To fix the concurrency bug, we need to use a `threading.Lock` to ensure that the critical sections of the code (checking for jobs, processing them, and updating results) are executed atomically. This prevents multiple threads from reading or modifying the shared `self.jobs` and `self.results` dictionaries simultaneously.

Here is the complete fixed implementation:

```python
import threading

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

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

    def process_job(self, job_id, processor):
        # Lock the entire critical section.
        # We must hold the lock while processing because the original logic 
        # keeps the job in self.jobs until the result is written. 
        # If we released the lock during processing, another thread could 
        # pick up the same job_id, leading to double processing and race conditions.
        with self.lock:
            if job_id not in self.jobs:
                return False

            data = self.jobs[job_id]
            result = processor(data)
            
            # BUG FIX: This write is now protected by the lock
            self.results[job_id] = result
            
            del self.jobs[job_id]
        return True

    def get_result(self, job_id):
        # Lock to ensure we read a consistent state of results
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes:
1.  **Added `self.lock = threading.Lock()`**: Initialized a lock object in the constructor.
2.  **Protected `add_job`**: Wrapped the dictionary assignment in a lock to prevent race conditions if multiple threads try to add jobs concurrently.
3.  **Protected `process_job`**: Wrapped the entire method logic (Check -> Read -> Process -> Write -> Delete) in a `with self.lock:` block.
    *   *Note*: In a high-performance system, you might optimize this by removing the job from `self.jobs` immediately (claiming it) and then processing outside the lock. However, based on the provided code structure where the job remains in `self.jobs` during processing, holding the lock throughout is the correct way to prevent the specific race condition described.
4.  **Protected `get_result`**: Wrapped the read operation in a lock for consistency.