To fix the concurrency bug, we need to introduce a `threading.Lock`. This lock ensures that access to the shared data structures (`self.jobs` and `self.results`) is serialized, preventing race conditions where multiple threads might read, write, or modify the dictionaries simultaneously.

Specifically, the original code had a "Time-of-Check to Time-of-Use" (TOCTOU) race condition. Two threads could check if a job exists, both proceed to process it, and then both write results, potentially overwriting each other or causing errors.

Here is the complete fixed implementation. Note that I have optimized the locking strategy in `process_job` to release the lock during the actual processing (the `processor(data)` call) to allow other threads to work concurrently, while still ensuring the job is claimed atomically.

```python
import threading

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

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

    def process_job(self, job_id, processor):
        # Acquire lock to safely check existence and retrieve the job
        with self.lock:
            if job_id not in self.jobs:
                return False
            # Using pop() is atomic: it removes the job from the queue
            # and returns the data in one step. This prevents multiple
            # threads from processing the same job_id.
            data = self.jobs.pop(job_id)
        
        # Perform the actual processing outside the lock.
        # This allows other threads to add or process other jobs
        # while this processor is busy.
        result = processor(data)

        # Acquire lock to safely write the result
        with self.lock:
            self.results[job_id] = result

        return True

    def get_result(self, job_id):
        # Protect the 'results' dictionary while reading
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes Made:
1.  **Added `self.lock = threading.Lock()`**: Created a lock object in the constructor.
2.  **Protected `add_job`**: Wrapped the dictionary assignment in a `with self.lock:` block.
3.  **Fixed `process_job` Logic**:
    *   Moved the job removal (`pop`) inside the lock. This is critical. In the original code, a thread could check if a job exists, get interrupted, and another thread could pick the same job before the first one processed it. By using `pop` inside the lock, the job is claimed immediately.
    *   Moved the processing logic (`processor(data)`) **outside** the lock. This is a performance optimization. Holding the lock during CPU-intensive work blocks other threads unnecessarily.
    *   Protected the result write (`self.results[job_id] = result`) with the lock.
4.  **Protected `get_result`**: Wrapped the read operation in a lock to ensure thread-safe reading.