Here's the complete fixed implementation. The core issue is that concurrent threads can read, modify, and write to the shared dictionaries (`self.jobs` and `self.results`) without synchronization, leading to data races and potential `KeyError` or corrupted state.

```python
import threading

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

    def add_job(self, job_id, data):
        # 2. Protect dictionary modification with the lock
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Extract data and remove the job under lock. 
        # This prevents another thread from picking up the same job concurrently.
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs[job_id]
            del self.jobs[job_id]
            
        # 3. Process outside the lock to avoid blocking other threads during 
        # potentially long-running tasks.
        result = processor(data)
        
        # 4. Store the result under lock to fix the race condition on writes
        with self.lock:
            self.results[job_id] = result
            
        return True

    def get_result(self, job_id):
        # 5. Protect dictionary reads with the lock
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes:
1. **Added `threading.Lock()`**: A reentrant-safe lock is initialized in `__init__` to serialize access to shared mutable state.
2. **Critical Sections Protected**: All reads and writes to `self.jobs` and `self.results` are wrapped in `with self.lock:` blocks. This eliminates TOCTOU (Time-of-Check-to-Time-of-Use) races and concurrent dictionary modification errors.
3. **Lock Scope Optimization**: The `processor(data)` call is intentionally placed **outside** the critical section. Holding a lock during CPU-bound or I/O-bound processing would severely limit concurrency. By extracting the data and removing the job first, we release the lock while processing, then reacquire it only to safely store the result.