Transform 45,000+ daily security alerts into 950 actionable threat intelligence reports with 97.9% noise reduction. Advanced HMM-based cybersecurity intelligence platform.
Threat Intelligence
950 Correlated Sessions, Attack Progression TrackingHMM Analysis Engine
Hidden Markov Models, Bayesian Probability ScoringRaw Security Events
45,000+ Daily Alerts, Microsoft Defender IntegrationTransforming overwhelming alert volumes into actionable intelligence
Current Reality: Security teams are drowning in alerts, spending entire shifts on triage instead of threat hunting. Critical attacks remain hidden among thousands of routine alerts, while regulatory requirements like SEBI guidelines demand enhanced monitoring capabilities.
BayesianShield Innovation: Mathematical approach using Hidden Markov Models to transform raw alerts into contextual threat intelligence. Analysts focus on 618 high-priority sessions instead of 45,000 alerts, achieving complete attack visibility with mathematical prioritization.
Hidden Markov Models for Cybersecurity Intelligence
Models attack progressions through phases: Normal Operations → Reconnaissance → Initial Access → Lateral Movement → Objective Execution. Learns probabilistic transitions between states from real-world data.
Groups related alerts into coherent attack narratives. Achieves 47:1 compression ratio (45,000 → 950 sessions) while maintaining full attack context and complete forensic timeline.
Probabilistic threat confidence (0–100%) with confidence intervals. Enables risk-based investigation prioritization and mathematical threat ranking for optimal resource allocation.
Identifies complex timing patterns like off-hours activities, burst behaviors, and persistence indicators. Analyzes across process, network, and file dimensions for comprehensive threat detection.
Real data processing capabilities with Microsoft Defender integration
Beyond traditional rule-based systems with sophisticated mathematical analysis
Security intelligence designed for regulated environments
Enterprise-grade platform with proven Microsoft Defender integration
Production-Ready Components
Expanding Integration Portfolio
Enterprise Architecture
BayesianShield vs Traditional Approaches
| Capability | Traditional SIEM | Generic AI Tools | BayesianShield |
|---|---|---|---|
| Alert Processing | Static rules, alert-by-alert analysis | Black-box models, generic training | Probabilistic learning, session-based grouping |
| Threat Detection | Binary classifications, manual tuning | Poor explainability, static deployment | Continuous probability scoring, self-adapting models |
| False Positives | High (95%+) | Vendor dependency, limited customization | Confidence-based filtering (97.9% reduction) |
| Attack Understanding | Individual alerts | Limited context | Complete attack progressions with HMM paths |
| Mathematical Foundation | Rule-based logic | Proprietary algorithms | Interpretable HMM + Bayesian inference |
| Regulatory Compliance | Manual reporting | Limited audit trails | Mathematical documentation + audit-ready reports |
Advanced prototype ready for financial institution partnerships
Advanced Prototype Achievements:
Financial Institution Collaboration:
Be among the first to deploy next-generation threat intelligence
Co-develop features specific to your environment and needs
Mathematical audit trails and compliance-ready reporting
Expert consultation on Bayesian threat modeling implementation
Join leading financial institutions in pioneering the future of cybersecurity intelligence. BayesianShield is available for partnership deployment with Microsoft Defender integration.