical CVE present” the day after a scan and run in prod for days completely unscanned for that new CVE. Moreover, any gaps in patching or visibility (e.g. a forgotten container running an old image) become easy targets for attackers. A one-time scan in CI or registry isn’t enough — continuous monitoring of what’s actually running (and whether it’s vulnerable) is needed to catch the inevitable stragglers and newly disclosed bugs.

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Gaps in CNAPP and DevSecOps Visibility

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Snapshot-based CNAPP architectures systematically fail to provide complete visibility into ephemeral and rapidly evolving Kubernetes environments.

Specific limitations include:

1. The majority of ephemeral containers evade detection entirely. Given that periodic scans are conducted at best on a 12 to 24-hour interval, containers with sub-hour or sub-minute lifespans are excluded from the observable dataset. They essentially run unmonitored by security tooling. Even if scans are more frequent (say every few hours), anything living minutes or seconds still evades capture.
2. Latency between vulnerability disclosure and detection remains significant. Critical CVEs incorporated into new images or builds may run in production for hours or days before snapshot tools surface their presence.

If a critical CVE is published today and a dev unknowingly builds it into an image, a typical agentless CNAPP might not flag it until the next daily scan — up to 24 hours later . During that window, the vulnerable container could be running in production exposed. In contrast, an integrated real-time agent could detect the vulnerable package on container start or when the CVE becomes known (some agent-based scanners can fetch updates and scan running containers within minutes).

1. In large K8s clusters, it is impractical to cover 100% of running pods with traditional agents, and large Kubernetes clusters routinely exhibit incomplete monitoring coverage. Studies indicate that fewer than 50% of cloud assets are protected by agent-based or runtime-aware solutions in practice. All these factors mean a percentage of running cloud resources at any time are unseen by security’s “radar.”
2. When an ephemeral container is compromised or crashes, teams often lack data to investigate. Containers’ short lifespan makes troubleshooting difficult — logs may be gone with the container, and if no agent was watching, there is no record of what happened. SOC analysts struggle with incidents in ephemeral infrastructure because traditional forensics assume the host/instance persists. >

Today, many incident response playbooks are incomplete for Kubernetes — e.g., capturing memory or process info from a pod that lived 2 minutes is nearly impossible after the fact. Without real-time capture or continuous logging, security teams may only learn that “something happened” (e.g. an anomaly or crash) with no detailed evidence. This is a huge blind spot in investigations and compliance reporting.

1. Most first-gen CNAPP platforms focused on posture (CSPM, config, vulnerabilities) more than live activity. This means their risk scoring can be skewed — e.g., treating all running CVEs as equal risk, when in reality a vulnerability actively being exploited or a container behaving strangely should be ranked higher.

Current CNAPP solutions (especially agentless, snapshot-based ones) leave significant visibility gaps in ephemeral, real-time activity. Many ephemeral pods bypass scheduled scans entirely, and even persistent resources may not be continuously monitored. This undermines security posture: you can’t secure what you don’t see. The latency of periodic scanning creates a dangerous window between a vulnerability emerging and being detected. And when incidents occur in ephemeral environments, lacking runtime data cripples response. In summary, relying on snapshots and periodic checks in a world of ephemeral, API-driven infrastructure causes many frames to be missed. This gap is precisely where attackers have an opening, as we explore next.

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Exploit Dynamics and Business Impact

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These visibility gaps are not theoretical. Adversaries actively exploit them, and the resulting financial and regulatory impacts are material. In today’s threat landscape, once a new critical CVE is announced, attackers often jump on it within hours. Here we translate how missing ephemeral/runtime visibility turns into tangible risks, and the potential financial impact by sector:

1. During Log4Shell (Dec 2021), scanning and exploitation began within hours of disclosure. Recorded Future observed attackers (many via Tor) aggressively searching for vulnerable servers the same day the vulnerability became public. Imperva recorded 1.4 million exploit attempts in just a few days. In fact, Cloudflare saw some Log4j exploit activity even before public disclosure (suggesting some attackers had early knowledge).

This demonstrates that the gap between vulnerability release and first attack can be effectively zero. If your containers aren’t patched or your security tooling doesn’t detect the issue in real-time, you likely have compromised containers before the next daily scan.

1. Attack chains increasingly leverage ephemeral container infrastructure. Kubernetes makes it easy for an adversary who gains initial access (say via a vulnerable app container) to launch new containers or jobs that serve as malicious tools — and then vanish. For instance, crypto-mining campaigns (TeamTNT, Kinsing malware, etc.) often run a malicious container for a short burst to mine cryptocurrency or pivot, then self-destruct to avoid detection.
   • Similarly, a compromised CI pipeline could briefly spin up a container that exfiltrates data and then terminates. Container orchestration can be abused to “live off the land”: using the infrastructure to create short-lived attack instances (privileged pods, debug containers) that perform recon or set up backdoors. These tactics map to MITRE ATT&CK techniques:
     • Initial Access: Exploiting a public-facing containerized app (MITRE T1190) or injecting a poisoned image in the supply chain.
     • Execution: Deploying a new malicious container in the cluster (MITRE T1610) or running commands via a kubelet API.
     • Persistence: Creating a cronjob or daemonset that keeps spawning pods, or abusing container auto-scaling to persist malicious code.
     • Lateral Movement: Using an ephemeral container with host mount to access other services (e.g., intruding the underlying node to move into other pods).
2. Container-related cloud breaches are prevalent. Estimates suggest that 23% of cloud security incidents involve container misconfigurations or vulnerabilities, with significant portions of lateral movement and privilege escalation attacks originating in containerized environments. Notably, a Cloud Native Computing Foundation survey found 52% of respondents use containers to run most of their applications in prod, so it stands to reason many breaches involve containers simply because that’s the infrastructure. Attackers target what’s available and ephemeral, unscanned containers are low-hanging fruit if they can find one with a known CVE or weak config.
3. Regulatory exposure is substantial in high-compliance sectors:
   • Fintech platforms risk PCI DSS violations and FFIEC scrutiny for undetected container-based vulnerabilities. Breach costs in financial services average $6 million per incident.
   • Healthcare organizations face HIPAA and HITRUST penalties. The healthcare sector exhibits the highest breach costs of any industry, with averages approaching $10 million.
   • SaaS infrastructure providers face material revenue and contractual risks. Breach-related costs for cloud-native SaaS platforms routinely exceed $5 million when accounting for customer churn, SLA penalties, and reputational damage.
4. The financial opportunity cost of leaving ephemeral workloads unmonitored can exceed 5 to 10% of annual revenue for affected organizations.

The lack of real-time container visibility directly translates to attacker advantages and huge potential losses. Attackers know organizations often have holes in their container monitoring — and they exploit them to gain quick wins (crypto-mining, data theft, lateral movement). The MITRE ATT&CK framework now includes container-specific techniques because this is a recognized attack surface. For high-risk industries, these blind spots also mean non-compliance and legal exposure (violating PCI, HIPAA, etc., due to not detecting incidents in time). The financial impact of a breach stemming from an “invisible” container can easily hit eight figures, especially when considering fines and lost customers.

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Market Implications for CNAPP Evolution

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The growing recognition of these issues is driving a shift in the CNAPP market and creating an opportunity for next-generation solutions that can truly cover ephemeral, real-time risks:

1. A significant proportion of existing CNAPP deployments rely primarily on snapshot-based, agentless scanning. These solutions gained popularity for ease of deployment (no agents) and broad coverage of cloud configs and workloads by reading cloud APIs and snapshots of VMs/containers. As a result, a large portion of CNAPP adoption to date is this “agentless” approach that excels at posture management but inherently lacks real-time runtime telemetry. These tools remain effective for cloud configuration posture management but are inherently insufficient for container runtime security.
2. Customer demand for runtime coverage is accelerating, particularly in high-velocity, regulated sectors.
   • High-transaction Fintech: Banks, payment processors, crypto exchanges and other fintech operate highly dynamic infrastructure (often microservices handling real-time transactions) and are prime targets for attack. They require granular visibility into every container handling sensitive data or money movement.
   • Regulated Healthcare: Hospitals, healthtech SaaS, and biotech running cloud-native apps have no tolerance for undetected incidents due to patient safety and strict laws. Healthcare DevOps teams might have slower change control but are now embracing cloud and need to ensure ephemeral data processing jobs or analysis pipelines aren’t a backdoor.
   • SaaS Infrastructure Providers: Companies offering cloud software or infrastructure to enterprises (including B2B SaaS, cloud platform services, etc.) often run at massive scale and serve as critical infrastructure for others. They face pressure from their clients to maintain strong security (often contractually via SOC 2, ISO, etc.). This drives them to seek out advanced solutions. A multi-tenant SaaS cannot afford an attack on one customer environment to pivot through the platform, thus they need real-time threat detection in their container orchestration.

Ideal Architecture to Close Gaps: The consensus emerging is that a hybrid agent-based approach is needed for true real-time security. This often means:

1. The reference architecture for modern CNAPP must incorporate agent-based or kernel-level telemetry. eBPF-based agents and node-level instrumentation provide efficient mechanisms for capturing runtime signals without excessive operational overhead. eBPF (extended Berkeley Packet Filter) technology is attractive because it can introspect container activity with minimal overhead and without requiring code changes in the containers.
2. Another model is using sidecars (dedicated security containers injected into each pod) or leveraging a service mesh to monitor traffic. However, sidecars per pod can add overhead and complexity, and ephemeral pods might not always get the sidecar if not controlled. Service mesh telemetry gives good network visibility but not process-level actions. Thus, the agent on the node (covering all pods) is generally more efficient. That said, combining multiple methods is ideal, e.g. a CNAPP might use agentless scanning for cloud config and IaC, node-level agents for runtime container monitoring, and admission controllers in K8s to enforce policy on new pods.
3. Continuous, Event-Driven Scanning: The architecture should treat security scanning as an event-driven continuous process, not a once-a-day batch. For instance, whenever a new container image is deployed, the system should immediately scan it (or at least fetch a recent scan result) and also monitor its behavior live. Integration with CI/CD is key — scanning images at build time, but also rescanning or validating at deploy time. An ideal system might use “streaming” vulnerability feeds — e.g., if a new CVE is announced that affects running containers, the agent or platform should flag those containers within minutes, not wait for the next full scan.

Those that fail to evolve beyond snapshots will likely be sidelined as customers realize that “cloud-speed” environments require cloud-speed security.

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Conclusion

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The evidence is unambiguous. The operational dynamics of modern Kubernetes-centric SaaS environments are incompatible with purely snapshot-based security scanning. Container lifecycles have shortened to the point where periodic inspection is functionally blind to the majority of ephemeral workloads. Vulnerabilities propagate through CI/CD pipelines faster than static tools can surface them. Attackers are exploiting these windows, with material financial and regulatory consequences across critical sectors.

As a result, event-driven, real-time CNAPP architectures are becoming essential for maintaining accurate risk posture in these environments. The transition from periodic to continuous security monitoring mirrors the transition from periodic to continuous delivery in software development. Security controls must operate at cloud-native velocity.

Vendors and enterprises that recognize and adapt to this shift will be better positioned to secure the next generation of cloud infrastructure. Those that do not will continue to operate with unacceptable blind spots, with predictable outcomes.

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Sources

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Primary Industry Data

1. CNCF Cloud Native Survey (2023/2024)

Annual global survey of cloud-native adoption trends, container usage, Kubernetes deployments, and CI/CD patterns.

2. Datadog Container Report (2023/2024)

Empirical data on container lifespan, deployment frequency, orchestration patterns, and Kubernetes usage across large-scale SaaS and enterprise environments.

3. Red Hat State of Kubernetes Security (2023/2024)

Industry survey on Kubernetes security practices, challenges in runtime visibility, patching trends, and DevSecOps adoption.

4. OWASP Top 10 Kubernetes (2022)

Community-driven reference for the most critical risks facing Kubernetes environments, including supply chain, runtime, and workload vulnerabilities.

5. MITRE ATT&CK Cloud Matrix

Comprehensive framework mapping cloud-native attack techniques, including those targeting containerized environments and ephemeral infrastructure.

6. IBM Cost of a Data Breach Report (2024)

Industry benchmark for average financial impact of data breaches across sectors including healthcare, financial services, and SaaS.

7. ABA Banking Journal / IBM Data (2024)

Specific cost data on financial services sector breach impacts, including regulatory penalties and brand damage.

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Vendor Research / Market Perspectives

8. Sysdig 2023 and 2024 Cloud-Native Security Reports

Detailed analysis of cloud-native security trends, including ephemeral container risks, runtime detection needs, and CNAPP adoption.

9. Unit 42 Cloud Threat Report (2025)

Recent data on cloud-native attacks, container exploit trends, and emerging threat vectors in Kubernetes environments.

10. SentinelOne (2025) on CNAPP vs CDR

Position paper on the need for continuous monitoring and the limitations of snapshot-based CNAPP approaches for ephemeral workloads.

11. Qualys CNAPP Whitepaper

Discussion of CNAPP architecture tradeoffs, frequency of scanning, and growing customer need for real-time insights.

12. NetRise Container Risk Study (2024)

Study showing an average of 604 vulnerabilities per container image across real-world environments.

13. LinkedIn Triam Security Post (2024)

Industry data point: 89% of production images contain vulnerabilities, with base image risks often unaddressed.

14. Recorded Future analysis of Log4Shell exploitation

Timeline study showing Log4Shell exploitation occurring within hours of public disclosure, underscoring the risk of undetected ephemeral workloads.

15. Gartner Market Guide for CNAPP (2024), via Sysdig

Summary of CNAPP market trends, runtime visibility gaps, and architectural guidance for next-generation solutions.

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Academic / Standards References

16. Snyk Open Source Security Report (2023)

Analysis of patch timelines, CI/CD pipeline practices, and the gap between staging and production vulnerability management.

17. Cloud Native Now / The New Stack

Survey data on container adoption, CI/CD tooling, and velocity of modern software delivery pipelines in Kubernetes.

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