Pros of Cartography

• Supports multiple cloud providers (AWS, GCP, Azure) and other data sources
• Provides a graph database for complex relationship analysis
• Offers more extensibility through custom data ingestion modules

Cons of Cartography

• Steeper learning curve due to Neo4j graph database
• Requires more setup and infrastructure to run effectively
• May be overkill for smaller environments or simpler use cases

Code Comparison

CloudMapper example (Python):

from cloudmapper.nodes import Account, Region
from cloudmapper.prepare import build_data_structure

account = Account(None, 'demo')
region = Region(account, 'us-west-2')
build_data_structure(account, region, args)

Cartography example (Python):

from cartography.intel.aws import eks
from cartography.util import run_analysis_job

session = boto3.Session()
neo4j_session = driver.session()
eks.sync(neo4j_session, session, ['us-west-2'], 1)
run_analysis_job('aws_eks_nodegroup_analysis', neo4j_session, {})

Both tools aim to map cloud infrastructure, but Cartography offers a more comprehensive approach with its graph database, while CloudMapper focuses primarily on AWS visualization. Cartography's flexibility comes at the cost of increased complexity, whereas CloudMapper provides a more straightforward solution for AWS-centric environments.

Pros of ScoutSuite

• Supports multiple cloud providers (AWS, Azure, GCP, Alibaba Cloud, OCI)
• Provides a web-based interface for easy report viewing and navigation
• Offers customizable rulesets for tailored security assessments

Cons of ScoutSuite

• May have a steeper learning curve due to its broader scope
• Potentially slower execution time when scanning multiple cloud providers
• Less focus on network visualization compared to CloudMapper

Code Comparison

ScoutSuite (Python):

from ScoutSuite.core.cli_parser import ScoutSuiteArgumentParser
from ScoutSuite.core.console_manager import ConsoleManager
from ScoutSuite.core.exceptions import RuleExceptions
from ScoutSuite.core.processingengine import ProcessingEngine
from ScoutSuite.core.ruleset import Ruleset

CloudMapper (Python):

from cloudmapper.webserver import run_webserver
from cloudmapper.prepare import prepare
from cloudmapper.gather import gather
from cloudmapper.find_risks import find_risks
from cloudmapper.report import report

Both tools are written in Python and focus on cloud security assessment. ScoutSuite offers a more comprehensive approach with multi-cloud support, while CloudMapper specializes in AWS environments with a strong emphasis on network visualization. ScoutSuite's code structure reflects its modular design for handling multiple cloud providers, whereas CloudMapper's code is more focused on specific AWS-related tasks.

Pros of CloudSploit

• Supports multiple cloud providers (AWS, Azure, GCP, Oracle)
• Offers a wider range of security checks and compliance frameworks
• Provides a web-based interface for easier visualization and management

Cons of CloudSploit

• Less focus on network visualization compared to CloudMapper
• May require more setup and configuration for advanced features
• Potentially steeper learning curve for users new to cloud security

Code Comparison

CloudMapper example (Python):

from cloudmapper.nodes import Account, Region
from cloudmapper.prepare import build_data_structure

account = Account(None, 'demo')
region = Region(account, 'us-west-2')
build_data_structure(account, region, {})

CloudSploit example (JavaScript):

const CloudConfig = require('./helpers/aws');
const scanner = require('./helpers/scanner');

const config = new CloudConfig();
scanner(config, (err, results) => {
    console.log(JSON.stringify(results, null, 2));
});

Both tools offer valuable insights into cloud infrastructure security, but they have different strengths. CloudMapper excels in network visualization and AWS-specific analysis, while CloudSploit provides a broader range of checks across multiple cloud providers. The choice between them depends on your specific needs and the cloud environments you're working with.

Pros of Cloudsplaining

• Focused specifically on IAM policy analysis and visualization
• Generates HTML reports for easy sharing and viewing
• Lightweight and easy to install via pip

Cons of Cloudsplaining

• Limited to IAM policy analysis, lacks broader AWS infrastructure mapping
• Does not provide real-time monitoring or continuous assessment capabilities

Code Comparison

Cloudsplaining:

from cloudsplaining.scan.policy_document import PolicyDocument
policy = PolicyDocument(policy_dict)
results = policy.analyze()

CloudMapper:

from cloudmapper.nodes import Account, Region
account = Account(None, "demo")
region = Region(account, "us-west-2")
region.get_vpcs()

CloudMapper offers a more comprehensive approach to AWS infrastructure analysis, including network mapping and security group visualization. It provides a broader view of the AWS environment but may require more setup and resources.

Cloudsplaining, on the other hand, specializes in IAM policy analysis, offering detailed insights into permissions and potential security risks. It's more focused but lacks the wider scope of CloudMapper.

Both tools serve different purposes within AWS security analysis. CloudMapper is better suited for overall infrastructure assessment, while Cloudsplaining excels at in-depth IAM policy evaluation.

Pros of Prowler

• More comprehensive security assessment, covering over 200 checks across multiple AWS services
• Regularly updated with new checks and features, ensuring coverage of latest AWS security best practices
• Provides detailed reports in various formats (HTML, JSON, CSV) for easy analysis and integration

Cons of Prowler

• Primarily focused on AWS, while CloudMapper supports multi-cloud environments
• May require more setup and configuration for specific use cases
• Can be more resource-intensive due to its extensive checks

Code Comparison

Prowler (Python):

def check_cloudtrail_enabled(self):
    regions = self.regions()
    for region in regions:
        trails = self.client('cloudtrail', region).describe_trails()
        if not trails['trailList']:
            self.add_issue(0, 'CloudTrail is not enabled', region)

CloudMapper (Python):

def get_ec2_instances(region):
    ec2 = boto3.client('ec2', region_name=region)
    instances = ec2.describe_instances()
    return [i for r in instances['Reservations'] for i in r['Instances']]

Both tools use Python and AWS SDK (boto3) for interacting with AWS services. Prowler's code snippet shows a security check implementation, while CloudMapper's focuses on data collection for visualization.

Pros of Checkov

• Supports multiple cloud providers (AWS, Azure, GCP) and IaC tools (Terraform, CloudFormation, Kubernetes)
• Offers a wide range of pre-built policies and custom policy creation
• Integrates easily with CI/CD pipelines and provides machine-readable output

Cons of Checkov

• Focuses primarily on static analysis, lacking the network visualization capabilities of CloudMapper
• May require more setup and configuration for complex environments
• Does not provide detailed AWS account inventory like CloudMapper

Code Comparison

CloudMapper example (Python):

from cloudmapper.nodes import Account, Region
from cloudmapper.query import query_aws

account = Account(None, 'demo')
region = Region(account, 'us-west-2')
query_aws(account, region)

Checkov example (CLI):

checkov -d /path/to/iac/code --framework terraform

CloudMapper is more focused on AWS-specific network mapping and visualization, while Checkov offers broader cloud security posture management across multiple providers and IaC tools. CloudMapper provides deeper AWS-specific insights, whereas Checkov excels in policy-based security checks and CI/CD integration.