The Monitoring Transparency Report: Q3-Q4 2022

Why We Publish This Report

In June, we published our first transparency report covering Q1 2022. We committed to making this a regular practice — because a monitoring company that isn’t transparent about its own data practices has no business asking employers to trust it with employee data.

This second report covers Q3-Q4 2022 (July through December). It follows the same structure: what data we collect, how it’s used, how long it’s retained, what changed, and what we learned.

What We Collected

In H2 2022, Teambridg’s platform collected the following categories of work pattern data:

• Application usage patterns: Which categories of applications (communication, development, creative, etc.) are used during work hours, aggregated at the team level. We do not track specific URLs, document titles, or file names.
• Focus time metrics: Duration and frequency of uninterrupted work blocks (45+ minutes without context switches).
• Meeting patterns: Calendar-derived meeting frequency, duration, and distribution. We do not access meeting content, transcripts, or recordings.
• Work hour patterns: Start times, end times, and after-hours activity frequency. Used for burnout risk analysis.
• Communication metadata (new in Q3): Message volume, response times, and channel activity from Slack/Teams integrations. We never access message content — metadata is hashed and aggregated.

The 23% reduction in data volume came from two changes: tighter default aggregation thresholds (we now aggregate application usage into broader categories by default) and reduced polling frequency for work pattern detection.

How Data Was Used

In H2 2022, Teambridg data was used for the following purposes:

• Team-level dashboards (100% of customers): Work pattern insights surfaced to managers at the team level — never individual surveillance.
• Employee self-dashboards (89% of customers): Employees viewing their own work pattern data. Up from 76% in H1 — more organizations are enabling employee access, which we strongly encourage.
• Burnout risk analysis (62% of enterprise customers): Our new Predictive Burnout Analytics feature, launched in Q4.
• Manager Copilot recommendations (34% of enterprise customers): Actionable recommendations based on team patterns. Adoption is growing as the feature exits beta.
• Aggregated research (with consent): Anonymized data contributed to our quiet quitting analysis and other published research. Only organizations that explicitly opt in contribute to research datasets.

Data was NOT used for:

• Individual employee surveillance or ranking
• Automated performance decisions
• Sale or sharing with third parties
• Advertising or marketing purposes

What Changed Based on Feedback

We made several substantive changes in H2 2022 based on customer and employee feedback:

1. Reduced default data retention from 12 to 6 months. Multiple customers asked why we needed a year of detailed data. The honest answer was: we didn’t. Six months of data is sufficient for trend analysis and burnout prediction. Customers who need longer retention can opt in, but the default is now more conservative.

2. Added granular data export controls. Employees can now export their personal data in machine-readable format (JSON or CSV) from their self-dashboard. This supports GDPR data portability requirements and gives employees genuine ownership of their data.

3. Introduced “monitoring pause” for employees. Employees can now request a monitoring pause for specific time periods (medical appointments, personal calls, etc.) directly from their dashboard, without needing manager approval. Pauses are logged for transparency but no data is collected during the pause window.

4. Enhanced integration transparency. When Slack or Teams integrations are active, employees now see a persistent indicator showing that communication metadata is being analyzed, with a link to detailed documentation about what is and isn’t collected.

Law Enforcement and Government Requests

In H2 2022, Teambridg received zero law enforcement or government requests for employee monitoring data.

Our policy on law enforcement requests is unchanged: we will resist any request that we believe is overbroad or lacks proper legal authority. If compelled to produce data, we will notify the affected customer and employee (to the extent legally permitted) and will produce only the minimum data required by the specific legal order.

We publish this section not because we expect frequent requests, but because transparency about government access is a fundamental trust issue. Employees deserve to know whether the data collected about their work patterns could end up in the hands of law enforcement.

Commitments for 2023

Based on what we learned in 2022, here are our transparency commitments for 2023:

1. Quarterly transparency reports. We’re moving from semi-annual to quarterly reports starting Q1 2023.
2. Independent privacy audit. We will commission an independent third-party audit of our data practices and publish the results.
3. AI transparency documentation. As our predictive analytics capabilities expand, we’ll publish detailed documentation about how our AI models work, what data they use, and how we validate their accuracy.
4. Employee advisory panel. We’re establishing an advisory panel of employees from customer organizations to provide direct feedback on our product decisions and data practices.
5. Open-source privacy tools. We plan to open-source several internal privacy tools (data anonymization, aggregation, and minimization libraries) to raise the bar for the entire monitoring industry.

Transparency isn’t a competitive advantage we want to keep. It’s a standard we want to make universal. If every monitoring vendor published reports like this, the industry’s trust deficit would shrink dramatically. We hope our example encourages others to follow.

Thank you for a transformative 2022. The best is ahead.