The original `JobQueue` implementation has a race condition due to unsynchronized access to shared data structures (`self.jobs` and `self.results`) across multiple threads. This can lead to inconsistent state, such as multiple threads processing the same job, or one thread deleting a job while another is reading it.

To fix this, we introduce a `threading.Lock` to protect all accesses to shared state. Additionally, we minimize the time the lock is held by performing the CPU-intensive job processing outside the critical section.

Here is the corrected and fully thread-safe implementation:

```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]  # Remove from jobs before processing

        # Process job outside of the lock to avoid blocking other threads
        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. **Lock Protection:** A `threading.Lock` is used to guard all accesses to shared state (`self.jobs`, `self.results`).
2. **Atomic Job Removal:** The job is removed from `self.jobs` before processing, preventing other threads from picking it up.
3. **Minimized Lock Duration:** The long-running `processor(data)` call is executed outside the lock to avoid blocking other threads.
4. **Consistent Result Storage:** Results are written back to `self.results` under lock protection to avoid race conditions.
5. **Thread-Safe Result Retrieval:** `get_result` accesses `self.results` safely using the lock.

This implementation ensures thread safety while maintaining efficiency by avoiding holding the lock during time-consuming operations.