Step 1: Specifying the Directory
The code prompts the user to enter the path of the directory containing the JSON files. This user input ensures flexibility and adaptability when working with different file structures.

Step 2: Checking Directory Existence
To ensure that the specified directory exists, the code verifies its existence. This step avoids potential errors and guarantees that the subsequent operations can be performed smoothly.

Step 3: Creating Empty Lists
If the directory exists, two empty lists, “vulnerabilities” and “misconfigurations,” are created. These lists will hold the extracted data from the JSON files.

Step 4: Processing JSON Files
The code loops through the JSON files in the specified directory, reading each file. It processes the JSON data and extracts relevant information based on the defined attributes such as target, title, description, severity, and more. The extracted data is then appended to the appropriate list, either “vulnerabilities” or “misconfigurations.”

Step 5: Creating Dictionaries
For each vulnerability or misconfiguration, a dictionary is created, containing the extracted attributes. These dictionaries provide a structured format for organizing the data.

Step 6: Appending Dictionaries to Lists
The dictionaries representing vulnerabilities and misconfigurations are appended to their respective lists, “vulnerabilities” or “misconfigurations.” This step consolidates the extracted data into manageable lists for further processing.

Step 7: Creating DataFrames
After processing all the JSON files, two DataFrames, “vulnerabilities_df” and “misconfigurations_df,” are created using the pd.DataFrame function from the pandas library. These DataFrames provide a tabular representation of the extracted data, enabling easy manipulation and analysis.

Step 8: Sorting DataFrames
If there are any vulnerabilities in the “vulnerabilities_df” DataFrame or misconfigurations in the “misconfigurations_df” DataFrame, they are sorted in descending order based on severity. This sorting arrangement allows users to prioritize and address the most critical issues first.

Step 9: Handling Empty Fields
Empty fields in both DataFrames are filled with the string “N/A.” This step ensures that all cells in the Excel file contain relevant information, even if certain attributes are missing for specific vulnerabilities or misconfigurations.

Step 10: Specifying Filename and Saving DataFrames
The user is prompted to enter the desired filename for the Excel output, without the extension. This user input provides flexibility when naming the output file. The DataFrames are then saved as separate sheets in the same Excel file using the ExcelWriter from pandas.

Step 11: Saving the Excel File
The Excel file is saved with the specified filename and the “.xlsx” extension. This ensures that the data is stored in a standardized format that can be easily accessed and shared.

Step 12: Providing Confirmation
To provide feedback to the user, a message is printed, indicating that the Excel file has been successfully generated. This confirmation assures users that the processing and exporting operations have been completed.

Conclusion:
The code snippet discussed in this blog post offers a comprehensive approach to process JSON files, extract vulnerability and misconfiguration data, and export the information to an Excel file. By leveraging the pandas library, organizations can efficiently organize, sort, and analyze the data, enabling proactive security measures and risk mitigation. With the ability to consolidate and visualize vulnerabilities and misconfigurations, organizations can make informed decisions and take swift action to safeguard their digital assets.