We’ve been evaluating Netskope for cloud app visibility and DLP. I’m seeing a significant number of false positives. Legitimate, sanctioned cloud storage apps (OneDrive for Business, approved Box instances) are being blocked or flagged as “High Risk” during user sessions.
Key data from our test logs:
- Event: `Application: Microsoft OneDrive`, `Activity: Upload`, `Policy Action: Blocked`
- Stated reason: `Anomaly Detection: Unusual Upload Pattern`. The threshold appears to be a static 50 MB in 5 minutes.
- No user-specific historical baselines are applied.
This is disrupting standard workflow. Has anyone decoupled the anomaly scoring from the blocking policies? The default profiles seem overly aggressive.
Our current policy configuration snippet:
```json
{
"policy_name": "Data Loss Prevention",
"apps": ["OneDrive", "Box", "Google Drive"],
"action": "block",
"triggers": [
{
"detector": "anomaly_engine",
"threshold": "high_risk"
}
]
}
```
Is the issue in the anomaly engine's baseline learning period, or is manual tuning of per-app thresholds required? Looking for concrete tuning parameters or a better architecture for sanctioned apps.
Numbers don't lie.
You've hit on the classic tuning problem with these systems. That static 50 MB/5 min threshold for "unusual upload" is way too low for a power user syncing project files.
The key is to decouple scoring from blocking. You can keep the anomaly detection active for visibility and alerts, but for your sanctioned apps list, build a separate, more permissive policy that only blocks on true DLP violations (like detected SSNs), not on behavioral risk scores. That way, the engine can still "see" the unusual pattern for reporting, but it doesn't break the workflow.
We built user group-based thresholds, so our developers have a much higher baseline than our front-office staff. It took about a month of monitoring in "alert-only" mode to set proper baselines. Have you tried running the policy in that mode first to gather data?
You're hitting the static threshold problem head on. That 50MB/5min baseline is meaningless without user or group context.
Run the anomaly engine in alert-only mode for two weeks to collect real baselines. Then set dynamic thresholds. For engineering, our OneDrive threshold is now 500MB/5min. For finance, it's 75MB. You need to separate the policy action from the risk score.
Create a sanctioned apps policy that only blocks on confirmed DLP content matches. Let the anomaly score feed a dashboard instead of a block rule.
Prove it with a benchmark.