Implement risk scoring by combining cloud provider tools with Kubernetes-native solutions for actionable insights.

Context

In production, fragmented visibility across AWS, Azure, GCP, and Kubernetes leads to missed critical issues. Risk scoring prioritizes vulnerabilities, misconfigurations, and drift that actually impact reliability or security.

Workflow

1. Inventory resources across all environments using native tools (AWS Config, Azure Resource Graph, GCP Asset Inventory, Kubernetes API).
2. Define risk criteria (e.g., CVSS score, exposure scope, remediation effort) aligned with team SLAs.
3. Integrate tools to collect findings:
   • AWS: Security Hub, GuardDuty
   • Azure: Security Center, Policy
   • GCP: Security Command Center, Config Connector
   • Kubernetes: kube-bench, Falco, Clair
4. Automate scoring via SIEM (e.g., Splunk, Elastic) or purpose-built platforms (e.g., Wiz, Palo Alto Prisma Cloud).
5. Validate and iterate by correlating scores with incident reports and adjusting weights.

Policy Example

| Risk Level | Criteria                          | Example Check                          |
|------------|-----------------------------------|----------------------------------------|
| Critical   | Publicly exposed database         | AWS Config rule for RDS public access |
| High       | Unpatched Kubernetes control plane| kube-bench check for CIS 5.1.1       |
| Medium     | Unused service principals         | Azure Policy for inactive identities  |

Tooling

• Cloud-native: Use built-in tools first (e.g., AWS Security Hub aggregates findings across services).
• Kubernetes: Combine CIS benchmarks (kube-bench), runtime security (Falco), and image scanning (Clair).
• Aggregation: Forward findings to a central SIEM or SOAR platform for scoring and correlation.

Tradeoffs

• False positives: Overly broad policies generate noise. Start with high-confidence checks (e.g., public buckets, unpatched nodes).
• Maintenance: Custom policies require updates as cloud providers release new services or Kubernetes versions.
• Coverage gaps: Some risks (e.g., application-layer flaws) require third-party tools beyond infrastructure scanning.

Troubleshooting

• No data in dashboard: Verify tool permissions (e.g., AWS IAM roles, Azure RBAC) and data collection intervals.
• Inconsistent scoring: Audit policy definitions for overlaps or missing criteria (e.g., CVSS vs. custom metrics).
• Stale findings: Ensure tools are polling at appropriate intervals (e.g., daily for critical issues, weekly for lower-risk).

Prioritize tools and policies that align with your team’s operational cadence. Start small, validate with real incidents, and expand scope incrementally.

Source thread: Has anyone found risk scoring that surfaces real issues across AWS Azure, GCP and Kubernetes??