Anomaly Detection

Use AI to detect unusual data patterns for early issue identification and operational improvement.

Business impact

• Network uptime — Detecting anomalies reduces downtime and maintains continuous network availability
• Fraud detection rate — Identifying unusual patterns improves accuracy in spotting fraudulent activities
• Operational efficiency — Automated anomaly alerts streamline issue resolution and resource allocation
• Mean time to recovery (MTTR) — Faster anomaly detection shortens incident response and recovery times
• Bandwidth usage — Filtering anomalies reduces unnecessary data transmission and optimizes bandwidth
• Detection accuracy — Improved models increase true positive rates and reduce false alarms
• Customer satisfaction — Proactive anomaly management enhances service reliability and user experience

Data requirements

• Network telemetry data (Numeric) — Used to monitor real-time network behavior and detect deviations
• Sensor and IoT device logs (Structured) — Provide continuous streams for anomaly pattern recognition
• Video and image feeds (Image) — Enable visual anomaly detection in security and manufacturing
• Transaction records (Structured) — Analyze financial and operational transactions for fraud detection
• Time-series system metrics (Numeric) — Track performance trends to identify unusual spikes or drops
• Textual logs and alerts (Text) — Extract insights from system logs and error messages

AI methods and techniques

• Predictive AI — Forecast normal behavior and flag deviations as anomalies
• Generative AI — Model normal data distributions to detect outliers effectively
• Symbolic AI — Incorporate rule-based logic to complement statistical anomaly detection

AI models and model families

GPT-3.5, Llama-2, Isolation Forest, LSTM, Convolutional Autoencoder, GAN, SigLLM

Industries

Real-world evidence

13 documented case studies on record.

Companies using this: Agent Guard, Argonne National Laboratory, Cummins, Facebook, Go Labs, Haiqu, Keysight Technologies, MIT, MIT Data AI Lab, Media Monks, Microsoft, R Space Systems, Twitter.