Vision
Transform device security from reactive to proactive by leveraging artificial intelligence to detect anomalies, predict threats, and automatically enforce protective measures before incidents occur.
The Problem We're Solving
Traditional MDM security relies on:
- Manual policy configuration
- Rule-based threat detection
- Reactive response to incidents
- Limited visibility into emerging threats
- High false-positive rates
This approach misses sophisticated attacks and creates alert fatigue for security teams.
Our Solution
An AI-powered security layer that:
- Learns normal device behavior patterns
- Detects anomalies in real-time
- Predicts potential security incidents
- Automates threat response
- Reduces false positives by 85%
Planned Capabilities
Behavioral Analytics
- User Behavior Profiling: Learn typical usage patterns per user
- Device Activity Monitoring: Track app usage, network access, data transfers
- Anomaly Detection: Flag unusual behavior automatically
- Risk Scoring: Assign dynamic risk scores to devices and users
Threat Intelligence
- Global Threat Database: Learn from security events across all customers (anonymized)
- Industry-Specific Threats: Sector-based threat modeling
- Zero-Day Protection: ML detection of unknown threats
- Threat Actor Patterns: Identify attack campaigns
Automated Response
- Adaptive Policies: Automatically adjust security policies based on threat level
- Quarantine Actions: Isolate compromised devices instantly
- Data Protection: Prevent sensitive data exfiltration
- Remediation Workflows: Auto-apply fixes for common issues
Advanced Features
- Predictive Security: Forecast potential security incidents 7-14 days ahead
- Attack Chain Analysis: Understand how attacks progress across your fleet
- Natural Language Queries: Ask questions like "Show me devices that accessed unusual domains yesterday"
- Security Copilot: AI assistant for security analysts
Use Cases
Insider Threat Detection
Identify employees exhibiting risky behavior before data breaches occur.
Example: System detects an employee downloading unusually large amounts of data to personal storage and automatically blocks further access while alerting security team.
Compromised Device Identification
Detect devices infected with malware based on behavior patterns.
Example: Device starts communicating with known botnet command-and-control servers. AI quarantines device and initiates remote wipe procedures.
Data Loss Prevention
Stop sensitive data from leaving your organization.
Example: Executive device tries to upload confidential documents to unknown cloud service. AI blocks transfer and notifies compliance team.
Zero-Day Threat Protection
Protect against unknown vulnerabilities.
Example: AI detects new exploit attempt based on unusual system calls and network patterns, blocks attack, and updates global threat database.
Technical Approach
Machine Learning Models
- Supervised Learning: Train on labeled security incidents
- Unsupervised Learning: Discover unknown threat patterns
- Reinforcement Learning: Improve response strategies over time
- Neural Networks: Deep learning for complex threat detection
Data Privacy
- All ML training uses anonymized, aggregated data
- On-device processing for sensitive operations
- Customer data never leaves your environment
- GDPR, CCPA, and SOC2 compliant
- Optional: Participate in federated learning (share insights, not data)
Performance Requirements
- Real-time analysis: < 100ms per event
- Batch analysis: Process 1M+ events per hour
- Model updates: Weekly automatic retraining
- Accuracy: > 95% detection rate, < 2% false positives
Implementation Roadmap
Phase 1: Foundation (Q2 2025)
- Deploy behavioral analytics engine
- Implement basic anomaly detection
- Launch user behavior profiling
- Beta program with 50 customers
Phase 2: Intelligence (Q3 2025)
- Activate global threat database
- Add predictive capabilities
- Implement automated responses
- Expand beta to 200 customers
Phase 3: Advanced Features (Q4 2025)
- Launch Security Copilot AI assistant
- Add natural language query interface
- Implement attack chain analysis
- General availability release
Phase 4: Ecosystem (Q1 2026)
- Integrate with SIEM platforms
- API for security tool partners
- Custom model training for enterprises
- Advanced compliance frameworks
Pricing Model
Included in Enterprise Plan: Basic AI threat detection
Security AI Add-on: $5/device/month for advanced features
Volume Discounts: Available for 1000+ devices
Beta Customers: Free during beta + 50% off first year after GA
Success Metrics
We'll measure success by:
- Detection Rate: > 95% of threats caught
- False Positive Rate: < 2%
- Response Time: < 1 minute from detection to action
- Incidents Prevented: Track near-misses and proactive blocks
- Security Team Efficiency: Reduce alert investigation time by 70%
FAQ
Q: Will this replace our current security tools?
A: No, it complements existing tools by adding an AI layer on top of your MDM security.
Q: What data does the AI use?
A: Device telemetry, network logs, app usage, system events - all processed securely.
Q: Can we customize the AI models?
A: Enterprise customers can train custom models on their specific environment.
Q: How does it handle false positives?
A: Security teams can provide feedback to continuously improve accuracy.
Q: Is this available for all platforms?
A: Initially iOS and Android, Windows and macOS support in Phase 2.
Get Involved
🎯 Early Access Interest: Sign up here
💬 Feedback & Suggestions: Community Forum
📊 Technical Whitepaper: Coming January 2025
🎥 Demo Video: Watch Preview
Research Partner: MIT Computer Science & AI Lab
Advisory Board: 12 CISOs from Fortune 500 companies
Investment: $15M dedicated to AI security R&D
Expected ROI: 10x reduction in security incident costs