This portfolio demonstrates comprehensive Python automation skills for AWS infrastructure security, large-scale data processing, and DevSecOps practices. The project showcases 4 main Python code examples as requested for the Nebula Tech Solutions Python DevOps Engineer role.

DevSecOps/
├── aws_automation/
│   └── iam_policy_scanner.py          # AWS IAM Policy Scanner & Remediation Framework
├── data_processing/
│   └── log_analyzer.py                # Large-scale Log Analysis & Security Event Processing
├── security_automation/
│   └── secrets_manager.py             # Secrets Rotation & GuardDuty Alert Handler
├── framework/
│   └── aws_security_framework.py      # Reusable AWS Infrastructure Security Framework
├── technical_answers/
│   └── answers.md                     # Technical Questions & Detailed Answers
├── tests/                             # Unit tests and integration tests
├── requirements.txt                   # Python dependencies
└── README.md                          # This file

Features:

• Comprehensive IAM policy scanning (managed, inline user, and role policies)
• Security violation detection (wildcard actions, resources, admin privileges)
• Automated remediation capabilities
• Detailed reporting and compliance tracking
• Error handling and retry mechanisms

Key Capabilities:

# Initialize scanner
scanner = IAMPolicyScanner(aws_region='us-east-1', dry_run=True)

# Run comprehensive scan
scanner.scan_managed_policies()
scanner.scan_user_policies()
scanner.scan_role_policies()

# Generate security report
report = scanner.generate_report('json')

Security Checks:

• Wildcard actions (* or service:*)
• Wildcard resources (arn:aws:*:*:*:*)
• Administrative privileges (iam:*, organizations:*)
• Public access policies (Principal": "*")

Features:

• Multi-format log processing (JSON, Apache, Nginx, CloudTrail)
• Security event detection and analysis
• Concurrent processing with ThreadPoolExecutor
• Memory-efficient chunked file reading
• Comprehensive security event correlation

Supported Formats:

• AWS CloudTrail logs
• Apache/Nginx access logs
• JSON application logs
• Compressed files (gzip, bz2)

Usage:

# Initialize analyzer
analyzer = LogAnalyzer()

# Analyze security logs
security_analysis = analyzer.analyze_security_logs([
    'cloudtrail.json',
    'apache.log',
    'application.json'
])

Features:

• Automated secrets rotation for AWS Secrets Manager
• GuardDuty finding remediation
• Secure password and API key generation
• Notification system (email, Slack, Teams)
• Compliance tracking and reporting

Capabilities:

# Initialize secrets manager
secrets_manager = SecretsManager(dry_run=True)

# Rotate expired secrets
results = secrets_manager.rotate_expired_secrets()

# Handle GuardDuty findings
guardduty_handler = GuardDutyHandler()
remediation_results = guardduty_handler.auto_remediate_findings()

Features:

• Modular security checker architecture
• Extensible framework for new security checks
• IAM and S3 security checkers included
• Comprehensive reporting and recommendations
• Easy integration with existing systems

Architecture:

# Initialize framework
framework = SecurityFramework(aws_region='us-east-1')

# Run security scan
results = framework.run_security_scan()

# Save results
framework.save_results('security_results')

Detailed answers to all technical questions are provided in technical_answers/answers.md:

1. IAM Policy Scanner Framework Design - Comprehensive framework architecture with policy scanning, violation detection, and automated remediation
2. AWS Workflow Automation - Step Functions + Lambda integration for scalable security automation
3. Error Handling & Retries - Robust Boto3 error handling with exponential backoff and circuit breakers
4. Terraform + Python Integration - Infrastructure as Code with Python automation layer
5. Logging, Monitoring & Alerting - Comprehensive observability strategy with CloudWatch and SNS

• Python 3.9+
• AWS CLI configured with appropriate permissions
• Terraform (for infrastructure deployment)

# Clone the repository
git clone <repository-url>
cd DevSecOps

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure AWS credentials
aws configure

Create a .env file with your configuration:

AWS_REGION=us-east-1
AWS_PROFILE=default
DRY_RUN=true
LOG_LEVEL=INFO

cd aws_automation
python iam_policy_scanner.py

cd data_processing
python log_analyzer.py

cd security_automation
python secrets_manager.py

cd framework
python aws_security_framework.py

• Boto3 Mastery: Comprehensive use of AWS SDK for Python
• Service Integration: IAM, S3, Secrets Manager, GuardDuty, CloudWatch
• Error Handling: Robust retry mechanisms and exception handling
• Pagination: Efficient handling of large datasets

• Multi-format Support: JSON, CSV, log files, compressed formats
• Concurrent Processing: ThreadPoolExecutor for performance
• Memory Efficiency: Chunked file reading for large files
• Pattern Recognition: Security event detection and correlation

• Policy Analysis: IAM policy security scanning
• Automated Remediation: Self-healing security controls
• Compliance Tracking: CIS, SOC2, PCI compliance checks
• Alerting: Real-time security notifications

• Modular Design: Extensible architecture
• Reusability: Framework components for different use cases
• Configuration Management: YAML-based configuration
• Testing: Unit tests and integration tests

1. Least Privilege: All IAM roles and policies follow least privilege principle
2. Secrets Management: Secure handling of credentials and secrets
3. Input Validation: Comprehensive input sanitization and validation
4. Error Handling: Secure error handling without information disclosure
5. Logging: Structured logging for audit trails
6. Monitoring: Real-time monitoring and alerting

The code examples demonstrate compliance with:

• CIS AWS Foundations Benchmark
• SOC 2 Type II
• PCI DSS
• AWS Well-Architected Framework

• Concurrent Processing: Multi-threaded operations for improved performance
• Memory Management: Efficient memory usage for large datasets
• Caching: Intelligent caching of frequently accessed data
• Batch Operations: Batch processing for AWS API calls

• Structured Logging: JSON-formatted logs for easy parsing
• Metrics Collection: Custom CloudWatch metrics
• Alerting: SNS-based alerting system
• Dashboard: CloudWatch dashboards for visualization

# Run unit tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=. --cov-report=html

# Run integration tests
pytest tests/integration/ -v

# Run in development mode
python -m pytest tests/ -v
python -m black .
python -m flake8 .

# Deploy using Terraform
cd terraform/
terraform init
terraform plan
terraform apply