Notifications
Clear all
Topic starter
24/06/2026 10:56 pm
TL;DR: Cloud-native teams are drowning in overlapping visibility tools, while AI-assisted development and faster deployments are pushing vulnerabilities into production before alert queues can keep up, according to Aqua Security. The real shift is from collecting findings to reducing exploitable exposure in runtime, where business context and operational clarity now matter more than raw signal volume.
NHIMG editorial — based on content published by Aqua Security: Built for This Moment
Questions worth separating out
Q: How should security teams prioritise cloud vulnerabilities when alert volume is overwhelming?
A: Prioritise vulnerabilities by whether they are present in running workloads, reachable from an attack path, and connected to business-critical services.
Q: Why do visibility tools fail to reduce cloud security risk on their own?
A: Visibility tools fail when they produce findings without telling teams which ones matter in production.
Q: What do security teams get wrong about CNAPP consolidation?
A: They often assume consolidation automatically creates clarity.
Practitioner guidance
- Rebuild triage around runtime exposure Map vulnerabilities to live workloads, public reachability, and business criticality before assigning remediation priority.
- Collapse overlapping CNAPP ownership Assign a single team to own exposure triage across posture, workload, and runtime tools, then define which platform is authoritative for each decision type.
- Measure production risk reduction, not alert volume Track how many exploitable issues are removed from reachable production paths, rather than how many findings are generated or closed in a queue.
What's in the full article
Aqua Security's full article covers the operational detail this post intentionally leaves for the source:
- The specific runtime exposure management workflow Aqua says it uses to separate exploitable issues from noisy findings.
- The product and operating-model changes the vendor says are needed to reduce alert fatigue across cloud native environments.
- The way Aqua positions AI workloads and AI model discovery inside its broader runtime protection story.
- The enterprise execution detail behind its contextual prioritisation approach, which this post only frames at a governance level.
👉 Read Aqua Security's analysis of runtime exposure management and cloud risk →
Runtime exposure management: what it means for cloud security teams?
Explore further
25/06/2026 7:45 am
Runtime exposure management is the natural endpoint of visibility fatigue. When cloud teams can already scan, alert, and centralise, the remaining problem is not detection density but decision quality. The article reflects a broader market shift from monitoring to protection, where operational context becomes the only way to convert findings into action. Practitioners should treat this as a governance reset, not a tooling refresh.
A few things that frame the scale:
- 88.5% of organisations acknowledge that their non-human IAM practices lag behind or are merely on par with their human identity and access management efforts, according to The 2024 Non-Human Identity Security Report.
- Only 19.6% of security professionals express strong confidence in their organisation's ability to securely manage non-human workload identities, which shows how narrow the operational trust gap remains.
A question worth separating out:
Q: How can organisations tell whether runtime protection is actually working?
A: Look for fewer exploitable issues reaching production, shorter time spent on non-exploitable findings, and clearer remediation decisions tied to real business impact. If teams are still spending most of their time curating alerts, runtime protection is not yet changing outcomes. Effective programmes shrink the set of problems that can actually be exploited.
👉 Read our full editorial: Runtime exposure management is replacing visibility-first cloud security
