Cybersecurity
and Compliance
Transform Emergency Response Into Disaster Prevention
Stop reacting to crises and start preventing them. By integrating advanced machine learning, time series forecasting, and multimodal correlation analysis with real-time trace data, our solution transforms system reliability from reactive crisis management into proactive failure prevention. Beemon delivers in advance warnings of critical system failures.
Our industry-first catastrophe prevention engine learns the unique degradation fingerprints of your specific architecture through deep analysis of historical trace data. Time series forecasting reveals how your system's behavior patterns predict future failures. Multi-dimensional correlation analysis connects seemingly unrelated metrics to predict cascade failures before they form. The AI understands your system so deeply that it can see problems developing long before traditional monitoring can even detect symptoms.
The AI doesn't just see problems-it predicts exactly how they'll propagate through your system and when they'll reach critical impact. By understanding service relationships, timing dependencies, and failure cascade patterns specific to your architecture, we forecast not just that something will fail, but how that failure will spread and what the consequences will be.
When degradation patterns emerge, your team receives early warnings with confidence scores and recommended actions. Armed with this foresight, you implement preventive measures-automatic failover, graceful degradation, or proactive scaling-that stop crises before they form. Customers experience zero impact because the disaster never materializes. Your engineering team becomes renowned not for fighting fires, but for preventing them entirely, building systems that never fail when it matters most.
Predictive Catastrophe Prevention Engine
Learn and leverage the unique degradation fingerprints for your platform.
Shift from reactive crisis management to proactive prevention, see tomorrow's problems today.
Act with confidence on early warnings that come with recommended actions-automatic failover, graceful degradation, or proactive scaling.
Prevent major outages at the source by understanding cascade propagation patterns specific to your architecture.
Analyzes historical patterns to predict future system behavior.
With our predictive catastrophe prevention platform, teams can:
Amazon Web Services (AWS) Outage (Dec 2021)
What happened:
AWS’s US-East-1 region suffered a major outage that disrupted major customers including Netflix, Disney+, Slack, and Coinbase. The failure originated from unexpected behavior in an internal network device, which cascaded into widespread service degradation across EC2, Lambda, and DynamoDB.
Impact:
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Monitoring tools became unreachable, leaving teams blind to the root cause.
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Latency spiked and throughput dropped across multiple core services.
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Engineers spent hours in emergency recovery, while customers faced massive downtime and financial losses.
How Predictive Catastrophe Prevention Could Help:
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Cascade failure forecasting could have modeled inter-service dependencies and identified the faulty network device as a high-risk anomaly before failure.
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Latency and throughput prediction would have flagged early congestion patterns in internal traffic routing.
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Proactive intervention could have isolated the fault within minutes-preventing cascading impact and preserving service continuity.
CrowdStrike Falcon Update Outage (Jul 2024)
What happened:
A routine update to CrowdStrike’s Falcon cybersecurity platform caused widespread Blue Screen of Death (BSOD) errors on millions of Windows systems worldwide. The bug conflicted with kernel-level operations, crippling hospitals, banks, airlines, and government systems.
Impact:
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Global Windows devices crashed simultaneously, halting critical infrastructure.
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IT and emergency teams were overwhelmed by mass system failures.
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The update lacked staged rollout, anomaly detection, and early rollback triggers.
How Predictive Catastrophe Prevention Could Help:
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Latency spike prediction could have detected abnormal system behavior during the initial deployment phase.
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Cross-modal correlation would have linked the faulty update to early crash telemetry and user reports.
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Cascade failure forecasting could have modeled how endpoint failures would disrupt essential services-enabling rollback before global impact.
See tomorrow's disasters today and prevent them before they materialize-predictive intelligence that delivers advance warnings with confidence scores and recommended actions, so your customers never experience downtime.
Real world examples where Beemon could make a difference:
Marketing department
Headquarters