Cut through the noise. Respond faster. Stay ahead.

Machine learning algorithms can more effectively detect patterns in activities and behaviors that indicate potential threats. AI assists in contextualizing indicators of compromise within the broader cybersecurity landscape for better decision-making. Deep learning models can identify complex attack vectors and suspicious activities that traditional methods might miss. AI aids in the proactive identification of potential threats by continuously monitoring for behavioral anomalies and IoCs.

Cloud infrastructure security is undergoing a significant transformation with the integration of AI. AI enhances threat detection, automates responses to security incidents and strengthens overall cybersecurity measures within cloud environments. By utilizing AI-powered tools like machine learning algorithms, security teams can detect anomalies and potential threats in real time, allowing for proactive mitigation of security risks. Additionally, AI can assist in analyzing vast amounts of security data quickly and accurately, enabling faster incident response and reducing the time to identify and contain security threats.

Some of our classical ML models store customer data in our ML pipelines to optimize performance. For example, our AI-driven alerts feature log anomaly detection and build ML models from 60 days of logs. To accomplish this, we retrain the model once a week. In this example, each week, we add one week of new data while expiring the oldest week of data. Rolling data windows are done to avoid fetching 60 days of data for every training run.

Sumo Logic Copilot also stores customer data in the ML backend to optimize performance. For example, certain Copilot features rely on the history of a customer’s queries. We will expire such data on a rolling window basis.

No. No customer data or PII is used for training or other purposes. All our capabilities serve customer outcomes. Our classic ML capabilities (e.g. AI-driven alerts and its anomaly detection features) create customer-specific models. Sumo Logic Mo Copilot uses a Large Language Model (LLM) served via Amazon Bedrock. As explained in our documentation and included links, no customer data is used for training or other purposes in the case of Sumo Logic Copilot.

Artificial intelligence (AI) enables the automation of data analysis, providing real-time insight, facilitating predictive maintenance, and improving operational efficiency. By leveraging AI technologies such as machine learning and advanced analytics, operational intelligence platforms can process large volumes of data from multiple sources, including historical and real-time data, to generate actionable insights for informed decision-making. AI algorithms can also help identify patterns, trends and anomalies in the data, enabling organizations to optimize their business operations and achieve operational excellence.

Artificial intelligence is crucial in security intelligence because it enhances threat detection, automates response actions and enables predictive analysis of potential threats. AI algorithms can analyze large volumes of data to identify patterns and anomalies, helping security teams detect and respond to cyber threats more efficiently. Additionally, AI technologies can aid in identifying vulnerabilities, predicting security risks and providing actionable intelligence to improve overall cybersecurity posture.