To fix the concurrency bug, we need to introduce a `threading.Lock` to synchronize access to the shared dictionaries (`self.jobs` and `self.results`).

The race condition in the original code arises because multiple threads can check if a job exists, read its data, and then attempt to write results or delete the job simultaneously. This leads to duplicate processing, `KeyError` exceptions during deletion, or overwriting results.

Here is the complete fixed implementation:

```python
import threading

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

    def add_job(self, job_id, data):
        # Lock to ensure atomicity when adding a job
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Lock to safely check for job existence and retrieve data.
        # We use pop() to atomically remove the job from the queue.
        # This prevents multiple threads from processing the same job.
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs.pop(job_id)
        
        # Perform the actual processing outside the lock.
        # This allows other threads to pick up other jobs while this one is running.
        result = processor(data)
        
        # Lock to safely write the result to the results dictionary
        with self.lock:
            self.results[job_id] = result
        
        return True

    def get_result(self, job_id):
        # Lock to safely read from the results dictionary
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes Explained:
1.  **Added `self.lock`**: A `threading.Lock` instance is created in `__init__` to protect shared resources.
2.  **Protected `add_job`**: The lock ensures that adding a job is atomic.
3.  **Protected Check-and-Remove in `process_job`**: The check `if job_id not in self.jobs` and the removal of the job are now wrapped in a lock. Using `self.jobs.pop(job_id)` ensures that the job is removed from the pending list atomically, preventing other threads from picking up the same job ID while it is being processed.
4.  **Protected `get_result`**: Reading results is also synchronized to ensure thread safety.
5.  **Lock Scope**: The lock is released before calling `processor(data)`. This is crucial for performance; holding the lock during the actual processing would serialize all jobs and defeat the purpose of a concurrent queue.