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What is security analytics?
Security analytics detects advanced security threats by combining monitoring data from network devices with big data analytics, artificial intelligence (AI), and machine learning (ML). By gathering, categorizing, and analyzing data points, such as network traffic, security analytics insights enable proactive security measures that go beyond simple threat detection.
With near real-time threat analysis, AI and ML technologies now play a critical role in cybersecurity. These advanced technologies collate data from the following resources, alerting organizations to any anomalous activity.
Business applications usage
External threat intelligence sources
Recent advancements in security analytics include adaptive learning systems that fine-tune detection models based on experience and learnings from authentication behavior and application code, providing smart anomaly detection to alert stakeholders. These technologies accumulate and analyze real-time data that includes:
These forms of data can then be used for both immediate threat response and investigations.
The direct and indirect costs of successfully exploited security breaches are very high, and cybersecurity has become a business-critical consideration. Employees, customers, suppliers, and partners have come to expect an organization’s systems to be highly available and have low tolerance for the slowdowns or downtime that can result from cybersecurity attacks.
Advances in the techniques and tactics of cyber attackers mean that the security sector is growing. The security landscape is further complicated by the growing sophistication of common attack approaches such as phishing, malware, and ransomware. And as many as 98% of cyberattacks today rely on at least some form of social engineering, which is very difficult to control since even IT professionals can fall victim to these manipulations. Attacks are often difficult to detect because they happen quickly and the indicators can be dispersed across different data sources, such as network servers, endpoints, and applications.
Amid the complexity, the ML-assisted tactics of security analytics provide organizations with multiple benefits:
Provides visibility into complex attack techniques, such as compromised credentials, lateral movement, and data exfiltration.
Offers early detection of insider-threat attacks through analysis of user account activities and user behavior.
Enables a proactive security approach by providing insights about known suspicious activities, allowing organizations to mitigate threats before they can be exploited.
Security analytics has multiple benefits not available through traditional application security. The diverse sets of huge data that can be analyzed in near real-time is a leading benefit of security analytics. Data analyzed can include the following:
Endpoint and user behavior data
Cloud use and traffic
Non-IT contextual data
External threat intelligence sources
Access and identity management data
Proof of compliance during an audit
Analyzing these diverse data sources provides organizations with a comprehensive view of events. By connecting the dots between these data sources, analytics delivers proactive security incident detection and faster response times that help the business to protect the integrity of systems and data. The key benefits that analytics offer to organizational security are highlighted below.
Proactive security -By correlating events with logging data and other sources in near real time, security analytics quickly detects indicators of any suspicious activity. This becomes the foundation of a proactive security posture that allows organizations to detect threats earlier to significantly improve responsiveness.
Maintaining regulatory compliance -Industry and government regulations, such as PCI-DSS, HIPAA, and GDPR, can be adhered to with security analytics. Analytics monitors access, authentication, and user behavior, enabling insider threat detection, and logging data for auditing. In addition, security analytics offer reporting capabilities with a unified view of all data events that identify potential non-compliance.
Improved incident forensics -Data analytics tools provide information about attack origins, what took place, and the nature of any damage or exploit. Each of these are vital for forensics. These attack insights allow security teams to build an accurate attack timeline and prevent similar events in the future.
Advanced data analytics applies ML and statistical models to detect anomalies in real time for threat analysis. These capabilities provide detailed security alerts and combine them with additional forensic data to detect and respond to cyber threats. Advanced analytics reduce security team workloads by triggering workflows automatically when insider threats or other risky behaviors are detected.
Conventional, manual-based security practices are nearly obsolete thanks to the complexity, diversity, speed, and volume of cyber attacks. Add the widening reach of an enterprise technology stack to this equation and the need for AI-powered security analysis becomes clear.
In addition, questions about the reliability and applicability of security data present a terrific argument for computing that goes beyond simple automation. As digital transformation becomes predominant, the volume of data grows exponentially. And the attack surface grows with it. Organizations need AI in security analytics to keep pace and put their organizations ahead in the security battle.
AI-powered data analytics now allow organizations to predict attacks and create baselines for normal behavior within networks and organizations' IT environments. Each of these techniques helps quickly expose suspicious behavior across large organizations and data sets. The AI enables alerts of any suspicious or anomalous behavior to be triggered to security teams. Armed with these alerts, organizations prevent insider threats. Additionally, analysis helps organizations understand the full impact or potential risk of any event.
AI in security analytics offers exponentially greater visibility into the activities going on within the organization by more quickly determining which activities are normal and which could be problematic. Capabilities embedded with intelligence technologies, including ML, analyze data from external sources, such as collective threat intelligence reports and vulnerability databases, as well as from within the organization. The systems use those analyses to search for and identify patterns that fall outside what they've been taught to recognize as acceptable or safe activities.
With the help of AI, advanced security analysis can:
Establish a baseline of normal activity to model anomalies. This applies both to user activity and network traffic.
Analyze malware activities that escape antivirus detection to define if they are a threat or not.
Correlate historic data of intrusions and attacks to identify patterns and detect intrusions.
As noted above, cyber attacks are becoming increasingly difficult to detect. Therefore, every organization can benefit from cybersecurity analytics. Most businesses today rely on vast amounts of data that need to remain protected. There are multiple applications of security analytics that keep networks and customer information protected.
Monitoring and analyzing network traffic -By combining network traffic monitoring data with other event patterns, analysis quickly spots anomalies and indicates potential attacks.
Detecting endpoint threats -Endpoints, such as laptops, desktops, and mobile devices, are often entry points for attack. By monitoring these endpoints, security analysis can quickly detect any threats targeting any organization endpoints.
Detecting insider threats -By monitoring employees and online partner behavior, security analysis efficiently detects insider threats. Security analytics can analyze user actions for any suspicious behavior within sensitive areas. This includes monitoring keystrokes and metadata activities for any user level.
Detecting malicious user threats -Tracking with user and entity behavior analytics (UEBA) enables threat detection throughout an organization. These tools use algorithms and ML to establish normal behavior and then profile for indicators of malicious activity.
Monitoring export and exfiltration of data - By blocking unauthorized communications channels and interfering with users attempting to submit credentials to non-authorized sites, security analytics prevents unauthorized copying or downloading of data. This process interferes with phishing attacks and prevents credential theft.
Automating regulation compliance - By logging data collection, managing personal data flow, and monitoring data activity, security analysis automatically maintains compliance with many regulations. Additionally, analysis tools can alert teams to any compliance violations and offer compiled reports for audits.
Proactive security through AI-powered security analytics
Security analytics starts with aggregating data from a variety of sources and filtering out the most useful information. It then gains contextual intelligence based on the unique state of your IT network and the likely security threats facing your organization. The threats are mapped against a risk profile that aligns with the business use case. Finally, future threats are modeled to prepare for potential threats before they occur.
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