Start by opening and reading the CSV file. You can use Python's built-in `csv` module to achieve this. Use `csv.reader` to parse the CSV file line by line. Make sure to handle the file with a `with` statement to ensure it is properly closed after reading.

```python
import csv

with open('data.csv', mode='r', newline='') as file:
csv_reader = csv.reader(file)
headers = next(csv_reader)
rows = list(csv_reader)
```

Convert the CSV data into a dictionary format where each row corresponds to an entry in the JSON structure. Use the headers from the CSV file as keys for each dictionary entry. This step involves iterating over each row and pairing each element with its corresponding header.

```python
data_list = [dict(zip(headers, row)) for row in rows]
```

Ensure that the data is correctly structured and does not contain any unexpected or malformed entries. This can involve checking for empty fields, ensuring data types are consistent, and possibly removing or correcting problematic rows.

```python
for data in data_list:
# Example check: ensure all fields have data
assert all(data.values()), "Data validation failed!"
```

With the data validated, prepare it for JSON serialization. This usually involves ensuring that all data types are JSON-serializable. Python's built-in data types (str, int, float, list, dict, etc.) are typically safe for JSON.

Use Python's `json` module to write the structured data to a JSON file. The `json.dump()` function can be used to serialize the list of dictionaries to a file, ensuring the file is in a human-readable format with the `indent` parameter.

```python
import json

with open('data.json', mode='w') as json_file:
json.dump(data_list, json_file, indent=4)
```

Implement error handling to manage potential issues such as file I/O errors or data serialization problems. Use try-except blocks to catch and manage exceptions, providing informative messages or fallback mechanisms.

```python
try:
with open('data.csv', mode='r', newline='') as file:
csv_reader = csv.reader(file)
headers = next(csv_reader)
rows = list(csv_reader)
data_list = [dict(zip(headers, row)) for row in rows]
with open('data.json', mode='w') as json_file:
json.dump(data_list, json_file, indent=4)
except Exception as e:
print(f"An error occurred: {e}")
```

Finally, open the generated JSON file to verify the output. Ensure the data is correctly formatted and matches the expected structure. You can manually inspect the JSON file or write a script to load the JSON and perform checks on the content.

```python
with open('data.json', mode='r') as json_file:
json_data = json.load(json_file)
assert isinstance(json_data, list), "Output verification failed!"
assert all(isinstance(item, dict) for item in json_data), "Output verification failed!"
```

By following these steps, you can ensure that data is accurately transferred from a CSV file to a JSON file without the need for third-party tools.