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An Introduction to Security Analytics
Security analytics uses ML and big data analytics to prevent threats. Learn how you can connect the dots with DNIF HYPERCLOUD.
Enterprises cannot predict the future, particularly when security threats are involved. Fortunately, there are plenty of security analytics tools such as UEBA to analyze threat events and potentially detect a threat before it impacts your critical infrastructure.
In this blog, we will talk about “security analytics” and understand the benefits of data analytics for security.
What is security analytics?
Security analytics is a proactive approach that utilizes machine learning and big data analytics to collect, categorize and analyze data to detect and prevent advanced threats. Machine learning has an important role in modern security and enables threat analysis and hunting.
Why security analytics?
Cyberattacks are becoming increasingly sophisticated and difficult to detect. Indicators or signals can be scattered across different data sources and may not always provide context to the security team. Due to this, cyberattacks can sometimes be undetected for months.
Security analysts provide enterprises with the much-needed visibility into attack techniques such as data exfiltration, compromised credentials, lateral movement, and so on. It helps with the early detection of attackers by checking for insider threat behaviors. Did you know that as per Goldstein, 60% of data breaches are caused by insider threats? Security analysts can provide information to your cybersecurity ecosystem and allow other systems to act on suspicious activities.
Benefits of data analytics for security
Unlike traditional security systems, security analytics can analyse large amounts of data in real-time for security teams. Here are some advantages of data analytics:
Maintaining regulatory compliance: Security analytics also helps an enterprise in adhering to industry and regulatory best practices and regulations, such as PCI DSS, GDPR, HIPAA, etc. It can monitor access and authentication, help with insider threat detection and collect log data for auditing.
Proactive approach: Security analysts correlate events in near real-time based on combined data from logs and other sources to identify indicators of suspicious activity. The result is proactive security that helps organizations detect threats early in the attack chain and improve response time.
Improved forensic capabilities: Big data security analytics is especially useful for forensic investigations. It provides insights into the source of the attack, how the attack occurred, and the extent of the damage. By revealing existing breaches, security teams can determine when an attack was launched and create an accurate attack timeline.
Big data analytics and machine learning in security
By using advanced statistical and data science models, big data analytics detects anomalies in real time for threat analysis. Security alerts are generated and combined with additional forensic data to detect and respond to cyberthreats. They also predict security attacks and create baselines for what ‘normal’ activity is. Security teams can then understand the end-to-end impact.
Machine learning identifies patterns against data sets to detect known and unknown patterns with a high degree of accuracy.
Connect the dots with DNIF HYPERCLOUD
DNIF is designed to reduce pressures at the security operations centre, from administration to incident response. The solution helps you uncover campaigns by continuously finding and building connections between entities seen in monitored telemetry helping you connect the dots and uncover complex threats faster. Connecting signals provide more context, reduces the alert pressure and allows analysts to make decisions faster.
Tools with machine learning and data analytics help the security team identify suspicious activities, detect threats, and monitor user behaviour in real time. This aids enterprises in keeping up with the growing threat of security attacks and formulating a comprehensive detection and prevention plan.