To fix the concurrency bug, we need to ensure that access to the shared dictionaries (`self.jobs` and `self.results`) is synchronized. The original code has a race condition where multiple threads could read the same job from `self.jobs`, process it concurrently, and then write results simultaneously, or encounter `KeyError` exceptions when deleting the job.

The most robust fix for a queue implementation is to **claim** the job (remove it from the pending queue) while holding a lock, process it outside the lock to allow concurrency, and then store the result under the lock. This prevents multiple threads from processing the same job and ensures thread-safe writes to the results dictionary.

Here is the complete fixed implementation:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        # Initialize a lock to protect shared state
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        # Lock to ensure thread-safe addition to the queue
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        with self.lock:
            # Check if job exists
            if job_id not in self.jobs:
                return False
            
            # Extract data and remove from queue immediately to "claim" the job.
            # This prevents other threads from picking up the same job.
            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # Process the job outside the lock to allow other threads to work concurrently
        result = processor(data)
        
        with self.lock:
            # Lock to ensure thread-safe write to results
            self.results[job_id] = result
            
        return True

    def get_result(self, job_id):
        # Lock to ensure thread-safe read of results
        with self.lock:
            return self.results.get(job_id)
```

### Explanation of Changes:
1.  **Added `self.lock = threading.Lock()`**: A threading lock is initialized in `__init__` to synchronize access to shared resources.
2.  **Protected `add_job`**: The write to `self.jobs` is now wrapped in `with self.lock:` to prevent race conditions when adding jobs while others are processing.
3.  **Refactored `process_job`**:
    *   **Claiming the Job**: Inside the lock, we check for the job's existence, retrieve the data, and immediately **delete** it from `self.jobs`. This "claims" the job, ensuring no other thread can process it (fixing the race condition where multiple threads might process the same job).
    *   **Processing Outside Lock**: The `processor(data)` call happens outside the lock. This allows the queue to remain responsive and process other jobs while one job is being computed.
    *   **Storing Result**: The write to `self.results` is wrapped in `with self.lock:` to fix the specific race condition mentioned in the bug comment ("multiple threads can write simultaneously").
4.  **Protected `get_result`**: The read from `self.results` is synchronized to ensure consistency.

*Note: By removing the job from `self.jobs` before processing, if the `processor` raises an exception, the job will be lost (not present in `jobs` or `results`). This is standard behavior for high-performance concurrent queues; if retry logic is needed, it should be handled by the caller or the processor itself.*