Here is the corrected implementation of the `JobQueue` class with proper thread-safety using a `threading.Lock`. The fix ensures that all accesses to shared state (`jobs` and `results` dictionaries) are protected by a lock, eliminating the race condition.

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self._lock = threading.Lock()

    def add_job(self, job_id, data):
        with self._lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        with self._lock:
            if job_id not in self.jobs:
                return False

            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # Process outside the lock to avoid blocking other operations
        result = processor(data)

        with self._lock:
            self.results[job_id] = result
        
        return True

    def get_result(self, job_id):
        with self._lock:
            return self.results.get(job_id)
```

### Key Improvements

1. **Lock Protection**: All access to shared dictionaries (`jobs`, `results`) is now protected by a single lock (`self._lock`), preventing concurrent modifications and data races.
2. **Atomic Check-and-Delete**: The job existence check and deletion are performed atomically within the same lock context, avoiding check-then-act race conditions.
3. **Non-Blocking Processing**: The actual job processing (via `processor(data)`) occurs outside the lock, allowing other threads to continue managing the queue without unnecessary blocking.
4. **Thread-Safe Result Retrieval**: Access to `results` in `get_result()` is also synchronized to ensure safe concurrent reads.

### Optional Enhancement: Use RLock for Reentrancy

If you anticipate that the same thread might call `process_job` recursively or in nested contexts, consider replacing `threading.Lock()` with `threading.RLock()` to allow the same thread to acquire the lock multiple times.