There are two main flows in IBM SmartCloud Analytics - Log Analysis - data ingestion and querying.
The data ingestion flow starts when data is sent to the Receiver interface. The IBM Tivoli Monitoring Log File Agent and an EIF Adapter are provided as components of IBM SmartCloud Analytics - Log Analysis so that log data is analyzed by IBM SmartCloud Analytics - Log Analysis using the Data collector or Receiver interface. IBM SmartCloud Analytics - Log Analysis analyzes data and extracts information about the domain, for example, error codes, IP addresses, timestamps, application / middleware / infrastructure domain specific attributes and so on. The extracted information and any additional raw data that you choose to add is stored in the BigData data store.
The data query flow is triggered by the IBM SmartCloud Analytics - Log Analysis application and is used to run queries. The results are displayed or further processed by the IBM SmartCloud Analytics - Log Analysis application extension. A IBM SmartCloud Analytics - Log Analysis application extension can also query an external data source to link a search result to, for example, say performance data. You can also use IBM SmartCloud Analytics - Log Analysis to query to an external data source or to perform analytics on the returned results using logic supplied by IBM SmartCloud Analytics - Log Analysis or by a IBM SmartCloud Analytics - Log Analysis application extension.
Splunk Enterprise Security is widely used for security operations, including threat detection, incident response, and log monitoring. It centralizes log management, offers security analytics, and ensures compliance, enhancing the overall security posture of organizations.
Companies leverage Splunk Enterprise Security to monitor endpoints, networks, and users, detecting anomalies, brute force attacks, and unauthorized access. They use it for fraud detection, machine learning, and real-time alerts within their SOCs. The platform enhances visibility and correlates data from multiple sources to identify security threats efficiently. Key features include comprehensive dashboards, excellent reporting capabilities, robust log aggregation, and flexible data ingestion. Users appreciate its SIEM capabilities, threat intelligence, risk-based alerting, and correlation searches. Highly scalable and stable, it suits multi-cloud environments, reducing alert volumes and speeding up investigations.
What are the key features?
- Comprehensive Dashboards: Provide a holistic view of security operations.
- Flexible Data Ingestion: Supports various data sources for better analysis.
- Robust Log Aggregation: Centralizes log management for easier monitoring.
- Machine Learning Toolkit: Enhances threat detection and prediction capabilities.
- SIEM Capabilities: Integrates security information and event management.
- Threat Intelligence: Utilizes external information to improve security measures.
- Risk-Based Alerting: Prioritizes alerts based on the risk they pose.
- Correlation Searches: Identifies and correlates security events effectively.
What benefits or ROI should users look for?
- Enhanced Visibility: Improves monitoring across endpoints, networks, and users.
- Faster Incident Response: Speeds up identification and resolution of security issues.
- Reduced Alert Volumes: Lowers false positives and enhances signal-to-noise ratio.
- Scalability: Adapts to growing and changing security needs.
- Improved Security Operations: Integrates multiple security facets into one platform.
Splunk Enterprise Security is implemented across industries like finance, healthcare, and retail. Financial institutions use it for fraud detection and compliance, while healthcare organizations leverage its capabilities to safeguard patient data. Retailers deploy it to protect customer information and ensure secure transactions.
