Security analytics - definition & overview

Security analytics uses data analytics and machine learning techniques to identify and respond to cybersecurity threats in real time.

A security analytics platform is a comprehensive software solution designed to collect, analyze and interpret security-related data to identify and respond to cyber threats. It combines various technologies, techniques, and tools to provide organizations with advanced capabilities for monitoring, detecting, and mitigating security incidents. Here are the primary functions and features of a security analytics platform:

1. Data collection: The platform collects security data from diverse sources such as network devices, servers, endpoints, applications and logs. It aggregates and normalizes this data for analysis and correlation.

2. Log management: The platform provides centralized log management capabilities, allowing organizations to store, index, search and retrieve large volumes of security logs and event data efficiently. It ensures that all security events are captured and can be accessed for analysis.

3. Real-time monitoring: The platform continuously monitors network traffic, system activities and user behavior. It analyzes the incoming data streams to detect anomalies, patterns of malicious behavior and potential security incidents.

4. Threat detection: By employing various techniques, including rules-based detection, statistical analysis, machine learning and behavioral analytics, the platform identifies potential threats and indicators of compromise (IoC). It correlates and analyzes security events and alerts to uncover malicious activities that may go unnoticed individually.

5. Incident investigation: The platform enables security analysts to investigate security incidents thoroughly. It provides tools and capabilities to conduct deep-dive analysis, visualize attack paths, trace back activities and gather evidence.

6. Threat intelligence integration: The platform integrates with external threat intelligence feeds, allowing organizations to enrich their analysis with up-to-date information about known threats, malware signatures, suspicious IP addresses and other indicators of compromise.

7. Visualization and reporting: The platform offers intuitive dashboards, charts and graphs to visualize security data and provide a clear overview of the security posture. It generates reports on security incidents, trends and metrics to facilitate communication with stakeholders, compliance requirements and executive decision-making.

8. Automation and orchestration: The platform automates repetitive security tasks, such as log collection, log analysis and response actions.

9. A security data lake: The platform provides a centralized repository that collects and analyzes large amounts of security data from various sources, offering a complete and historical view of an organization’s security posture.

10. Application security: The platform monitors a company’s software, collecting security and event log data from every infrastructure, application and network supporting the application, to ensure they are not vulnerable or infiltrated by malicious code at any point in the continuous integration/ continuous deployment (CI/CD) process and production cycle.

11. Advanced security analytics platforms often include threat-hunting capabilities. Security analysts can proactively explore the data to search for unknown or advanced threats that may have evaded traditional detection methods.

12. Compliance and auditing: The platform assists organizations in meeting regulatory compliance requirements by providing auditing capabilities, generating compliance reports and supporting incident investigations for compliance purposes.

Overall, a security analytics platform empowers organizations to gain deep visibility into their security posture, detect threats effectively, visualized and respond swiftly to incidents and continuously improve their defenses against cyber attacks. It combines data analysis, threat intelligence, automation and visualization to deliver a comprehensive solution for enhancing cybersecurity.

Cloud security and cybersecurity, while related, are not the same thing. Cybersecurity encompasses all security aspects, including hardware, software, networks, user behavior and policies. On the other hand, cloud security specifically focuses on securing cloud computing environments, including cloud-based infrastructure, platforms and applications.

Unlike cloud security, cybersecurity applies to traditional IT environments, such as data centers and local networks, and computing environments. When it comes to compliance, cloud security often requires organizations to understand and comply with additional cloud-specific standards and regulations. However, cloud security involves a shared responsibility in which the provider is responsible for securing the underlying cloud infrastructure, while the customer is responsible for securing their applications, data and configurations within the cloud environment. And with this shared responsibility comes more complexity than cybersecurity.

In summary, cybersecurity encompasses the broader practice of securing information systems, while cloud security is a subset that specifically addresses the unique security challenges associated with cloud computing environments.

A comprehensive security analytics solution incorporates several key elements to effectively analyze, detect and respond to cyber threats. Here are the essential components of a security analytics solution:

Data collection: The solution should gather data from various sources, such as network logs, system logs, security devices (firewalls, IDS/IPS), endpoint agents and threat intelligence feeds. It should support collecting structured and unstructured data to capture various security events.

Real-time monitoring and analysis: The solution should employ real-time monitoring and analysis techniques to identify threats as they occur. Monitoring logs across your public, hybrid, and multi-cloud environments with real-time analysis enables swift response and reduces attackers' dwell time within the network.

Machine learning and artificial intelligence: Machine learning (ML) and artificial intelligence (AI) algorithms are crucial in security analytics. These techniques can learn from historical data and adapt to evolving threats, improving detection accuracy and reducing false positives. ML/AI can automate repetitive tasks, identify unknown threats and enable predictive analytics for proactive defense.

Threat intelligence integration: Integrating external threat intelligence feeds and sources enhances the analysis capabilities of the solution. It provides up-to-date information on known threats, indicators of compromise (IoCs), malicious IP addresses and attack patterns. Threat intelligence helps identify emerging threats, prioritize alerts and enrich the analysis process.

Visualization and reporting: A security analytics solution should present the analyzed data visually intuitively. Dashboards, charts and graphs enable security analysts to quickly grasp the overall security posture, identify trends, and drill down into specific incidents. Reporting capabilities allow communication with stakeholders, including executives and incident response teams.

These key elements work together to form a comprehensive security analytics solution that enables organizations to detect, investigate and respond to cyber threats effectively, thereby strengthening their overall security posture.

Big data analytics plays a crucial role in cybersecurity due to the increasing volume, variety and velocity of security-related data generated by organizations. Big data analytics is essential in cybersecurity for scaling the analysis of massive data sets.

Big data analytics also enables the identification and detection of insider threats that may go unnoticed by traditional security measures. By integrating external threat intelligence feeds and continuously updating and enriching security analysis with machine learning, big data analytics supports real-time threat intelligence, predictive analytics, and earlier threat detection for improved incident response. All together, big data analytics streamlines identifying compliance violations, performing audits and generating compliance reports.

Security analytics is a crucial component of modern security operations (SecOps), aiming to identify and respond to cyber threats effectively. However, it also faces several challenges that can impact its effectiveness.

Today, data overload can overwhelm security analytics systems. Related is the data quality variability issue, including false positives and a need for more context. This security data often involves sensitive, personally identifiable information subject to compliance regulations. On top of all this is the ever-increasing sophistication of advanced threats and cyber attack techniques in a rapidly evolving technology landscape.

Security analytics can be used for a variety of use cases, including:

Threat detection and response: Enable real-time monitoring and analysis of security events to effectively detect and respond to threats. It helps organizations identify and investigate potential security incidents, such as malware infections, unauthorized access attempts, data exfiltration, or insider threats. Machine learning algorithms, correlation capabilities and threat intelligence integration enhance the detection of advanced threats and enable proactive response.

Log analytics and compliance: Organizations can analyze logs and gain insights into security controls, access logs, user activities and system events. It assists in meeting compliance requirements by providing auditing capabilities, generating compliance reports and facilitating security monitoring for adherence to regulatory frameworks such as PCI-DSS, HIPAA, GDPR, or industry-specific standards.

Security analytics: Secure cloud infrastructure by monitoring and analyzing logs from various cloud services and resources. It assists in identifying misconfigurations, unauthorized changes and vulnerabilities in cloud environments. Organizations can proactively detect and mitigate security risks related to cloud-based storage, compute resources, networking and identity and access management.

Overall, security analytics can be used for a wide range of use cases to improve the security posture of organizations. By providing real-time monitoring and analysis capabilities, security analytics can help detect and respond to security threats more effectively, reduce the risk of data breaches and other cyber attacks and ensure compliance with regulatory requirements and industry standards.

Security Information and Event Management (SIEM) systems can help with security analytics by providing a centralized platform for collecting, storing and analyzing security-related data from across an organization's IT environment. SIEM solutions can help detect and respond to security threats by correlating data from various sources, such as network logs, application logs and system logs, and applying advanced analytics and machine learning algorithms to identify potential threats.

SIEM systems typically provide the following capabilities that can help with security analytics:

1. Log collection and storage - SIEM solutions can collect and store log files from various sources, including network devices, servers and endpoints. This gives security teams a centralized view of security-related events and activities across the organization's IT environment.

2. Correlation and analysis - SIEM solutions can correlate data from various sources and analyze it to identify potential security threats. This can include detecting suspicious network activity, identifying malware infections and detecting unauthorized access attempts.

3. Alerting and reporting - SIEM solutions can generate alerts and reports when potential security threats are detected. This allows security teams to respond quickly and effectively to security incidents.

4. Incident management - SIEM solutions can help with incident management by providing tools to investigate and remediate security incidents. This can include providing detailed information on the scope and impact of a security incident and facilitating collaboration between different teams and stakeholders.
   

Overall, SIEM systems can play an important role in security analytics by providing a centralized platform for collecting, analyzing and responding to security-related data from across an organization's IT environment. By leveraging advanced analytics and machine learning algorithms, SIEM solutions can help security teams identify potential security threats and respond quickly and effectively.

Sumo Logic, a cloud-based log management and analytics platform, offers security analytics that gives organizations insights and visibility into their cloud security posture. These dashboards help monitor and analyze security events, detect anomalies and facilitate incident response in cloud environments. Learn more about Sumo Logic’s security analytics solution.