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implementing-cloud-workload-protection

implementing-cloud-workload-protection

github.com/mukul975Security35.2k36 installsUpdated 8w ago
Repository

About implementing-cloud-workload-protection

A cloud workload protection guide for security practitioners, showing how AWS Systems Manager can check running instances for threat indicators. It focuses on processes, file integrity, network connections, resource anomalies, and unauthorized binaries.

Key features

  1. 01Check for suspicious processes
  2. 02Monitor critical file integrity
  3. 03Audit network connections
  4. 04Detect resource usage anomalies
  5. 05Identify unauthorized binaries by hash

Use cases

  1. 01Investigate mining processes on cloud instances
  2. 02Check for command-and-control connections
  3. 03Monitor changes to critical system files
  4. 04Find abnormal CPU usage

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill implementing-cloud-workload-protection

After installing, type/implementing-cloud-workload-protectionin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

LICENSE
references/
api-reference.md
scripts/
agent.py

Details

From the project README

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"]},
)

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