Table of Contents
Big data analytics and machine learning are used in security analytics, a proactive security strategy, to collect, classify, and examine data from network devices to identify sophisticated threats.
These systems gather information from various sources, including endpoint and user activity data, business applications, outside sources of threat intelligence, and contextual data from non-IT sources. Modern Cybersecurity relies heavily on machine learning technology, which enables almost real-time data and threat analysis.
An approach to Cybersecurity known as security analytics focuses on data analysis to create preventative security measures. For example, network traffic monitoring could be utilized to spot signs of compromise before a danger materializes. This article covers detailed knowledge of Security Analytics.
Factors Essential for the Development of Security Analytics
The following factors are essential for the development of security analytics:
Transitioning from Protection to Detection
Hackers employ various attack techniques that take advantage of numerous weaknesses. Some hazards may go months without being noticed. Security analytics systems can monitor typical attack trends and inform users as soon as an unusual one is found.
A Unified View of the Enterprise
Security analytics delivers both a real-time and historical picture of occurrences by structuring data in this manner. Giving users a consolidated view of risks and security breaches from a single interface enables better planning, quicker problem-solving, and more informed decision-making.
Seeing Results and a Return on Investment
IT teams are under increasing pressure to share results with senior management and stakeholders. Analysts can swiftly identify risks and respond to security breaches due to security analytics’ time-to-resolution metrics and lower number of false positives.
What is a Security Analytics Solution?
Applications for security analytics employ both historical and real-time data to identify dangers. Several sources of data are:
- Workstation, server, sensor, mobile device, and other endpoint real-time alerts
- Live updates from various IT security programs (firewalls, intrusion prevention, endpoint detection, and response, etc.)
- Network traffic kinds and volume
- Client logs
- Threat intelligence feeds from third parties
- Data from diverse sources is combined in security analytics, which then searches the data for correlations and anomalies.
Various techniques may be used by a security analytics tool to analyze the data. These include conventional rule-based methods as well as machine learning and statistical analysis.
Security Analytics Capabilities
Cybersecurity analytics can perform various tasks, including forensic analysis and network monitoring. Among the most typical are:
Analyze Network Traffic
Events are connected, and patterns that suggest a possible attack are found.
Identify Endpoint Threats
The platform employs endpoint threat detection to identify attackers aiming for an organization’s endpoints.
Detects Data Exfiltration
Security analytics prevents unauthorized download or copying of data by restricting unwanted communication routes and prohibiting users from entering their credentials on non-enterprise websites. This also guards against credential theft from phishing assaults.
Help you Stay within Compliance Regulations
The platform assists your business in automating compliance demands, including log data collection, personal data flow management, data activity monitoring, and report compilation, allowing the compliance team to spot compliance infractions.
The Need for Security Analytics
Due to improvements in the methods and strategies used by cyber attackers, who can infiltrate a system in seconds and occasionally remain unnoticed for months, the cybersecurity industry is expanding. Attacks are frequently challenging to identify as they occur quickly, and the symptoms can be spread over various data sources, including network servers, endpoints, and applications.
Security analytics give businesses insight into sophisticated attack methods like data exfiltration, lateral movement, and compromised credentials. Examining user account activity for insider threat behaviors aids in the early detection of attackers in contrast to conventional security technologies. Additionally, security analytics can feed data into the organization’s security ecosystem, enabling other systems to respond to ominous activity.
Benefits of Security Analytics
Tools for security analytics can benefit:
Rapid Detection and Response
Cyberthreat identification and response are accelerated by security analytics. IT can mitigate or prevent the damage that a breach creates by acting quickly.
The demand for security analytics is mainly driven by the requirement to adhere to governmental and commercial standards. Security analysis applications can provide a unified view of all data events occurring. Some conditions call for monitoring and log gathering for auditing and forensics. This offers the capacity to identify and address potential non-compliance and promptly verify compliance.
An IT department can use security analytics to quickly identify possible risks and make sense of the massive amounts of data coming into and out of its network. An organization can avoid a potentially expensive data breach or cyberattack by using a security analytics approach, which offers real-time insight and a historical record of past threats. An efficient security analytics solution is increasingly essential for ensuring the security of an organization’s data and IT systems.
Security Analytics Use Cases
You can make data-driven decisions due to security analytics. You must know the required data and intend to accomplish this efficiently.
As each organization has a unique technological stack, user base, and set of devices, no two are identical. Despite having similar regulations, companies often implement and enforce them differently.
Before looking into or implementing a security analytics technology, you should think about the following:
- Network traffic analysis
- User behavior analytics
- Threat detection
- Data exfiltration
- Insider threat detection
- Incident investigation
Security Analytics vs. SIEM
There are occasions when security analytics and Security Information and Event Management (SIEM) solutions are compared. Despite some similarities, security analytics and SIEM are not the same. For the purpose of identifying specific events occurring on the devices they are monitoring, SIEM systems gather log data produced by those devices, then aggregate that data. Comparatively, security analytics is a more comprehensive and cloud-friendly approach focused on DevOps CI/CD lifecycles and larger data quantities.
Organizations may detect insider risks, spot suspicious activity, and keep tabs on user behavior in close to real-time by using security big data analytics and machine learning techniques. Due to this, security teams are better able to predict attacks, keep up with the sophistication of cyber threats, and thwart threats before they cause harm.