Runtime security gaps in cloud-native environments are widening

At a glance

What this is: A Frost & Sullivan-backed report, covered by Oligo Security, argues that cloud-native security is increasingly constrained by a runtime visibility gap rather than by a lack of static findings.

Why it matters: IAM, NHI, and SecOps teams need runtime-aware controls because cloud-specific attack paths can emerge only when workloads are live, making pre-execution posture checks insufficient on their own.

Context

Cloud-native environments change faster than periodic scanning cycles. Containers, Kubernetes, serverless functions, and multi-cloud deployments can spin up and disappear before traditional posture tools finish checking them, which leaves a gap between configuration review and live execution.

For identity and access programmes, that gap matters because the attack chain often depends on what a workload, API, or service account can do at runtime, not just what it is permitted to do on paper. Static visibility can miss the moment when exposure becomes exploitable.

Oligo Security’s source material frames this as a runtime problem for cloud-native operations, and that is the right way to read it. The security question is whether teams can observe and stop active behaviour inside production, not whether they can catalogue theoretical weakness beforehand.

Key questions

Q: What breaks when cloud native security relies only on static scanning?

A: Static scanning misses the conditions that emerge after deployment. In cloud native and AI environments, threats often appear in runtime behavior, ephemeral infrastructure, and cross service interactions. If teams stop at pre deployment checks, they can overlook concealment techniques, abuse of live APIs, and data movement that only becomes visible once workloads are active.

Q: Why do runtime controls matter more for cloud-native workloads than snapshot tools?

A: Runtime controls matter because cloud-native risk is created by live execution, not just by the presence of a misconfiguration. A workload can look safe in a snapshot and still be exploitable once it is running, handling traffic, or chaining into other services. Runtime context tells teams whether a weakness is active enough to require action.

Q: How should security teams prioritise application security findings in cloud environments?

A: Security teams should prioritise application findings by combining severity with exposure, reachability, ownership, and business impact. A medium issue on an internet-facing production service can be more urgent than a critical issue in dead code. ASPM helps by correlating scanner output with runtime context so remediation reflects actual risk.

Q: What is the difference between posture checks and runtime enforcement in cloud security?

A: Posture checks tell you whether a system is configured according to policy at a point in time. Runtime enforcement decides whether an action is allowed while the workload is actually executing. Both matter, but they solve different problems. Posture management helps find misconfiguration. Runtime enforcement stops malicious or unexpected behavior before it can move, escalate, or affect data.

Technical breakdown

Why static scanning misses live cloud-native risk

Static scanners and posture tools assess code, images, or infrastructure snapshots. That model breaks down when workloads are ephemeral, because the security state can change between checks. In containerised and serverless environments, the relevant question is not whether an image or function is vulnerable in theory, but whether the vulnerable component actually loads, executes, or reaches a sensitive path in production. Runtime security closes that gap by watching live execution and correlating behaviour with exploitability. This matters because the same misconfiguration can be harmless in one context and exploitable in another once the workload is running.

Practical implication: move from snapshot-based assurance to runtime checks for workloads that can appear and disappear faster than your scan cadence.

How cloud-specific attack paths bypass posture-first models

Cloud-native attack paths often blend identity misuse, exposed APIs, and lateral movement inside orchestration layers such as Kubernetes. A posture-only model may flag the presence of a weakness, but it does not always show whether the path is reachable from the current execution state. That is why application layer exploits, exposed interfaces, and entitlement mistakes become so important at runtime. The issue is not merely detection volume. It is that many findings never become real attack opportunities, while the ones that do can unfold faster than human review cycles can interpret them.

Practical implication: prioritise controls that evaluate exploitability in context, especially where cloud identities, APIs, and orchestration traffic intersect.

Why runtime context changes alert quality

Runtime security reduces the gap between a theoretical issue and an actual threat by adding execution context. If a function never loads, an alert about it matters less than one tied to a live process handling production traffic. If an exploit attempt is blocked inside the application, the result is different again, because the tool is observing and acting on behaviour rather than only recording a condition. That distinction is central to cloud-native security operations. It changes triage from enumerating possible weaknesses to deciding which weaknesses are being exercised right now.

Practical implication: tune detection workflows around live execution evidence so SecOps can separate actionable events from dormant findings.

Threat narrative

Attacker objective: The attacker wants to turn a live cloud-native exposure into active execution, lateral movement, and operational impact before static tools can create a usable response path.

1. Entry begins when attackers exploit cloud-specific issues such as exposed APIs, IAM missteps, or application-layer weaknesses in live services.
2. Escalation follows when the adversary uses runtime access to move through Kubernetes, workloads, or adjacent cloud components that were not visible in static checks.
3. Impact occurs when the intrusion chain reaches production execution, where the attacker can act faster than scan-based controls can detect or prioritise the abuse.

Breaches seen in the wild

• Massive Docker Hub Secrets Leak: 10,000+ Docker Hub container images expose hardcoded secrets and authentication keys.
• Secrets in Docker Hub images (RWTH Aachen study): A 2023 RWTH Aachen study found secrets in 8.5% of container images, and 275,269 internet hosts still using the leaked private keys.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Runtime visibility is now a governance boundary, not just a detection layer. Cloud-native programmes cannot treat pre-deployment checks as sufficient proof of control when workloads are short-lived and execution is continuous. The decisive question is whether the organisation can see what is actually running, not merely what was approved. Practitioners should treat runtime observability as a core identity and workload governance control.

Cloud-native security is shifting from finding weaknesses to measuring exploitability. Static findings are increasingly noisy because they do not tell teams whether a vulnerable component is live, reachable, or chained to a meaningful business process. This changes how risk gets prioritised across SecOps, AppSec, and cloud teams. The practical conclusion is that exploitability context now determines which findings deserve action first.

Identity, API, and workload exposure now converge at runtime. Cloud missteps rarely remain isolated to one control family because a live workload can combine entitlement mistakes, exposed interfaces, and orchestration reachability into a single attack path. That makes runtime the point where IAM and cloud security stop being separate conversations. Teams need to govern execution, not just configuration.

Runtime gap is the right name for the new control deficit. The article’s substance is not that organisations have too little scanning, but that scanning happens in the wrong temporal layer for modern cloud-native execution. That gap is where exploitation becomes real. Security leaders should frame investment around closing the runtime gap rather than expanding checklist coverage.

For cloud-native programmes, false comfort is the real failure mode. Posture tools can create confidence that an environment is covered while missing the moment a workload becomes exploitable in production. This is especially relevant where container churn and serverless execution compress the window for manual review. Practitioners should align operating models to live behaviour, because that is where attacks now land.

From our research library:

• Kubernetes production adoption has surged from 66% in CNCF's 2023 survey to 82% in 2025.

What this signals

Runtime gap: cloud-native teams need to treat live execution as a distinct governance layer because ephemeral workloads can outpace scan-based assurance. The operating model has to recognise that configuration review and production behaviour are not the same control surface.

The practical shift is toward exploitability-first prioritisation. When a finding only matters once a function loads or a process executes, security teams need workflow hooks that separate dormant issues from live attack paths before noise consumes SecOps capacity.

For practitioners

• Prioritise runtime detection for ephemeral workloads Map container, serverless, and Kubernetes assets to runtime controls first when their lifespan is shorter than your scan-and-review cycle.
• Correlate identity and workload signals during execution Join cloud entitlement events, API activity, and workload execution context so investigators can see when a live process becomes exploitable.
• Reduce noise by triaging only live exploit paths Separate dormant findings from issues that actually load, execute, or touch production data so engineering time goes to active exposure.
• Embed runtime alerts into response workflows Route runtime detections into SIEM, SOAR, and engineering queues so containment happens inside the same operational path used for cloud incidents.

Key takeaways

• Cloud-native environments create a mismatch between static assurance and live execution, and that mismatch is where modern risk concentrates.
• The most useful signal is whether a weakness is actually running in production, because that determines whether it is exploitable now or merely observable on paper.
• Security teams should re-centre runtime visibility, exploitability context, and incident workflows around live workloads rather than scan results alone.

Key terms

• Runtime Security: Runtime security is the practice of detecting and constraining malicious behavior while software is executing. It focuses on live workload activity, not just code quality or pre-deployment checks, so teams can contain abuse after a system is already running.
• Cloud detection and response: Cloud detection and response is the practice of finding suspicious activity in cloud environments and acting on it quickly. It combines telemetry from cloud control planes, workloads, identities, and network paths to detect misuse, then triggers investigation, containment, and remediation across accounts, regions, and services.
• Application Detection And Response: Application Detection and Response is a runtime control focused on what applications do after they start running. It helps security teams identify exploit attempts, malicious behaviour, and active abuse in the application layer, where static tools often have limited visibility.
• Exploitability context: Exploitability context is the evidence used to decide whether a vulnerability matters in a specific environment. It includes reachability, code path exposure, compensating controls, and product-specific advisories, and it turns raw scan data into a decision that can be defended.

Deepen your knowledge

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