What is security analytics?

Security analytics is an approach to cybersecurity that, like SIEM (Security Information and Event Management), analyzes data to detect anomalies, unusual user behavior, and other cyber threats. It aggregates data from across an organization’s entire ecosystem and turns that data into actionable insights so IT can proactively act to minimize risks and prevent security incidents. Advanced network security features like artificial intelligence (AI) and machine learning (ML) further help by automating the detection and remediation process.

This approach can offer faster and more comprehensive protection from security events without complicating the employee experience. In addition to external threat intelligence, a sophisticated security analytics solution provides proactive visibility across an organization, improves the user experience, and ultimately drives better business outcomes.

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What are the key elements of a security analytics solution?

A security analytics solution should be able to monitor IT performance across an organization’s architecture as well as analyze behavior data for potential threats. For an analytics platform to be effective, it must provide critical security data regarding user activity as well as network traffic analysis and anomaly detection. The three main performance areas that an IT security solution should be able to report on include network, applications, and device performance.

If performance is poor in any of these areas, there is a greater likelihood that malware will slip past threat detection solutions and work undetected in the infrastructure. By using a security analytics tool equipped with AI and ML, along with security policies and best practices, organizations can make big strides towards reducing risks across their architecture.

How does machine learning work with security analytics?

The most advanced security analytics solutions integrate machine learning, which allows software to improve its own performance at a particular task using relevant data. In contrast to the predefined and fixed data transformations that many security analytics solutions include upon installation, ML-capable security analytics transform their own performance and capabilities by being adaptive and responsive to big data. Here’s how it works:

  1. For machine learning to apply useful security insights, a ML engine needs access to a lake of diverse data drawn from events, applications, network activity, and user behavior across an organization. The best way to fill this big data lake is by integrating security analytics with a unified digital workspace that contains all the data sources mentioned above. This simplifies the data collection process and helps ensure all data is relevant.
  2. Once an organization’s security analytics platform has filled its data lake, the next step is to correlate this data to individual users inside the organization. This is the beginning of the risk profiles that the ML engine will develop later.
  3. After this data is correlated to distinct users, the machine learning engine can be applied to develop insights into how those users behave at work. This allows the machine learning technology to gain insights into each user’s normal activity and behavior that the organization would otherwise not be able to obtain.
  4. Now that the machine learning engine has developed these actionable insights, it creates specific risk profiles for each user inside the organization. This allows the security analytics tool to continually score the user’s session for risk. If a user begins acting suspiciously by deviating from their normal work activity, the security analytics platform would recognize this aberrant behavior immediately thanks to the risk indicators developed by the ML engine.

This unsupervised anomaly detection is one of the most common and important ways that machine learning works with security analytics. Outside of security, machine learning can also continually analyze performance data to quickly identify issues and pinpoint their root causes.


User behavior analytics for proactive security insights

A successful data breach could cripple your business—learn how to stop these threats with security analytics.

Why do organizations need security analytics?

With cyberattacks and breaches continuing to rise, data security is a top business concern for today’s C-suite. Whether through malicious activity, insider threats, or unintentional leaks, organizations suffer as a result of lost data. Negative repercussions can include loss of revenue or brand reputation, expensive lawsuits, massive governance and compliance fines from violating regulations like HIPAA and GDPR, and disruptions to operations. Breaches can wreak havoc for IT teams as well—just becoming aware of a security issue is time consuming. Remediation after a breach also uses valuable personnel hours and eats into budget intended for other purposes.

The primary benefit of security analytics is its ability to deliver end-to-end visibility. IT can see the current state of security across geographical information, access and logins, SaaS and web app use, virtual apps and desktop events, data, and endpoints. To prevent damaging security incidents, a strong analytics platform should proactively address attempted breaches by finding and flagging abnormal user activity using behavior analytics, and then instantly respond instead of react. This provides security assurance to IT and business leaders that they know the existing state of their security posture and how to improve it going forward.

What are the use cases for security analytics?

One of the top needs for security analytics is a holistic approach that examines internal as well as external user activity. Because many incidents involve internal actors, behavior analytics can help identify these security threats before they turn into costly data breaches. In addition, a secure workspace is crucial to detecting anomalies and potential cyberthreats, since it also allows employees access to all necessary apps while ensuring data security from the inside out.

A best-in-class security analytics solution is automated to examine all data, traffic, and activity across the entire infrastructure. By monitoring and applying machine learning to user behavior, security analytics solutions can better identify unusual activity and quickly provide security alerts. This end-to-end view enables IT to take a proactive approach to security instead of a reactive one.

Top security analytics use cases include:

  • Protecting the business from a loss of valuable intellectual property with real-time insights
  • Creating a secure digital workplace for efficient internal use
  • Monitoring incoming and outgoing traffic on your network
  • Providing additional security tools for apps, mobile devices, and clouds
  • Improving visibility for IT across the entire digital workspace environment
  • Empowering IT teams to automate and take a proactive approach to detection versus a reactive approach with remediation

Citrix solutions for security analytics

To proactively prevent cyberattacks, you need comprehensive security analytics to assess, detect, and prevent risks. Citrix Analytics for Security uses machine learning to create individual risk scores so you can identify and stop threats before they lead to data breaches. With end-to-end visibility and real-time analysis, this advanced solution makes it easy to respond to suspicious activity instantly and automatically.