How It Works

1) Agentless data collection

CloudGuard collects security-relevant signals via cloud-native APIs, including configuration state, identity and permission metadata, storage exposure, logs, and network reachability information — without deploying host agents or performing intrusive traffic probing.

This approach ensures broad coverage across dynamic cloud environments while avoiding performance impact and operational friction on production workloads.

2) Correlation and normalization

Collected signals are normalized and correlated across cloud accounts, regions, and services. CloudGuard continuously maps assets, identities, trust relationships, and exposure surfaces into a consistent multi-cloud view, including short-lived and frequently changing resources.

3) Dynamic risk graph modeling

Assets, identities, permissions, network relationships, and detected risks are unified into a contextual risk graph. This graph-based model captures how individual issues relate to each other, enabling situation-aware analysis instead of isolated findings.

4) AI-powered attack-path prediction

Built on the dynamic risk graph, CloudGuard applies AI-assisted attack-path prediction to simulate potential attacker movement across identities, permissions, and reachable assets. This allows CloudGuard to identify exposure paths that are most likely to be exploited and to quantify their potential blast radius.

The result is risk prioritization driven by real-world exploitability rather than static severity scores alone.

5) Closed-loop response and verification

CloudGuard translates prioritized exposure paths into actionable remediation guidance, tracks progress over time, and continuously verifies whether applied changes effectively reduce risk across the environment.

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