Cyscale CNAPP Platform

Discovers and classifies sensitive data in IaaS and PaaS stores such as object storage, databases, and data warehouses, surfacing data exposure risk alongside infrastructure findings.

Exports compliance evidence pre-mapped to framework control requirements (SOC 2, ISO 27001, PCI DSS), in formats auditors can consume directly: not raw CSV exports requiring manual assembly.

Analyzes IAM policies across AWS, Azure, and GCP to surface over-permissioned roles, unused permissions, and cross-account trust relationships that create lateral movement opportunities.

Continuously audits cloud and Kubernetes configuration across AWS, Azure, and GCP against security benchmarks, flagging misconfigurations and identity-permission gaps that create exploitable exposures.

Correlates individual misconfigurations and CVEs into chained attack scenarios showing lateral movement paths from exposed entry point to a target asset. Produces a prioritized list of attack paths rather than a flat CVE inventory.

Analyzes container images and dependencies for CVEs, malicious or compromised packages, and SBOM generation across the build pipeline.

Aggregates posture findings and policy enforcement across multiple cloud accounts, subscriptions, and projects from a single control plane, critical for organizations with 10+ cloud accounts.

Maps detected misconfigurations to specific control requirements across CIS Benchmarks, NIST 800-53, SOC 2, PCI DSS, HIPAA, and ISO 27001 in a single assessment pass.

Audits cloud service configurations across AWS, Azure, and GCP against security best practices and benchmarks, flagging misconfigurations such as public storage, permissive network rules, and disabled logging. Coverage breadth and per-service depth vary significantly across products.

Continuously discovers and inventories cloud resources across accounts, subscriptions, and projects so posture assessment runs against a current, complete picture of the environment rather than a stale or partial asset list. Coverage of newer and less common resource types varies across products.

Assesses the identities and service accounts that AI models, pipelines, and agents use, flagging over-permissioned non-human identities and access paths that violate least privilege. Reports identity risk as a posture finding, distinct from enforcing access policies at the model API at runtime.

Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.

Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.

Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.