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.

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Replace hours of manual investigation with automatic root cause discovery that connects causes and effects across all your data.

Understand complete failure stories by detecting anomalies that span infrastructure, application, and business layers as interconnected events.

Stop manually correlating different data sources, our AI automatically learns and maintains relationships across all modalities.

Break down team silos with unified visibility that speaks the same language across DevOps, SRE, application, and business teams.

Uncover hidden root causes through automatic correlation of metrics, traces, logs, code changes and more, revealing causal chains no single data type could expose.

Multimodal Knowledge Graph

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.

Real world examples where Beemon could make a difference: 

With our multimodal knowledge graph platform, Support, DevOps and SRE teams gain the power to:

Unified System Intelligence Across All Data Types

Stop drowning in fragmented data. Connect it all to complete understanding. Your operations data tells a complete story, but only if all the pieces connect. Harness the power of multimodal AI to transform how you understand system failures. By integrating Graph Neural Networks with OpenTelemetry's unified data collection, our solution automatically learns the complex relationships between traces, metrics, logs, code changes, and infrastructure events.

 

Our Graph Neural Network-powered platform automatically unifies all your observability data into one intelligent knowledge graph, revealing cause-and-effect relationships that no single data type can expose. Our graph-based platform eliminates the painful manual correlation work that operations teams waste hours on daily. Instead, the AI model automatically discovers how a metric spike in one service triggers latency in another, which cascades into trace failures and ultimately shows up as errors in logs.

 

These cross-layer patterns paint a broader story that predicts failures before they cascade, uncovering root causes that span infrastructure, application, and business domains. Operations teams no longer investigate in isolation. The result: root causes that span infrastructure to application to business impact, detected automatically and understood completely without manual intervention or static threshold rules.