Here's the complete fixed implementation of the `JobQueue` class that addresses the concurrency bug by using proper locking mechanisms:

```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]

        # Perform processing 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 Fixes:

1. **Added a Thread Lock**: A `threading.Lock` is initialized in `__init__` to synchronize access to shared data structures.

2. **Protected Data Access**: All reads and writes to `self.jobs` and `self.results` are now enclosed in `with self.lock:` blocks to prevent race conditions.

3. **Optimized Lock Scope**: The lock is released during the CPU-intensive `processor(data)` call to avoid unnecessary blocking of other threads, while still ensuring atomicity for job removal and result storage.

4. **Safe Deletion and Storage**: The job is safely removed from `self.jobs` before processing, and the result is stored atomically within the lock.

This implementation ensures thread-safe job queue operations and prevents data corruption or lost updates in a multi-threaded environment.