// OSINT OpSEC Identity Generator

Maintaining operational security (OPSEC) is a critical part of any security-related task. Whether you’re conducting penetration testing, red teaming, or OSINT investigations, safeguarding your activity and identity is paramount. One overlooked aspect is the use of fake personal data to protect your operations.

Instead of relying on online services like Mockaroo or FakeNameGenerator—which can potentially track your activity—you can use Python and the Mimesis library to generate fake data locally. This approach ensures privacy, efficiency, and flexibility, while giving you full control over the data creation process.

In this post, we’ll explore how you can use Mimesis to generate realistic fake data tailored to your needs.

Why Use Mimesis?

Mimesis is a powerful Python library designed for generating synthetic data across multiple categories, including personal details, addresses, business information, and more. With support for over 40 locales, it’s perfect for generating data specific to any region.

Key Advantages of Mimesis:

• Privacy: Operates locally, removing the need for third-party services that could track your activity.
• Customizability: Generate data tailored to specific use cases, from personal identities to business mockups.
• Efficiency: Quickly create large datasets without relying on external APIs.

How to Generate Fake Data with Mimesis

Step 1: Install Mimesis

To get started, you’ll need to install the Mimesis library. If you’re using Conda, activate your environment and run the following command:

pip install mimesis

Step 2: Write a Python Script

The following Python script generates realistic Romanian-specific fake data using Mimesis. Replace the locale as needed to tailor it for other countries.

from mimesis import Person, Address
from mimesis.locales import Locale

# Initialize Mimesis generators for Romania locale
person = Person(Locale.RO)
address = Address(Locale.RO)

# Generate sample personal data
print("Generated Personal Data:")
print("Name:", person.full_name())
print("Email:", person.email())
print("Phone Number:", person.telephone())
print("Street Address:", address.address())
print("City:", address.city())
print("Postal Code:", address.postal_code())

# Generate multiple records
def generate_multiple_records(num_records):
    print("\nGenerating multiple records:")
    for _ in range(num_records):
        print("---")
        print("Name:", person.full_name())
        print("Email:", person.email())
        print("Phone Number:", person.telephone())
        print("Street Address:", address.address())
        print("City:", address.city())
        print("Postal Code:", address.postal_code())

# Generate 5 records as a test
generate_multiple_records(5)

Step 3: Run the Script

Save the script as generate_fake_data.py and run it in your terminal:

python generate_fake_data.py

Sample Output

The script will output fake data, such as:

Generated Personal Data:
Name: Maria Popescu
Email: maria.popescu@example.ro
Phone Number: +40 723 456 789
Street Address: Strada Mihai Viteazul 32
City: București
Postal Code: 010101

Generating multiple records:
---
Name: Andrei Ionescu
Email: andrei.ionescu@example.ro
Phone Number: +40 733 987 654
Street Address: Strada Libertății 10
City: Cluj-Napoca
Postal Code: 400100

Understanding the Code

Key Functions

• Person Module:
• full_name(): Generates a full name.
• email(): Creates a random email address.
• telephone(): Produces a valid phone number based on the locale.
• Address Module:
• address(): Generates a random street address.
• city(): Outputs a city name.
• postal_code(): Produces a valid postal code.

Batch Generation

The generate_multiple_records function simplifies the creation of multiple datasets, making it easy to create bulk fake data for testing purposes.

OPSEC Best Practices

1. Run Locally:
   Avoid online services by generating data directly on your machine.
2. Tailor Fields:
   Customize data fields to fit your specific needs. For example, you can generate industry-specific data using Mimesis’ extensive providers.
3. Validate Data:
   Ensure the generated data aligns with your operational requirements.
4. Secure Your Environment:
   Use isolated or virtual environments to further anonymize your activity.

Conclusion

Mimesis provides a reliable and private way to generate realistic fake data for your security tasks. With its rich feature set and local execution, it’s the ideal tool for maintaining OPSEC while working on sensitive projects.

Start using Mimesis today and take control of your data generation. If you have any questions or need help setting up your script, feel free to leave a comment!