Continuous detection of misconfigurations, IAM over-privilege, container and Kubernetes exposure, and CVE-mapped cloud-native risk — across single and multi-cloud environments.
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Full-stack cloud security posture management from identity to workload.
Identify publicly exposed S3 buckets, Azure Blob containers, and GCP Cloud Storage buckets, plus overly permissive bucket policies and missing encryption-at-rest.
S3 · Blob · GCSDetect over-permissioned roles, unused admin privileges, wildcard policy statements, and stale credentials across AWS IAM, Azure AD, and GCP IAM.
IAM · Privilege AnalysisScan container images for known CVEs, detect exposed Kubernetes dashboards and API servers, and review RBAC and pod security policy configuration.
K8s · Container CVEsIdentify overly permissive security groups, NSGs, and firewall rules exposing management ports, databases, or internal services to the public internet.
Security Groups · NSGsUnified posture view and policy enforcement across AWS, Azure, and GCP accounts — normalized risk scoring for organizations running hybrid or multi-cloud architectures.
AWS · Azure · GCPMap cloud configuration against ISO 27001, SOC2, PCI-DSS, and DPDP Act 2023 control requirements with continuous drift detection and evidence collection.
ISO 27001 · SOC2 · PCI-DSSEach cloud provider defines a different boundary for what they secure vs. what you must secure.
Cloud misconfiguration remains the leading cause of cloud security incidents — not zero-day exploits against the cloud platforms themselves, which are generally well-hardened by AWS, Azure, and Google, but customer-side errors in how cloud resources are provisioned, permissioned, and exposed. A single misconfigured storage bucket, an overly broad IAM policy, or a forgotten development security group can expose an entire dataset or provide an attacker a foothold into production infrastructure.
Every major cloud provider operates on a shared responsibility model: the provider secures the underlying infrastructure, while the customer is responsible for securing what they put on top of it — data classification, identity and access management, network configuration, and application-layer security. This model is frequently misunderstood, leading organizations to assume cloud-provided defaults are secure when in practice S3 buckets, Azure Storage, and GCS buckets all require explicit configuration to prevent public exposure, and IAM systems default toward permissive access unless deliberately constrained.
Public storage bucket exposure has caused some of the largest data breaches across all three major cloud providers — exposed customer PII, leaked credentials, source code, and backup databases. Detection requires continuous scanning, not point-in-time audits, because bucket policies change frequently through CI/CD automation, third-party integrations, and well-intentioned but careless manual changes. Our CSPM continuously enumerates storage resources across connected cloud accounts and flags public read/write access, missing encryption, and policy drift from your defined baseline.
Identity is the new perimeter in cloud environments. Attackers who compromise a single low-privilege credential frequently escalate through IAM policy chains — assuming roles, modifying policies they have permission to edit, or exploiting trust relationships between accounts. Our IAM audit identifies wildcard permissions (Action: "*"), unused administrative privileges, stale access keys older than 90 days, and cross-account trust relationships that create unintended privilege escalation paths. This maps directly to the principle of least privilege central to NIST SP 800-53, ISO 27001 Annex A.9, and the AWS/Azure/GCP Well-Architected security pillars.
Container adoption has shifted a substantial share of cloud workloads to Kubernetes, which introduces its own configuration surface: exposed API servers without authentication, overly permissive RBAC bindings granting cluster-admin to service accounts, missing network policies allowing unrestricted pod-to-pod communication, and container images running known-vulnerable base layers. We scan container registries against our CVE database (1,625+ advisories with CISA KEV and EPSS scoring) to identify images carrying actively exploited vulnerabilities before they reach production.
Organizations running workloads across AWS, Azure, and GCP simultaneously face a governance challenge: each provider has different IAM models, different security group/NSG/firewall semantics, and different native security tooling (AWS Security Hub, Microsoft Defender for Cloud, Google Security Command Center) that don't share a unified risk taxonomy. Without normalization, security teams end up triaging three separate alert streams with inconsistent severity scoring. Our multi-cloud governance layer normalizes findings across providers into a single risk-ranked queue, so a critical S3 exposure and a critical Azure Storage exposure are scored on the same scale and prioritized together.
Cloud security posture and vulnerability management are often treated as separate disciplines, but cloud-native CVEs — vulnerabilities in container base images, managed Kubernetes versions, serverless runtimes, and cloud-native middleware — require the same CVE-to-asset mapping rigor as traditional infrastructure. We correlate discovered cloud workloads against our continuously updated CVE advisory database, prioritizing by CISA Known Exploited Vulnerabilities status and EPSS exploitation probability rather than CVSS base score alone, which often overstates real-world risk for vulnerabilities with no observed exploitation.
For regulated organizations, cloud misconfiguration is not just a security risk but a compliance failure. ISO 27001, SOC2, PCI-DSS, and India's DPDP Act 2023 all impose requirements on data protection, access control, and audit logging that map directly to specific cloud configuration settings — encryption at rest and in transit, access logging retention, multi-factor authentication enforcement, and data residency controls. Our compliance mapping continuously evaluates cloud configuration against these frameworks and surfaces drift before it becomes an audit finding.
Serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) and managed services (RDS, Cosmos DB, Cloud SQL) shift infrastructure management to the provider but introduce their own configuration surface: function execution roles with excessive permissions, environment variables containing hardcoded secrets, publicly accessible managed database instances without network restriction, and missing encryption on data at rest. Because these services abstract away the underlying compute, security teams frequently assume the provider has handled security comprehensively — but identity, network exposure, and data protection configuration remain entirely the customer's responsibility under the shared responsibility model, regardless of how "managed" the service appears.
Cloud environments are not static — Infrastructure as Code deployments, manual console changes, third-party SaaS integrations requesting new permissions, and CI/CD automation all introduce configuration changes continuously. A security posture that was compliant at the last quarterly audit can drift into non-compliance within days through entirely legitimate, unreviewed changes. Continuous monitoring with automated baseline comparison — alerting on any deviation from your approved configuration baseline, not just known-bad patterns — catches this drift in near real time rather than at the next scheduled review cycle, which for many organizations is measured in months.
Hardcoded credentials, API keys committed to source repositories, and secrets embedded in environment variables or container images remain one of the most common and most damaging cloud security failures, frequently providing attackers a direct path to broader account compromise once discovered through automated scanning of public and private repositories. We assess secrets management maturity across your cloud accounts — verifying use of dedicated secrets management services (AWS Secrets Manager, Azure Key Vault, GCP Secret Manager) over hardcoded or environment-variable-based credential storage, and checking for credential rotation policies and access logging on secret retrieval.
Cloud security posture management is preventive, but organizations must also prepare for the scenario where a cloud resource is compromised despite preventive controls. This requires cloud-specific incident response capability: the ability to rapidly enumerate what a compromised IAM role or service account could access, cloud-native forensic log sources (CloudTrail, Azure Activity Log, GCP Audit Logs) configured with sufficient retention, and pre-authorized incident response playbooks for cloud-specific scenarios like credential compromise, cryptomining workload injection, or data exfiltration via storage API abuse.
Organizations running workloads across AWS, Azure, and GCP simultaneously face a compounding security challenge: each provider has its own IAM model, its own logging format, its own native security tooling, and its own terminology for conceptually similar controls. A security team fluent in AWS IAM policy structure may misconfigure an equivalent Azure RBAC assignment simply because the underlying models differ subtly. Unified cloud security posture management normalizes findings across providers into a consistent risk model, so a critical misconfiguration in any cloud is prioritized the same way regardless of which provider's console it lives in — eliminating the blind spots that emerge when teams treat each cloud as a separate security domain with separate tooling and separate review cadences.
Cloud security increasingly extends beyond IaaS configuration into the container orchestration layer. Kubernetes clusters introduce their own attack surface — overly permissive RBAC bindings, exposed dashboards, unencrypted secrets stored as Kubernetes Secrets objects rather than a proper secrets manager, and container images built from vulnerable base layers. We assess Kubernetes-specific risk alongside traditional cloud configuration: pod security standards enforcement, network policy segmentation between namespaces, and admission controller policies that prevent insecure workloads from being scheduled in the first place.
A persistent source of cloud security failures is misunderstanding the shared responsibility model — the cloud provider secures the infrastructure underlying the service (physical data centers, hypervisor, network infrastructure), while the customer remains fully responsible for securing what they configure within that infrastructure: identity and access policy, data classification and encryption, network security group rules, and application-layer security. The vast majority of cloud breaches stem from customer-side misconfiguration, not provider infrastructure failure — meaning the responsibility for the controls that actually prevent most breaches sits with the customer regardless of which provider they use, a reality our cloud security assessments are built around.
Engineering teams under deadline pressure routinely grant broad permissions — wildcard IAM policies, overly permissive security groups — because it's faster than scoping access precisely, with the intention to "tighten it later." That tightening rarely happens once the system is working in production. We help organizations build permission-scoping into the standard provisioning workflow itself, using infrastructure-as-code templates with least-privilege defaults baked in, so secure configuration is the path of least resistance rather than an afterthought that competes with delivery deadlines.
Cloud environments are never static — a security posture assessed and hardened today drifts as new resources are provisioned, configurations change through routine operational work, and temporary exceptions granted for a specific deployment are never revoked once their original purpose is served. This drift means cloud security posture management cannot be a one-time hardening exercise; it requires continuous re-assessment against the same baseline, with automated alerts the moment a previously-compliant resource drifts out of its secure configuration state, closing the gap before drift accumulates into a meaningful exposure.
How is cloud security different from traditional network security? Cloud security shifts the primary attack surface from network perimeter defense toward identity and configuration management, since cloud resources are reachable via API regardless of network topology — meaning IAM misconfiguration, not firewall rules, is typically the highest-risk factor in cloud environments.
Do we need separate tooling for each cloud provider? Not necessarily — unified CSPM platforms normalize findings across AWS, Azure, and GCP into one consistent risk view, though some highly provider-specific capabilities may still benefit from native vendor tooling used alongside the unified platform.
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