To fix the concurrency bug (race condition) in the `JobQueue` implementation, we need to introduce a `threading.Lock`. This lock ensures that access to the shared dictionaries (`self.jobs` and `self.results`) is atomic, preventing multiple threads from reading or modifying the state simultaneously in an unsafe way.

Here is the complete fixed implementation:

```python
import threading

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

    def add_job(self, job_id, data):
        # 2. Lock the dictionary while adding a job
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # 3. Lock to safely check existence and retrieve the job
        # We must hold the lock to prevent another thread from 
        # deleting or modifying the job between the check and the access.
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs[job_id]
        
        # 4. Process the job outside the lock.
        # Holding the lock during processing would block other threads 
        # from adding jobs or retrieving results, reducing performance.
        result = processor(data)
        
        # 5. Lock to safely store the result and remove the job
        with self.lock:
            self.results[job_id] = result
            del self.jobs[job_id]
        
        return True

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

### Explanation of the Fix

1.  **`self.lock = threading.Lock()`**: Added in `__init__` to create a synchronization primitive.
2.  **Critical Sections**: The lock is acquired using the `with self.lock:` context manager before accessing `self.jobs` or `self.results`. This ensures that only one thread can manipulate these dictionaries at a time.
3.  **Atomic Check-and-Act**: In `process_job`, the check (`if job_id not in self.jobs`) and the retrieval (`data = self.jobs[job_id]`) are performed while holding the lock. This fixes the Time-Of-Check-to-Time-Of-Use (TOCTOU) race condition where one thread might check for a job, and another thread might delete it before the first thread reads it.
4.  **Lock Release During Processing**: The `processor(data)` call is placed *outside* the lock. This is a best practice to avoid holding the lock for a long time (if the processor performs heavy work or I/O), which would serialize the entire queue and defeat the purpose of concurrency.
5.  **Safe Cleanup**: The deletion of the job (`del self.jobs[job_id]`) is moved inside the lock after the result is saved, ensuring the dictionary state remains consistent.