Vision One's API is decent for pulling data, but their native dashboards are too rigid for real SRE work. If you're defining SLIs for detection coverage or mean time to acknowledge (MTTA), you need to own the visualization layer.
I built a set of Grafana dashboards to track what actually matters. The repo is here: [link]. It's built around two core SLOs we care about:
* **Detection-to-Triage Latency:** Time from Vision One alert to creation in our incident management system.
* **Platform Coverage:** Percentage of our production workloads sending telemetry, derived from the endpoint inventory data.
The API has some sharp edges. For example, the pagination on the Workbench (alerts) endpoint is cursor-based, but the `nextCursor` field can be null even when there are more records. You have to check the total count against your retrieved count every time. Here's the wrapper function I had to write:
```python
def get_all_workbench_items(initial_url, headers):
all_items = []
url = initial_url
while url:
response = requests.get(url, headers=headers)
data = response.json()
all_items.extend(data.get('items', []))
# Don't trust 'nextCursor' alone; check if we've fetched all items
url = None
if data.get('nextCursor'):
if len(all_items) < data.get('totalCount', 0):
url = f"{initial_url}&nextCursor={data['nextCursor']}"
return all_items
```
The dashboards plot error budgets and include annotations for major incidents (pulled from our postmortem system), so we can correlate platform outages with security visibility gaps. If your SLO is "99.9% of endpoints report hourly," a Vision One outage that breaks data collection should burn your error budget, and this makes that clear.
Useful for others who want to move beyond "is the console up?" (their SLA) to "is it reliably providing the data we need to meet our security reliability objectives?" (our SLO).
SRE: Sleep Randomly Eventually
Totally agree on owning the visualization layer. The native dashboards are fine for a high-level view, but they fall apart when you need to correlate data across systems for a true engineering analysis.
That pagination quirk is a classic example of an API sharp edge that'll burn you in production. We hit something similar and ended up implementing a retry with a decaying window on the `items` count, because sometimes the API would just return an empty array mid-pagination. Your wrapper looks solid. Did you run into any issues with rate limiting on the inventory endpoint when calculating platform coverage? We had to add some aggressive caching for that.
Hope is not a strategy