Cybersecurity

and Compliance

Barclays Bank Payment Gateway Failure (Jan 2025)

 

What happened:

Barclays experienced a three-day outage in its payment gateway system, right during the UK’s self-assessment tax deadline. The failure stemmed from undetected performance degradation in backend services.

 

Impact:

  • Over 50% of transactions failed, causing widespread public outrage.

  • Barclays paid $6.6 million in compensation for customer distress.

  • SLA breaches triggered contractual penalties and regulatory scrutiny.

 

How AI-Powered SLA Monitoring Could Help:

  • Proactive alerting would have identified transaction failures before they spiked.

  • Custom SLO tracking could have flagged critical business transactions at risk.

  • Predictive analytics would have enabled preemptive scaling or rerouting to avoid downtime.

Slack’s Global Outage (Jan 2021)

 

What happened:

Slack, the widely used corporate communication platform, suffered a global outage that lasted nearly five hours. The root cause was a server scaling issue that degraded performance silently before triggering a full-blown outage.

 

Impact:

  • Millions of users were unable to communicate during critical business hours.

  • SLA violations went unnoticed until users began reporting issues.

  • Slack faced reputational damage and potential financial penalties due to service disruption.

 

How AI-Powered SLA Monitoring Could Help:

  • Real-time risk scoring would have flagged the scaling bottleneck early.

  • Predictive alerts could have warned ops teams few hours before the crash.

  • Automated compliance tracking would have ensured visibility into SLA thresholds being breached silently.

GET A TRIAL

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.

See beyond your infrastructure with complete visibility into external dependencies and partner behavior that can silently undermine your platform.

Eliminate threshold and rule management, self-learning models automatically adapt to your unique architecture, traffic patterns, and seasonal rhythms.

Zero-Touch Anomaly Detection

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: 

With our zero-touch anomaly detection platform, operations teams transform monitoring into business protection:

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.