The following versions of the Enhanced Data Classification Pipeline currently receive security updates and support.

Users are strongly encouraged to use the latest stable version to receive security fixes and improvements.

The security of this project is taken seriously. If you discover a security vulnerability, please report it responsibly.

• Open a public GitHub Issue for security vulnerabilities.
• Publicly disclose vulnerabilities before a fix is available.
• Publish exploit code without prior coordination.

Responsible disclosure helps protect users and contributors.

Please contact the project maintainer and include the following information:

Provide a detailed explanation of the issue.

Explain how the vulnerability can be triggered.

Describe the potential consequences if exploited.

Include:

• Logs
• Screenshots
• Sample code
• Error messages

if available.

If possible, suggest a fix or improvement.

The project aims to follow these response targets:

Response times may vary based on project availability and issue complexity.

Keep dependencies updated regularly.

Check outdated packages:

pip list --outdated

Update packages:

pip install --upgrade package_name

Update all project dependencies:

pip install --upgrade -r requirements.txt

All user-provided datasets should be treated as untrusted input.

Developers should:

• Validate dataset structure.
• Verify file formats.
• Handle malformed CSV files gracefully.
• Sanitize external inputs.

Example:

if not dataset_path.endswith(".csv"):
    raise ValueError("Only CSV files are supported.")

When loading datasets:

• Verify file existence.
• Restrict supported formats.
• Avoid executing file contents.
• Handle corrupted files safely.

Example:

import os

if not os.path.exists(dataset_path):
    raise FileNotFoundError("Dataset not found.")

Never commit:

• API Keys
• Access Tokens
• Passwords
• Private Certificates
• Cloud Credentials
• Database Secrets

Use environment variables instead.

Example:

import os

API_KEY = os.getenv("API_KEY")

Before pushing code:

git status

Verify sensitive files are not staged.

Recommended .gitignore entries:

.env
venv/
__pycache__/
*.log
*.db
.ipynb_checkpoints/

This project processes machine learning datasets.

Developers should be aware of:

Malicious datasets may:

• Manipulate training results
• Reduce model accuracy
• Introduce hidden biases

Always verify dataset sources.

Unexpected data values may cause:

• Training failures
• Evaluation errors
• Visualization issues

Validate data before training.

Avoid using information from:

• Test data during training
• Future observations
• Target variables in features

Proper train-test separation must always be maintained.

Before using custom datasets:

Use trusted sources such as:

• Kaggle
• UCI Machine Learning Repository
• Government Open Data Platforms
• Academic Research Datasets

Review:

• Missing values
• Duplicates
• Invalid labels
• Outliers

Datasets should not contain:

• Passwords
• Financial records
• Personal identifiers
• Private customer information

unless explicit authorization has been granted.

Contributors should:

• Follow PEP 8 standards.
• Use secure coding practices.
• Avoid hardcoded credentials.
• Handle exceptions properly.
• Validate external inputs.
• Keep dependencies updated.
• Review code before submission.

Example:

try:
    dataset = pd.read_csv(file_path)
except Exception as error:
    print(f"Dataset loading failed: {error}")

Current project limitations include:

• No authentication system.
• No user account management.
• No encrypted dataset storage.
• No access control mechanisms.
• Local execution environment only.

Future versions introducing:

• Cloud deployment
• APIs
• User authentication
• Web dashboards
• Distributed training

should undergo additional security reviews.

Security patches and fixes will be announced through:

• GitHub Releases
• Release Notes
• Repository Changelog
• Project Documentation

Users should regularly update to the latest version.

Researchers who responsibly disclose valid security issues may be acknowledged in:

• Release Notes
• SECURITY.md Acknowledgments
• Contributors List

unless anonymity is requested.

Before submitting code:

• No secrets committed
• Dependencies updated
• Input validation added
• Error handling implemented
• Dataset loading tested
• No unsafe code execution
• Documentation updated

For security-related concerns, please contact:

Project: Enhanced Data Classification Pipeline

Maintainer: Kommineni Pranav

Email: your-email@example.com

GitHub Repository: https://github.com/your-username/data-classification-pipeline

This Security Policy is based on industry best practices for open-source software, machine learning systems, and responsible vulnerability disclosure.

By contributing to or using this project, you help maintain a secure and trustworthy environment for all users.

Thank you for helping keep the Enhanced Data Classification Pipeline secure. 🔒