Cybersecurity
and Compliance
Stop Alert Fatigue. Start Business-Focused Intelligence
AI-powered anomaly detection that learns your system's unique patterns and only alerts you to real problems. By integrating Graph Neural Networks with continuous learning models and multimodal data correlation, our solution delivers context-aware anomaly scoring that identifies emerging failures before they impact customers. Focused on reducing false positives and improving detection accuracy.
Unlike traditional monitoring that treats every deviation as an alert, our self-learning platform automatically understands what "normal" means for your specific architecture, traffic patterns, and business rhythms. Instead of treating every metric deviation as equally important, the platform learns your unique baseline and adapts continuously as your system evolves.
Our multimodal approach sees the complete picture! Correlating technical signals with business context to understand exactly what matters to your business. We recognize that even 1% conversion loss equals tens of thousands in daily revenue, that subtle trace latency patterns precede failures by milliseconds, and that technically "acceptable" performance can still devastate user satisfaction. Contextual anomaly scoring reveals why each alert matters to your business and what action to take.
The result: fewer alerts, higher accuracy, and every notification backed by business relevance, not just technical alarm thresholds. Your team gets alerts about problems that matter, not metrics that fluctuate. We flag the issues that drive real business impact while ignoring the noise that doesn't, transforming your monitoring from an exhausting alert firehose into intelligent guidance that protects your business.
Zero-Touch Anomaly Detection
See your complete system story with AI that automatically connects the dots across traces, metrics, logs, code changes, infrastructure and more to reveal hidden root cause stories.
Catch failures before they cascade, Graph Neural Networks automatically learn service relationships and detect subtle warning signs that precede major outages.
Receive actionable alerts backed by business relevance by understanding that 1% conversion loss matters more than a CPU spike.
Connect the dots-every anomaly comes with a contextual explanation showing why it matters to your business and what it means to your users.
Eliminate threshold and rule management, self-learning models automatically adapt to your unique architecture, traffic patterns, and seasonal rhythms.
See beyond your infrastructure with complete visibility into external dependencies and partner behavior that can silently undermine your platform.
With our zero-touch anomaly detection platform, operations teams transform monitoring into business protection:
Facebook’s Global Outage (Oct 2021)
What happened:
Facebook, Instagram, and WhatsApp went offline globally for nearly six hours after a network misconfiguration during routine maintenance disconnected Facebook’s backbone infrastructure. The outage also crippled internal tools, leaving engineers unable to access systems or diagnose the issue quickly.
Impact:
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Billions of users lost access to core communication and social services.
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Internal teams were locked out, delaying incident resolution.
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The company faced major reputational damage and billions in lost ad revenue.
How Zero-Touch Anomaly Detection Could Help:
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Graph Neural Networks could have modeled dependencies across Facebook’s global infrastructure to flag routing anomalies early.
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Context-aware anomaly scoring would have identified the misconfiguration as a high-impact deviation—not a routine network update.
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Self-learning models, independent of the affected network, could have alerted engineers to cascading risks before full-scale failure.
GitHub’s MySQL Routing Outage (Nov 2020)
What happened:
GitHub suffered a major outage impacting Git operations, API requests, GitHub Actions, and Pages. A misconfiguration in the MySQL routing layer caused backend traffic to be misrouted, overloading databases and triggering cascading service degradation that lasted several hours.
Impact:
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Millions of developers were unable to push code, run CI/CD pipelines, or deploy applications.
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Traditional monitoring only revealed latency and failed requests, not the hidden routing anomaly.
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Engineering teams spent hours manually correlating metrics and logs to isolate the root cause.
How Zero-Touch Anomaly Detection Could Help:
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Graph Neural Networks could have mapped service-to-database dependencies in real time to detect abnormal routing patterns.
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Context-aware anomaly scoring would have highlighted the misconfiguration as a high-impact deviation, not just a generic database issue.
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Self-learning models could have pinpointed the root cause autonomously, reducing time to detect and recover without human tuning or thresholds.
Get alerts that actually make sense! Our zero-touch anomaly detection automatically learns what normal means for your system and alerts you only when problems emerge that threaten revenue, user satisfaction, or business continuity.
Real world examples where Beemon could make a difference:
Marketing department
Headquarters