Top 10 Log Analysis Software for Data Security in 2026
Log analysis software collects data from servers, network devices, and applications, then parses it for system administrators to troubleshoot problems and track performance.
Here are the top 10 log analysis software based on my & other users’ experiences and vendor features. Follow the links to see vendors’ best practices:
Choosing the Right Tool Category
Enterprise Monitoring Vendors: These platforms combine infrastructure monitoring, APM, and log analysis in one place. Use them when you need to correlate log data with application performance and infrastructure metrics to identify bottlenecks across your entire stack.
Tools: Dynatrace, New Relic, SolarWinds Observability SaaS, Datadog, Sumo Logic, Coralogix
Log Analysis Specialists: These tools focus on deep log investigation and correlation. Choose these for detailed log forensics without the overhead of complete infrastructure monitoring.
Tools: LogicMonitor LM Logs, Elastic Stack (ELK)
SIEM-Focused Vendors: Security-first platforms that collect logs to detect threats and alert incident response teams. They lack infrastructure monitoring, making them less useful for performance troubleshooting.
Tools: ManageEngine Log360, Splunk Enterprise Security, Graylog
Market presence and feature comparison
See vendor selection criteria.
SOAR: Vendors with (security orchestration, automation, and response) SOAR, and (user and entity behavior analytics).
For more: Top 10+ SOAR software.
Insights (below) come from our experience with these solutions as well as other users’ experiences shared in Gartner 1 , G22 , and TrustRadius3
SolarWinds Observability SaaS
SolarWinds Observability SaaS serves DevOps, IT ops, and Cloud Ops teams. The platform offers:
- Log monitoring
- Application monitoring (specializes in Java applications)
- Kubernetes monitoring
- Network and website monitoring
Database integrations include MongoDB, MySQL, PostgreSQL, and Amazon Aurora.
Users collect log data from websites, network devices, virtual machines, and AWS for centralized visibility.
Source: SolarWinds4
The search box lets you find, filter, and analyze logs. The Logs Explorer displays only logs that match your syntax.
Source: SolarWinds5
For trend analysis, the trends graph reveals patterns in log volume over time.
Source: SolarWinds6
Explore SolarWinds Observability SaaS for DevOps, IT ops, and Cloud Ops teams.
Visit WebsiteManageEngine Log360
Source: ManageEngine7
ManageEngine Log360 handles security center operations through its SIEM capabilities. The platform integrates with help desk software, including Jira Service Desk, ServiceNow, Zendesk, Kayako, and ManageEngine ServiceDesk Plus.
- Threat detection uses data loss prevention (DLP) to catch potential breaches before data leaves your network.
- User and entity behavior analytics (UEBA) monitors log data to establish baseline patterns in your network, then flags deviations from normal behavior.
- Security orchestration, automation, and response (SOAR) conducts incident investigation and response through automated playbooks.
When investigating logs, you can use predefined attack patterns to define custom rules, set time intervals, and apply filters. The forensic analysis feature locates the exact point of attack in your network and identifies which component was exploited.
Source: ManageEngine8
Dynatrace
Dynatrace monitors infrastructure while analyzing log data from 600+ sources, including:
- Native integration with all AWS services
- Familiar log sources: Syslogs, networking, Fluent Bit, FluentD
- API integration for Azure and Google Cloud
The log distribution dashboard provides search and filtering without requiring query language knowledge. Instead of learning SPL or KQL, you can build custom filters through the interface.
Dynatrace includes Davis, an AI assistant that pre-processes JSON logs and lets you search in plain language rather than query syntax. You can type “show me errors from the payment service in the last hour” rather than constructing a complex query.
LogicMonitor LM Logs
Source: LogicMonitor9
LogicMonitor works well for companies with IT systems spread across multiple locations. The LM Envision platform collects and analyzes logs from distributed infrastructure, displaying events and anomalies through keyword search and filtering.
LM Logs monitors and collects data from:
- Networks and servers
- Virtual machines
- SD-WANs
- SaaS platforms
- Websites and databases
During log ingestion, LogicMonitor transforms raw data into structured insights. The platform can:
- Forward Syslog logs using standard TCP protocols across network devices, firewalls, routers, and switches
- Forward Syslog logs from Unix-based systems and Linux servers
- Forward logs from Kubernetes clusters and containers
Datadog
Source: Datadog10
Datadog collects log data from servers, databases, cloud services, containers, and applications. Enterprise DevOps teams use it to monitor infrastructure, apps, and logs while supporting cloud SIEM operations.
Teams create dashboards by dragging and dropping log analytics components. The interface uses auto-tagging and metric correlation to display log data in context. IT operations can assess the percentage of service logs that contain errors, then visualize the results as top lists or time-series graphs.
The “Watchdog Insights” recommendation engine notifies teams when a specific host, service, or log asset shows unusually high errors. This helps on-call engineers investigate incidents involving unfamiliar systems without manually reviewing thousands of log entries.
New Relic
New Relic tracks and analyzes logs from applications, infrastructure, and web browsers. The main logs UI shows all ingested logs and lets you filter them to logs with specific content.
Users gain visibility into application and infrastructure metrics, including logs received, log errors, and error rates by service. The platform correlates log data with application performance metrics, so you can see how spikes in errors affect response times.
Sumo Logic
Source: Sumo Logic11
Sumo Logic combines cloud monitoring, log management, and cloud SIEM in a single platform. The solution provides detailed network insights for compliance, visualizing inbound network activity by host IP for audits.
Security teams can trace requests through your infrastructure to understand attack vectors. The platform maps network flows and highlights unusual patterns that might indicate lateral movement or data exfiltration.
Splunk Enterprise Security
Splunk Enterprise Security collects data from log files and network traffic, then categorizes and saves it in a searchable format. Users write SPL (Search Processing Language) queries to identify events, trends, or anomalies.
You can investigate decoded HTTP requests to find suspicious activity. The example below shows that most access came from the suspicious PHP file “cc8356c82af96ee7994175bb86a8da87.php”:
Source: Coralogix15
Log analysis methods
Log normalization
Log normalization involves converting logs from different formats into a consistent, standardized format. This allows for easier analysis and comparison across several systems and log sources.
For example, correlating access logs with error logs from specific IP addresses could help identify when an error occurs during a specific user’s session. This is especially critical for troubleshooting issues and tracing them to their root cause.
Pattern recognition
Pattern recognition helps identify anomalies or outliers. For example, if a system experiences a sudden traffic spike, pattern recognition could detect this deviation (e.g. a DDoS attack).
Log monitoring
Log monitoring automates the detection of anomalies in logs and provides real-time alerts. For example, log monitoring software could flag unusual login attempts, possibly indicating a brute force attack, or alert administrators to a spike in system errors caused by a software bug.
System performance analysis
System performance analysis examines logs to reveal system performance metrics like CPU usage, memory utilization, and network traffic. For instance, high CPU usage logs could reveal a need for resource optimization or network logs could point to bandwidth bottlenecks.
Vendor selection criteria
- Number of reviews: 100+ total reviews
- Average rating: Above 4.0/5
- Number of employees: 100+
FAQ
Further reading
- Role-based Access Control (RBAC)
- Network Segmentation: 6 Benefits & 8 Best Practices
- AI Application Security: Threats, Vulnerabilities & Real Examples
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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