When Loom’s “Blur Sensitive Information” AI feature fails, sensitive data—such as passwords, credit card numbers, authentication tokens, API keys, or private client details—remains completely legible on screen. In other cases, the AI creates erratic, lagging blur boxes that drift across the frame, blurs non-sensitive text, or fails to generate any masking overlays at all.
Fast Fix
Slow down your scrolling and keep sensitive text stationary on screen for at least two seconds during capture. Loom’s computer vision model requires stable keyframes to anchor its bounding boxes; rapid page scrolling, dynamic popups, and high display scaling (such as 4K monitors running at non-native DPI) cause the optical tracking algorithms to lose lock.
Diagnostic Snapshot
| Parameter | Operational Detail |
|---|---|
| Failure Layer | Optical Character Recognition (OCR) and computer vision bounding-box tracking |
| Impact Level | High (accidental exposure of credentials, PII, or internal keys) |
| Hardware Vulnerability | High-DPI (Retina/4K) monitors, ultra-wide setups, low-contrast UI themes |
| Execution Point | Pre-capture desktop hooks or post-capture AI processing |
| Admin Access Required | No (controlled by workspace AI settings or recording parameters) |
Why the AI Blur Engine Fails
Loom’s sensitive data masking uses a two-stage computer vision pipeline:
[Screen Content Stream] ──► [Stage 1: OCR Text Detection] ──► [Stage 2: Pattern Classification]
│
┌────────────────────────────────────────────────────────────────┴───────┐
▼ ▼
[Pattern Matches PII / Key] [Pattern Not Recognized]
│ │
[Bounding Box Generated] [Zero Masking Applied]
│ │
▼ ▼
(Rapid Scrolling / Frame Drops) ──► Mask Drifts Off-Target Sensitive Data Exposed
This computer vision pipeline is vulnerable to specific visual triggers:
- Dynamic Motion and Rapid Scrolling: The AI model identifies a target string (e.g., an API key), calculates its X/Y coordinates, and applies a blur box. If you scroll quickly, the bounding box tracking loop can lag several frames behind the actual text, exposing the credentials during motion.
- Low Visual Contrast and Custom Fonts: Code editors with dark themes, syntax highlighting, or low-contrast interfaces (e.g., grey text on a charcoal background) reduce OCR recognition accuracy. If the optical detector cannot parse character boundaries, the pattern classifier never triggers.
- Display Canvas Scaling Mismatches: On 4K or high-refresh displays where macOS or Windows uses non-integer display scaling (e.g., 150% or 175%), coordinate mapping can become offset, placing the blur box adjacent to the sensitive text rather than directly over it.
Step-by-Step Resolution
Step 1: Adjust Recording Habits to Maintain AI Tracking Lock
Because the blur tool relies on optical tracking:
- Never rapidly scroll past sensitive fields: If you must navigate through a page containing credentials, pause scrolling for one full second before and after moving.
- Keep windows stationary: Avoid dragging or resizing application windows while sensitive information is visible inside them.
- Zoom in on the interface: Press
Ctrl +orCmd +in your browser or code editor to increase text size. Larger text drastically improves the OCR model’s ability to detect and mask string patterns accurately.
Step 2: Correct Display Resolution and DPI Scaling
Coordinate drift occurs when Loom calculates bounding boxes at a downsampled canvas resolution while recording at a higher native resolution:
- Check your operating system’s display scaling:
- Windows: Go to Settings > System > Display and set Scale to 100% or 200% (avoid fractional values like 125% or 175% during recording).
- macOS: Go to System Settings > Displays and choose a standard resolution setting.
- Windows: Go to Settings > System > Display and set Scale to 100% or 200% (avoid fractional values like 125% or 175% during recording).
- macOS: Go to System Settings > Displays and choose a standard resolution setting.
- If your recording has resolution downscaling issues that degrade the AI model’s text legibility, review Fix: Loom “Resolution Mismatch” (1080p recording showing as 720p).
Step 3: Prevent Frame Drops That Break Tracking
If your system drops frames during capture, the AI loses its optical anchor points, causing the blur mask to disappear or jump erratically:
- High-intensity graphics apps can starve Loom’s video capture pipeline. If you notice stutter or frame drops during software demonstrations, follow the steps in Troubleshooting: Loom “Frame Rate Drop” during Software Demos.
- On macOS, confirm that system overlays and drawing tools are not lagging, as this indicates WindowServer resource starvation. See Fix: Loom “Canvas” Drawing tools lagging on macOS.
Step 4: Verify Workspace AI Feature Permissions
If the blur tool fails to trigger entirely across all recordings, workspace administrators may have disabled automated AI processing:
- Have a Workspace Admin open Workspace Settings.
- Navigate to AI Features or Add-ons.
- Verify that Loom AI and automated content detection are toggled On.
- If other automated intelligence features are also unresponsive, check Fix: Loom “AI Auto-Title” failing to generate to confirm whether the workspace AI service is experiencing operational outages.
Hard Stop: What to Do If Sensitive Data Is Exposed
If you record a video and discover that the AI blur tool failed to obscure a password, private token, or confidential customer record:
- Immediately set the video to Private:
- Open the video page.
- Click Share and change the setting to Only people in your workspace or Only me.
- Open the video page.
- Click Share and change the setting to Only people in your workspace or Only me.
- Do not rely on post-capture re-processing for passwords: If a live credential (such as an AWS Secret Key or customer credit card) was visible for even a few frames, assume it has been compromised.
- Rotate the exposed credential immediately: Change the password, invalidate the API token, or revoke the session key on your production systems before re-recording or publishing the video.