Security analytics is an essential strategy for companies to protect their infrastructure and assets against cybersecurity threats. It involves using data collection, data aggregation, and analysis tools to detect potential threats in real-time, preventing any negative effect on the company’s bottom line. Security analytics combines big data capabilities with threat intelligence to help detect, analyze and mitigate insider threats, persistent cyber threats, and targeted attacks from external bad actors.
The Benefits of Security Analytics
1. Security incident and anomaly detection and response: Security analytics tools are capable of analyzing a wide range of data types, making connections between different events and alerts to detect security incidents or cyber threats in real-time.
2. Regulatory compliance: Security analytics tools help enterprises comply with government and industry regulations, such as the Health Insurance Portability and Accountability Act and Payment Card Industry Data Security Standard. Security analytics software can integrate a variety of data sources, giving organizations a single, unified view of data events across a variety of devices. This enables compliance managers to monitor regulated data and identify potential noncompliance.
3. Enhanced forensics capabilities: Security analytics tools provide companies insights into where attacks originated from, how their systems were compromised, what assets were compromised and whether there was any data loss. These tools can also provide timelines for any incidents. The ability to reconstruct and analyze incidents can help organizations shore up their cybersecurity strategy to prevent similar incidents from happening again.
Security Analytics Tools
Security analytics tools detect behaviors that indicate malicious activity by collecting, normalizing, and analyzing network traffic for threat behavior. Providers that specialize in security analytics offer machine learning tools for applying security models to traffic across a company’s assets. Some of the most popular security analytics tools currently available include WildFire from Palo Alto Networks, Sumo Logic, and Logz.io Security Analytics.
Security Analytics Use Cases
Companies can deploy security analytics for a wide variety of reasons. Some common use cases include analyzing network traffic to detect patterns indicating potential attacks, monitoring user behavior including potentially suspicious activity, detecting potential threats, detecting data exfiltration, monitoring employees, detecting insider threats. Identifying compromised accounts, identifying improper user account usage, such as shared accounts, investigating malicious activity, demonstrating compliance during audits, and investigating cybersecurity incidents.
SIEM vs. Security Analytics
Security information and event management (SIEM) systems collect log data generated by monitored devices to identify specific security-related events occurring on individual machines. They then aggregate this data to determine what’s occurring across an entire system. This enables organizations to identify any variations in expected behavior so they can formulate and implement the necessary responses. Legacy SIEM systems aren’t built to handle modern continuous integration/continuous delivery (CI/CD) lifecycles based on frequent build and deployment cycles. As such, they can’t handle the massive amounts of data these methods generate. Unlike legacy SIEM systems, security analytics takes advantage of cloud-based infrastructure.
Big Data Security Analytics
IT security professionals must ensure that their companies’ systems are secure, that cyber threat risks are kept to a minimum, and that they are complying with data governance regulations. Consequently, one of their primary responsibilities is monitoring and analyzing huge amounts of log and event data from servers, network devices, and applications. Big data security analytics refers to the techniques and strategies used to analyze vast amounts of security data. Big data security analytics can be divided into two functional categories: performance and availability monitoring (PAM) and SIEM. PAM applications focus on managing operations data, while SIEM tools focus on log management, event management, behavioral analysis, database monitoring, and application monitoring.
In conclusion, security analytics is an essential strategy for enterprises to protect their infrastructure and assets against cybersecurity threats. By using security analytics tools, organizations can analyze security events to detect potential threats before they negatively affect the company’s infrastructure and bottom line. Companies can deploy security analytics for various use cases, including detecting insider threats, demonstrating compliance during audits, and investigating cybersecurity incidents. With big data security analytics, IT security professionals can analyze vast amounts of security data while complying with data governance regulations. Security analytics is an essential aspect of a company’s cybersecurity strategy, and organizations should invest in the right tools and best practices to keep their assets and infrastructure secure.