How Enterprises Can Use AI to Detect Unusual Patterns in Data
AI-driven anomaly detection is a powerful technique your can gain insights on in our JUNO INNOVATE lab, that can be used by enterprises to detect unusual patterns in data, which can indicate fraud, security breaches, cloud failures, or other problems. By using AI to identify anomalies, enterprises can take corrective action early on, before they cause serious damage.
How AI-Driven Anomaly Detection Works
AI-driven anomaly detection works by using machine learning algorithms to learn the normal patterns in a given dataset. Once the algorithms have learned the normal patterns, they can be used to identify any data points that deviate significantly from the norm. These data points are considered anomalies and may indicate a problem.
Benefits of AI-Driven Anomaly Detection
AI-driven anomaly detection can be used to detect suspicious activity, such as unauthorized access to systems or data, malware infections, and denial-of-service attacks.
AI-driven anomaly detection can be used to detect fraudulent transactions, such as credit card fraud and insurance fraud.
AI-driven anomaly detection can be used to detect and prevent problems with cloud infrastructure and other IT systems.
AI-driven anomaly detection can automate the process of detecting and investigating anomalies, freeing up staff to focus on other tasks.
Fraud detection in Banking
Start a free trail in JUNO INNOVATE to explore how AI-driven anomaly detection can be used to detect fraudulent transactions in real time. One of our lab pre-define model will show you how a bank can use AI to monitor credit card transactions for unusual patterns, such as a sudden spike in spending or a large purchase made in a foreign country. If the AI detects an anomaly, it can flag the transaction for review by a human analyst.
Security breach detection in Tech
Get better insights into your cyber security by implementing one of our JUNO INNOVATE use cases that models AI-driven anomaly detection to detect security breaches by monitoring network traffic and system logs for unusual activity. For example, an AI system can be used to detect a sudden increase in failed login attempts or a large volume of data being transferred out of the network. If the AI detects an anomaly, it can alert security personnel to investigate.
Cloud failure detection
Similar to our JUNO INNOVATE preventative maintenance use case for cloud, AI-driven anomaly detection can be used to detect cloud failures by monitoring performance metrics such as CPU usage, memory usage, and network latency. For example, an AI system can be used to detect a sudden spike in CPU usage or a decrease in network latency. If the AI detects an anomaly, it can alert IT personnel to investigate and take corrective action.
By using AI to identify anomalies in data, enterprises can take corrective action early on, before they cause serious damage. Accelerate your AI-enablement with how to guides for these scenarios within our JUNO INNOVATE lab. Together, we can revolutionize the digital and AI landscape to make new possibilities in our world. Start your use case and utilize one of our pre-built models now.