Evidence graph
Normalize cloud assets, identities, data stores, workloads, network reachability, and findings into one connected model.
CID decisions
Use CID to correlate impact and recommend the minimum remediation set with the highest risk reduction.
Executive proof
Translate technical exposure into board-ready metrics, narratives, and improvement evidence.
What the platform is designed to solve
Cloud security teams do not need another isolated scanner. They need a decision layer that explains how weaknesses combine across identities, workloads, data, and network exposure. Cloudryption is designed around that operating problem.
The platform ingests cloud evidence, normalizes it into a common graph, derives attack paths, ranks crown-jewel exposure, and generates remediation decisions that are explainable to engineers and executives.
Core platform capabilities
- Read-only cloud discovery across AWS, Azure, and GCP operating models.
- Asset normalization and relationship mapping to reduce duplicate or fragmented resource views.
- Identity risk intelligence for excessive privileges, dormant identities, toxic combinations, and privilege escalation paths.
- Data risk intelligence for sensitive data exposure, public access, encryption posture, and access blast radius.
- Attack path intelligence that connects internet, identity, workload, and data paths.
- Risk reduction simulation that ranks fixes by expected impact, not by finding count.
- Executive and technical reporting generated from the same evidence graph.
How it fits your existing stack
Cloudryption is not positioned as a replacement for every cloud security tool. It is the decision layer that helps teams understand which findings, identities, attack paths, and data exposures deserve action first.
It can operate beside posture scanners, cloud-native security services, ticketing systems, and reporting workflows by adding correlation, prioritization, and risk-reduction context.
See Cloudryption in your environment
Request a focused walkthrough and validate the decision model against realistic cloud security scenarios.