Runtime evidence is better when it shows a smaller set of actually executed components than the build inventory suggests. That difference tells you which vulnerabilities matter operationally and which are only present in dormant code. If your prioritisation changes after runtime validation, the static view was incomplete.
Why This Matters for Security Teams
Static scanning gives a broad inventory of what could be exposed, but runtime evidence shows what is actually present in the live environment. That difference matters because security teams rarely fail on code that never executes; they fail on components, libraries, and services that are reachable, active, and tied to real attack paths. A live execution view helps separate theoretical exposure from operational risk, which is exactly where prioritisation needs to improve.
This is especially important for release gating, vulnerability management, and third-party assurance. A package may appear in the build manifest, yet never load in the deployed service, or it may load only in a narrow feature path that is disabled by default. Runtime evidence reduces guesswork by showing the reachable attack surface, but it does not replace secure build controls or SCA coverage. It is stronger when used as a validation layer against the software supply chain baseline described in NIST Cybersecurity Framework 2.0.
In practice, many security teams discover the gap only after a high-severity finding survives multiple sprints because the static report looked worse than the running service.
How It Works in Practice
Runtime evidence is produced by observing what a service loads, calls, executes, or exposes while it is running in a production-like environment. That can come from process telemetry, container inspection, eBPF-based observation, service dependency tracing, or workload protection tooling. The goal is not to prove that static scanning is wrong, but to determine whether a vulnerability sits in an executable path, a dormant dependency, or an asset that is never actually deployed.
In practice, teams compare the static bill of materials or dependency inventory with runtime observations and then triage by execution relevance. A vulnerability in a transitive library is less urgent if the library never loads, while a flaw in an active runtime module deserves faster treatment. This is particularly useful for containerised services, ephemeral workloads, and release pipelines where build artefacts and deployed artefacts drift.
- Use static scanning to establish breadth of potential exposure.
- Use runtime evidence to confirm reachability and execution.
- Prioritise issues that affect active code paths, exposed ports, and invoked services.
- Track deltas between build-time inventory and production telemetry as a control signal.
The best operational model is a layered one: build-time controls catch known content early, while runtime evidence validates what attackers could actually touch. That approach aligns with asset and exposure management thinking in the NIST Cybersecurity Framework 2.0 and with the idea that detection should be based on observable behaviour, not only declared configuration. These controls tend to break down in highly dynamic serverless environments because execution paths are short-lived, telemetry can be incomplete, and attribution to a specific build artefact becomes harder.
Common Variations and Edge Cases
Tighter runtime validation often increases instrumentation overhead, requiring organisations to balance better prioritisation against observability cost and privacy constraints. That tradeoff becomes sharper in regulated environments, high-throughput platforms, and systems with heavy autoscaling. Current guidance suggests using runtime evidence as a decision aid, not as the only source of truth, because there is no universal standard for how much execution proof is enough.
Edge cases matter. A dormant component can still become relevant if a feature flag is enabled, a failover path activates, or an attacker forces alternate code paths through malformed input. Likewise, a package may not appear in normal traffic but still be reachable during incident recovery or maintenance windows. Teams should also be cautious when container base images, sidecars, or injected agents alter the runtime picture after the build scan has completed. In those cases, the static view remains valuable because it shows latent exposure that runtime may not exercise during a short observation window.
For identity-heavy or agentic systems, runtime evidence is also useful for confirming which secrets, tokens, or tool integrations are actually used by an AI agent or service account. That intersection is important because dormant permissions in a manifest can look harmless until an active workflow binds them at runtime. Practitioners should treat the runtime delta as a prioritisation input, not a blanket statement that unused code is safe.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATT&CK and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | ID.AM-2 | Asset inventory must be reconciled with what actually runs in production. |
| MITRE ATT&CK | T1027 | Attackers often hide malicious execution behind legitimate or dormant code paths. |
| NIST AI RMF | GOVERN | AI and runtime observability need accountable governance when telemetry drives decisions. |
| OWASP Non-Human Identity Top 10 | NHI-07 | Runtime validation helps expose overprivileged machine identities and unused secrets. |
Compare build inventory to runtime telemetry and update your asset records with deployed reality.
Related resources from NHI Mgmt Group
- What is the difference between static scanning and runtime protection for Java?
- What is the difference between static vulnerability scanning and runtime risk management?
- What is the difference between static scanning and runtime analysis in AppSec?
- How do you know if Oracle SoD evidence is actually working?
