Here's the corrected implementation of the `JobQueue` class with proper synchronization to prevent race conditions. A `threading.Lock` is used to protect all access to the shared `jobs` and `results` dictionaries. The processor is executed outside the critical section to allow concurrent job execution while still ensuring thread safety for data structure modifications.

```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
            # Extract data and remove job from queue while still holding lock
            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # Execute processor outside the lock to allow concurrency
        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:
- **Thread-safe access to shared state:** All reads and writes to `self.jobs` and `self.results` are protected by a `threading.Lock`.
- **Critical section minimized:** The lock is released before executing the processor function, allowing multiple jobs to be processed concurrently.
- **Safe job removal:** Jobs are removed from the queue while the lock is held, preventing race conditions where multiple threads might process the same job.
- **Consistent locking in `get_result`:** Ensures that result retrieval is also thread-safe.

### Usage Example:
```python
import time

def slow_processor(data):
    time.sleep(1)
    return data * 2

queue = JobQueue()
queue.add_job(1, 5)
queue.add_job(2, 10)

queue.process_job(1, slow_processor)
queue.process_job(2, slow_processor)

print(queue.get_result(1))  # Output: 10
print(queue.get_result(2))  # Output: 20
```

This implementation ensures thread safety while maintaining good concurrency for job processing.