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Implementing cloud workload protection

Skill onfire7777/universal-ai-skills-library/skills/implementing-cloud-workload-protection

Implements cloud workload protection using boto3 and google-cloud APIs for runtime security monitoring, process anomaly detection, and file integrity checking on EC2/GCE instances. Scans for cryptomining, reverse shells, and unauthorized binaries. Use when building runtime security controls for cloud compute workloads.From its SKILL.md

Install
npx -y skills add onfire7777/universal-ai-skills-library --skill implementing-cloud-workload-protection

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SKILL.md

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Implementing Cloud Workload Protection

When to Use

  • When deploying or configuring implementing cloud workload protection capabilities in your environment
  • When establishing security controls aligned to compliance requirements
  • When building or improving security architecture for this domain
  • When conducting security assessments that require this implementation

Prerequisites

  • Familiarity with cloud security concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Instructions

Monitor cloud workloads for runtime threats by checking process lists, network connections, file integrity, and resource utilization anomalies.

import boto3

ssm = boto3.client("ssm")
# Run command on EC2 instances to check for suspicious processes
response = ssm.send_command(
    InstanceIds=["i-1234567890abcdef0"],
    DocumentName="AWS-RunShellScript",
    Parameters={"commands": ["ps aux | grep -E 'xmrig|minerd|cryptonight'"]},
)

Key protection areas:

  1. Process monitoring for cryptominers and reverse shells
  2. File integrity monitoring on critical system files
  3. Network connection auditing for C2 callbacks
  4. Resource utilization anomaly detection (CPU spikes)
  5. Unauthorized binary detection via hash comparison

Examples

# Check for unauthorized outbound connections
ssm.send_command(
    InstanceIds=instances,
    DocumentName="AWS-RunShellScript",
    Parameters={"commands": ["ss -tlnp | grep ESTABLISHED"]},
)

What ships with it: 3 files

19.4 KB alongside SKILL.md, 1 of them executable

references/

scripts/

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