75%
of global knowledge workers now use AI tools regularly
(Worklytics 2025)
Closed Community Initiative
Data Leakage Consortium
A dedicated consortium of individuals, companies, and experts solving one of the hardest enterprise AI problems: confidential data leakage through browser-based AI tools.
Join the Consortium
Apply in under two minutes. We review each submission for fit.
Join the Data Leakage Consortium
Work with peers focused on getting organizations to 0-1% data leakage risk confidence with stronger controls and fewer unknowns.
Complete the form below to apply. We will follow up with next steps after review so you can help reduce business risk and close the growing control gap.
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Why This Matters Now
Only 17% of companies have controls in place to prevent employees from uploading and sending confidential data with an AI tool in a browser.
Browser-based AI adoption is outpacing governance. Security teams can estimate risk, but most cannot measure leakage confidence with precision across workflows, users, and tools.
Risk Signals
57%
of employees using shadow AI tools input sensitive data into them
(Menlo Security 2025)
AI adoption is accelerating faster than enforceable controls. That gap is exactly why this consortium exists.
The Reality Gap
Even growth-stage and late-stage companies implementing controls still report major blind spots. They often estimate their safeguards work only part of the time and lack dependable leakage measurement.
Consortium Focus
Build a practical, operator-led playbook that helps companies close control gaps, benchmark efficacy, and move toward high confidence in near-zero leakage outcomes.
Who Should Join
Should I join?
If you're trying to reduce data leakage and help people stay safer online, this community is for you. Frankly, anyone can join: security experts, leaders, managers, students, operators, and builders.
If you're a security leader or IT owner, collaborate on policies and controls that actually work in production.
If you're an operator building DLP or workflow controls, share what works and pressure-test approaches with peers.
If you're a practitioner, researcher, or expert, help define measurable methods to prevent leakage and close blind spots.
If you're a manager, student, or curious contributor, bring questions, learn quickly, and help teams ship safer AI usage.
You do not need to be a deep technical specialist to contribute. Practical insight, cross-functional perspective, and commitment to safer systems are all valuable here.
Cost of Data Leakage
The average cost of a data breach reached $4.88M in 2024.
Average global breach cost benchmark (IBM 2024).
Significant Financial Costs
Recovery expenses include forensics, legal work, customer notification, and support. Ransomware demands can exceed $1.1M, and regulatory fines can reach 4% of global turnover under strict regimes.
Reputational Damage and Lost Trust
Breaches trigger churn, weaken brand confidence, and can reduce long-term demand. Negative experiences spread quickly and make acquiring new customers harder.
Operational Disruption
Investigations and containment can force downtime, disrupt workflows, and reduce productivity for weeks as teams adapt to emergency controls.
Loss of Intellectual Property
Leaked product plans, trade secrets, or proprietary data can erase years of R&D advantage and create long-term revenue drag.
Legal and Compliance Liabilities
Exposure incidents often lead to class-action claims, partner disputes, and compliance penalties, creating ongoing legal and contractual obligations.
Common Causes of Data Leaks
Human Error: mis-sent files, phishing clicks, and unsafe handling.
System Misconfiguration: exposed cloud storage and weak defaults.
Third-Party Vulnerabilities: vendor access paths becoming weak points.
Malicious Insiders: intentional exfiltration by trusted users.
Reducing these risks requires proactive controls, practical playbooks, and cross-team collaboration. That is the purpose of this consortium.