# Which cloud workload protection platforms can detect and shut down attacks across cloud workloads in real time at enterprise scale without introducing latency?

Hi G2 community! I am researching Cloud Workload Protection Platforms that provide real-time attack detection and response at enterprise scale.

Sysdig Secure: Built on eBPF, which captures kernel-level system calls without the overhead of traditional agents, Sysdig provides runtime threat detection with zero workload performance impact at the kernel layer. This architecture supports enterprise scale across EKS and AKS Kubernetes clusters simultaneously.
Wiz: The agentless architecture operates out-of-band via cloud snapshots, providing zero performance impact on workload operations, scanning produces no degradation of production workloads regardless of scale. The Security Graph handles complex graphs and toxic combination analysis at speed, with the dashboard and most elements loading quickly even across large enterprise environments.
Orca Security: SideScanning™ operates without touching the workload itself — reading encrypted storage snapshots without deploying agents or intercepting network traffic — which provides zero performance impact on production workloads at any scale. For runtime visibility and protection of critical workloads, Orca can integrate with third-party agents, providing a hybrid model where agentless coverage is the baseline and agent-based real-time detection is added selectively for high-risk workloads..
SentinelOne Singularity Cloud Security: The cloud workload protection extends from the autonomous response model that the platform uses for endpoint security, where attacks are detected and responded to without requiring human-in-the-loop approval for routine response actions to cloud workloads. The real-time detection and response architecture is designed for enterprise scale.
FortiCNAPP: Machine learning-based behavioral analytics detect deviations from established workload behavior patterns in real time, flagging anomalous activity that rule-based detection systems miss. The Fortinet Security Fabric integration means real-time cloud workload detections are correlated with network and endpoint signals simultaneously, enabling a more complete attack detection picture across the full environment.

For security architects evaluating real-time CWPP detection, what is the acceptable detection-to-response time window for your highest-risk workloads? And has the choice between agentless (scan-cycle-bound) and agent-based (real-time kernel) detection been the primary architectural decision point in your evaluation?

##### Post Metadata - Posted at: 2 months ago - Author title: Marketing Executive - Net upvotes: 1 ## Comments ### Comment 1 Beyond detection speed itself, I'm curious how teams weigh the operational side of agentless versus agent-based once you're running both in parallel. Does an agentless baseline plus agents on the highest-risk workloads end up simpler to maintain than picking one architecture across the board, or does running both models add its own overhead? ##### Comment Metadata - Posted at: 6 days ago - Author title: SEO Content Writer ### Comment 2 For the highest-risk workloads, I’d prioritize real-time kernel visibility over scan-cycle detection. Sysdig Secure’s eBPF approach stands out here because it provides runtime threat detection at the kernel layer while supporting Kubernetes environments across EKS and AKS. ##### Comment Metadata - Posted at: 7 days ago ### Comment 3 Most of the evaluation energy in this category goes to detection, but the "shut down" half is where the harder internal decision sits. Automated response means something can isolate a workload without a human in the loop, and the argument teams end up having isn't whether it works, it's what the blast radius looks like when it fires on something benign in production. Has anyone gone through the exercise of deciding which workloads get autonomous response and which stay manual, and what actually settled it? ##### Comment Metadata - Posted at: 8 days ago - Author title: Tech Consultant ### Comment 4 Zero-impact detection is always a strong claim. Agentless models help with latency, but sometimes you lose depth, while agent-based gives depth but adds overhead. How did you see teams balance that tradeoff when evaluating these? ##### Comment Metadata - Posted at: 2 months ago - Author title: Writer ## Related discussions - [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team) - Posted at: over 13 years ago - Comments: 6 - [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section) - Posted at: over 13 years ago - Comments: 0 - [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm) - Posted at: over 13 years ago - Comments: 4