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 the entire ecosystem and turns that data into actionable insights so that 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 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, a 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.

Data breaches exposed
4.1 billion
records in the first six months of 20193

How does machine learning work with security analytics?

Machine learning is a software capability that allows software to improve its own performance at a particular task using relevant data. With 69 percent of IT leaders saying that AI and machine learning is transforming their business1, it’s little surprise that the most advanced security analytics solutions integrate machine learning. 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 this 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 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 began 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.

What are the business and IT needs for security analytics?

Hackers attack every 39 seconds, or an average of 2,244 times a day2. With cyberattacks and breaches continuing to rise, cybersecurity 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. Considering the average time to identify a breach is 206 days3, 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.

This in mind, the primary benefit of security analytics is delivering end-to-end security visibility to IT. This shows IT the current state of their security across geographical information, access and logins, SaaS and Web App use, virtual apps and desktops 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.

Insider threat via a company’s own employees (and contractors and vendors) is one of the largest unsolved issues in cybersecurity.2

McKinsey & Company

What are the use cases for security analytics?

One of the top needs of security analytics is a holistic approach that examines internal as well as external user activity. Because 34 percent of data breaches involve internal actors4, behavior analytics can help recognize security threats from internal users before they turn into costly data breaches. In addition, a secure workplace is crucial to detecting anomalies and potential cyber threats. 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.

Watch how analytics solutions help protect your organization from security threats

Top security analytics use cases include:

  • Protecting the business from a loss of valuable intellectual property with real-time insights
  • Creating a secure workplace for efficient internal use
  • Monitoring incoming and outgoing traffic on your network
  • Additional security tools for apps, mobile devices, and clouds like Microsoft Azure and AWS. This includes the ability to ingest security events from specific vendors to enrich the user risk profile, such as data collection across Microsoft Azure and other Microsoft offerings that can be analyzed in depth and then exported to Microsoft Sentinel. 
  • 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

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