A Security Analytics solution is a proactive form of security that benefits organizations. Learn how it is used and the specific advantages of Gurucul’s platform.
In this blog post we go into detail about the organizational benefits of security analytics solutions. The discipline of security analytics emerged to take event and activity data that has been collected from across an organization’s total computing environment and analyze it for threat detection and security monitoring. It does this by combining big data capabilities with threat intelligence to help detect, analyze, and mitigate insider threats, as well as persistent cyber threats and targeted attacks from external bad actors. Security analytics provides real-time visibility, automated contextual detection, prioritized investigation, and risk-driven response.
Organizations benefit in many ways from the use of a security analytics solution, as detailed in the sections below. Security analytics is a proactive form of cybersecurity, meaning the security team can detect and mitigate legitimate threats before they can do significant damage. Automated remediation reduces delays in shutting down or eliminating threats. Security analytics tools can help an organization comply with government and industry regulations by offering a unified view of data events, particularly around regulated data. Moreover, these types of solutions help security teams be more efficient by automatically analyzing huge volumes of data, gathering contextual information, and prioritizing investigations.
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What is Cyber Security Analytics?
Cyber security analytics combines big data capabilities with threat intelligence to help detect, analyze, and mitigate insider threats, as well as persistent cyber threats and targeted attacks from external bad actors. It provides real-time visibility, automated contextual detection, prioritized investigation, and risk-driven response.
Security Analytics Solutions – What are they used for?
Security analytics solutions are used for monitoring, analyzing, and detecting security threats and risks within an organization’s network infrastructure, systems, applications, and data. These solutions leverage advanced analytics techniques, machine learning algorithms, artificial intelligence, and data visualization tools to gain insights into security events, identify patterns, and detect anomalies that may indicate potential security breaches or malicious activities.
Some specific use cases are:
- Threat detection and prevention
- Incident response and investigation
- Log and event management
- Employee monitoring
- Identifying compromised accounts
- Demonstrating compliance with regulatory requirements
- Security posture assessment
Collecting, Preparing, and Aggregating Security Data
Relevant security data is collected from various sources such as log files, network traffic, system events, threat intelligence feeds, and security sensors. This data can be obtained from different security tools, devices, and platforms deployed within an organization’s infrastructure—including cloud. In addition, contextual data such as asset metadata, geo-location, and data from non-IT systems (such as human resources) may be collected.
The collected data needs to be cleansed, normalized, and transformed to ensure consistency and usability. This involves removing noise, filtering irrelevant information, and converting data into a consistent format suitable for analysis. Data preparation also includes activities like data deduplication, data integration, and data enrichment with contextual information.
Then, data is aggregated from multiple sources to create a consolidated dataset. Aggregation enables a broader view of the security landscape and allows for comprehensive analysis. It involves merging data from different log sources, network devices, and other security systems into a centralized repository or a security information and event management (SIEM) system.
Analyzing The Data Collection
Once the data is prepared and aggregated, it can be explored and visualized to gain insights. Data exploration involves applying various analytical techniques, such as statistical analysis, data mining, and/or machine learning algorithms, to uncover patterns, correlations, and anomalies within the data. Visualization techniques such as charts, graphs, and dashboards are used to represent the findings in a visual format for easier interpretation.
During the analysis phase, security analysts apply techniques to identify potential threats and security incidents. This involves using predefined rules, signatures, and/or machine learning models to detect known or potential patterns of attacks, as well as anomaly detection methods to identify suspicious or abnormal behavior. When a potential threat or incident is detected, the incident response process is triggered, which involves investigating, containing, mitigating, and remediating the incident.
Throughout the analysis phase, security analysts leverage their expertise, knowledge of threat landscapes, and understanding of the organization’s security context to interpret the results and make informed decisions.
Reporting After The Data Collection
The analysis phase culminates in the generation of reports and communication of findings. Security analysts prepare reports that summarize the insights gained from the analysis, including identified threats, vulnerabilities, and recommended actions. These reports are shared with stakeholders such as IT teams, management, or incident response teams to facilitate informed decision-making and prioritize security efforts.
Predict
Predictive capabilities in cyber security analytics serve several use cases:
Threat Detection – By analyzing historical data and current security events, predictive models can identify patterns and indicators of compromise to detect potential threats in real-time.
Anomaly Detection – By establishing baseline patterns of normal behavior, any deviations can be flagged as potential security incidents. This helps in detecting sophisticated attacks or insider threats that may go unnoticed by traditional security measures.
Risk Assessment – By analyzing historical data and security incidents, predictive analytics can assess the risk associated with specific vulnerabilities or configurations. This enables organizations to prioritize their security efforts and allocate resources effectively to mitigate potential risks before they are exploited.
How Can Organizations Benefit from Security Analytics?
Organizations can benefit from security analytics solutions in several ways. Here are some key advantages:
Predictive and Proactive Security
By leveraging advanced analytics techniques, security analytics solutions can predict and anticipate potential security threats and risks. Machine learning algorithms can analyze historical data and identify patterns that indicate future attacks. This enables organizations to implement proactive security measures, such as patching vulnerabilities, strengthening access controls, and implementing security best practices, before an actual breach occurs.
Threat Detection and Prevention
Security analytics solutions can detect and prevent a wide range of threats, including malware infections, network intrusions, insider threats, and other suspicious activities. By analyzing large volumes of security data in real-time, these solutions can identify patterns, anomalies, and indicators of compromise that may go unnoticed by traditional security measures. This early detection helps organizations respond promptly and prevent potential security breaches.
Improved Incident Response
Security analytics solutions provide valuable insights and contextual information during incident response efforts. By correlating and analyzing security events and logs from various sources, these solutions help security teams understand the scope and severity of incidents. This leads to more effective incident investigation, containment, and mitigation, minimizing the impact of security breaches and reducing downtime.
Enhanced Incident Investigation
When a security incident occurs, security analytics solutions can aid in investigation and forensic analysis. By correlating data from multiple sources and applying advanced analytics techniques, these solutions can provide valuable insights into the nature and scope of an incident. This helps security teams in understanding the root cause, determining the extent of the breach, and supporting legal or compliance requirements.
Enhanced Visibility and Situational Awareness
Security analytics solutions offer comprehensive visibility into an organization’s security posture. By aggregating and analyzing data from multiple sources, these solutions provide a holistic view of the security landscape, including vulnerabilities, threats, and ongoing security events. This increased visibility allows organizations to identify weak points, prioritize security measures, and make informed decisions to improve their overall security posture.
Compliance and Regulatory Requirements
Security analytics solutions help organizations meet compliance and regulatory requirements by providing continuous monitoring and reporting capabilities. These solutions offer a unified view of data events, particularly around regulated data such as health information records (HIPAA), personally identifiable information (GDPR), and payment card data (PCI DSS). Moreover, they can generate audit logs, compliance reports, and security metrics that demonstrate adherence to security standards. This assists organizations in fulfilling their obligations, passing audits, and maintaining a secure environment.
Operational Efficiency and Cost Savings
Security analytics solutions automate data analysis and provide actionable insights, saving time and effort for security teams. By identifying and prioritizing security events, these solutions help focus resources on critical issues, reducing false positives and minimizing response time. This increased efficiency translates into cost savings by optimizing resource utilization and reducing the potential financial impact of security incidents.
Connecting The Cyber Security Dots with The Gurucul Security Analytics and Operations Platform
Gurucul has created a purpose-built, cloud-native Security Analytics and Operations Platform that scales with the business and empowers security analysts. With a consolidated set of capabilities, the platform helps to automate tasks beyond just collection and correlation and provides a full set of capabilities for threat detection, investigation, and response (TDIR). The platform is optimized to ingest as much data as possible, applying a wide area of analytics and using true machine learning/artificial intelligence to adapt and learn to newer threats.
Gurucul provides the most comprehensive set of security analytics, out-of-the-box threat content, a trained (not rule-based) machine learning engine, the highest number of machine learning models, and a full enterprise risk engine. This empowers security teams to confidently take on the evolving threat landscape with a unified and analytics-driven approach. Our cloud-native platform and solutions are built to lower deployment, management, and operational costs while preventing damage to business due to cyber-attacks.
Gurucul’s flexible platform can be deployed with all our capabilities or be packaged into solutions that are best suited to meet the needs of your security operations. We also have a large group of supported integrations across a full spectrum of security operations tools for ease-of-deployment and immediate time-to-value.
Among the many benefits are:
Reduced Costs and Operational Efficiencies
Gurucul’s platform helps organizations to drastically reduce overall operational expenses while improving the efficiency of TDIR programs across the board. The platform supports more data ingestion that is currently inhibited by licensing costs, heavy customization for new data sources, storage limitations, and poor analytics leading to too many alerts and false positives. The platform helps to reduce threat detection time from weeks or months to minutes or hours through automation. Using Gurucul’s security analytics, organizations can reduce manual effort through automation of tasks and prioritization of remediation actions with a rich level of context. There is also the opportunity to improve analyst efficiency and accelerate training through open and transparent models, gathered context, and clear response recommendations leading to improved TCO. And, organizations can reduce capital expenditures and operating expenditures with predictable, asset-based licensing.
Proactive Security
Gurucul’s security analytics platform quickly identifies and addresses new, emerging, and unknown threats that evade most signature-based solutions and rely on updates which could take weeks or months to develop. Security analysts can hunt for threats before they become serious incidents.
Automated Response
The platform enables automated response, based on understanding overall risk, with targeted and dynamically created playbooks, which consider users, identity, network, and other context to accelerate remediation steps cross-functionally. This vastly speeds up the process of mitigating a serious threat.
Conclusion
Cyber security analytics is a must-have component of a modern-day threat detection and response system. It is the most efficient and effective way to detect and quickly respond to threats. Organizations benefit from security analytics in so many ways—with operational efficiency and cost savings, continuous monitoring and reporting, validation of compliance with regulatory requirements, enhanced visibility and situational awareness, and much more.
Learn More
Gurucul’s industry-leading Security Analytics and Operations Platform empowers security teams to confidently and holistically address the ever-evolving threat landscape.
FAQs
What are security analytics solutions used for?
Security analytics solutions are used for monitoring, analyzing, and detecting security threats and risks within an organization’s network infrastructure, systems, applications, and data. These solutions leverage advanced analytics techniques, machine learning algorithms, and data visualization tools to gain insights into security events, identify patterns, and detect anomalies that may indicate potential security breaches or malicious activities.
What are examples of security analytics?
An example of security analytics is the use of machine learning algorithms to analyze network traffic data in order to detect and prevent cybersecurity threats. This involves collecting and analyzing large volumes of data from various sources such as network logs, firewall logs, and intrusion detection system alerts.
Through the use of advanced data analytics techniques such as anomaly detection, clustering, and pattern recognition, security analysts can identify abnormal behavior and potential security breaches. This information can then be used to proactively respond to security threats and prevent them from causing harm to the organization’s systems and data.
Another example of security analytics is the analysis of user behavior to detect potential insider threats. By monitoring user activity, security analysts can identify unusual patterns of behavior that may indicate an employee is engaging in malicious activities such as data theft or sabotage. This can help organizations detect and prevent insider threats before they cause significant damage.
How can real-time security analytics be used for threat detection?
Security analytics solutions can detect and prevent a wide range of threats, including malware infections, network intrusions, insider threats, and other suspicious activities. By analyzing large volumes of security data in real-time, these solutions can identify patterns, anomalies, and indicators of compromise that may go unnoticed by traditional security measures. This early detection helps organizations respond promptly and prevent potential security breaches.
Why are data analytics well-suited for security organizations?
Data analytics provides security organizations with the ability to handle large volumes of data, detect complex patterns that may be indicative of threats, and make informed decisions in real-time. By leveraging data analytics techniques, security organizations can enhance their threat detection capabilities, allocate resources efficiently, and continuously improve their security posture in the face of evolving threats.
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About The Author
Nilesh Dherange, Chief Technology Officer, Gurucul
Nilesh Dherange is responsible for development and execution of Gurucul’s technology vision. Nilesh brings a wealth of experience in inventing, designing, and building software from inception to release. Nilesh has been a technologist and leader at three startups and at one of the largest software development companies in the world. Prior to founding Gurucul, Nilesh was an integral member of a company that built a Roles and Compliance product acquired by Sun Microsystems. Nilesh was also a co-founder and VP of Engineering for BON Marketing Group where he conceptualized and created BON Ticker — an innovative patented bid management system which used predictive analytics to determine advertising bids for PPC marketing campaigns on search engines like Google, Yahoo, MSN etc. Nilesh holds a B.A in Social Science, B.E in Computer Engineering from University of Mumbai and M.S in Computer Science from University of Southern California.