AI-driven detection of unauthorized subcontracting, offshoring, and anomalous code behaviors
Detect unauthorized subcontracting, offshore code submissions, and anomalous developer behaviors in your code repositories and communication channels using advanced AI analytics.
Rivia's AI-driven monitoring analyzes commit patterns, code review behaviors, and communication anomalies to ensure all development work is performed by authorized personnel—protecting your intellectual property and maintaining code integrity.
Setup & activation approximately 4 weeks
Ongoing managed service (annual contract)
Startup: $7,500 setup + $1,500/month
Small Business: $12,500 setup + $2,5000/month
Medium Business: $15,000 setup + $5,000/month
Comprehensive behavioral monitoring with AI-powered anomaly detection
Continuous analysis of commit patterns, code review behaviors, and communication anomalies using machine learning.
Real-time alerts for suspicious activities including unusual commit times, coding style changes, and unauthorized access patterns.
Expert triage and investigation support with documented findings and evidence collection for HR review.
Summaries of detected anomalies, investigation outcomes, and recommended actions delivered monthly.
Identify developers outsourcing work to unauthorized third parties or offshore contractors.
Detect sudden changes in coding style, commit patterns, or quality that suggest different authors.
Flag commits and activities occurring during unusual hours or from unexpected geographic locations.
Monitor for suspicious data access, large file transfers, or unauthorized repository cloning.
Analyze Slack/Teams messages for indicators of unauthorized collaboration or code sharing.
Detect account access patterns suggesting credential sharing or unauthorized account usage.
Rivia monitors multiple data sources for comprehensive behavioral analysis
GitHub, GitLab, Bitbucket
Slack, Microsoft Teams
VPN, authentication systems
Jira, Azure DevOps
Deploy connectors to repositories and communication platforms with appropriate API access.
AI models learn normal behavior patterns for each developer based on historical data.
Real-time analysis of all development activities with automatic anomaly flagging.
Expert triage of alerts with documented findings and recommended actions.