Trace Id is missing
Skip to main content
Microsoft Security

What is cybersecurity analytics?

Learn how cybersecurity analytics helps organizations manage security risks through data analysis.
Explore AI-powered, unified SecOps

Cybersecurity analytics overview

Cybersecurity analytics is a way to proactively manage cybersecurity risks using tools like security information and event management (SIEM). By using machine learning and behavioral analysis to analyze organizational and user data, companies can predict or prevent incidents instead of just responding to them after they occur.

As the volume of data, apps, devices, and identities increases, so does the difficulty of tracking and securing them all manually. Often, security teams have dozens of distinct tools providing hundreds of signals per hour, which is overwhelming and makes it challenging to correlate patterns manually.

With cybersecurity analytics, organizations can:
  • Correlate insights across different security tools, platforms, and clouds.
  • Detect threats faster. 
  • Improve incident responses. 
  • Assess risks before they are exploited.
  • Streamline processes and resource allocation. 
  • Improve overall threat intelligence.
  • Increase threat awareness and visibility.

Key takeaways

  • Cybersecurity analytics is a way to proactively manage cybersecurity risks using techniques like machine learning and behavioral analysis. to collect and analyze data, then identify patterns and anomalies that may indicate a security threat. 
  • A typical workflow includes data collection, data normalization, data analysis, machine learning, and data visualization.
  • Organizations use cybersecurity analytics to detect internal and external threats, manage incidents, assess risks, and comply with security requirements.
  • Organizations have access to tools such as EDR, XDR, network traffic analysis, SIEM, SOAR, threat hunting, threat intelligence, UEBA, vulnerability management, and continuous monitoring.
  • Some key benefits include faster threat detection, improved incident responses, risk assessment, streamlined processes, and increased threat awareness and visibility overall. 
  • Some challenges include data privacy concerns, skill gaps, and evolving threats.
  • In the future, the field of cybersecurity analytics may see the rise of generative AI, the expansion of analyst skillsets, automated responses to threats, and more optimization.

How does cybersecurity analytics work?

Cybersecurity analytics works by collecting and analyzing data from various sources to identify patterns and anomalies that may indicate a security threat. This data is then processed using advanced analytical techniques—such as machine learning—to detect and respond to potential threats in real time. The typical workflow of a cybersecurity analytics solution includes the following steps:
 
  1. Data collection. It may sound like a truism, but effective cybersecurity analytics relies on comprehensive access to an immense amount of data from users, endpoints, routers, apps, and event logs, just to name a few sources.

  2. Data normalization. A glut of raw data isn’t the most helpful at providing actionable security insights. With data normalization, security teams can aggregate datasets from diverse sources into a single format and summarize it to support analysis and decision-making. 

  3. Data analysis. Once the data is normalized into a consistent, comprehensible form, analysis can begin. This is where patterns and insights are identified from a multitude of seemingly disparate data points. Using tools like rules, workbooks, and queries, behavioral trends can be identified and flagged as potential risks.

  4. Machine learning. Analyzing big data takes time and resources, and security professionals only have so much of both. By training machine learning models to recognize threat patterns or risky behaviors, security professionals can process data much faster, detect anomalies more easily, and prioritize investigations. For example, user and entity behavior analytics (UEBA) tools use behavioral analytics, machine learning algorithms, and automation to identify abnormal behavior within an organization’s network. 

  5. Data visualization. Security insights from big data can be unwieldy and difficult to comprehend, which can be a challenge for business and security decision makers. Data visualization is the graphical representation of trends, outliers, and patterns using charts, graphs, and maps to make complex data more accessible and understandable. With comprehensible threat intelligence, organizations get a comprehensive view of the threat landscape to make informed security decisions.
Some organizations use a cloud-native SIEM tool to aggregate data that is then analyzed at machine speed to identify patterns, trends, and possible issues. Using a cloud-native SIEM allows organizations to import their own threat intelligence feeds and signals from their existing tools.
Use cases

Cybersecurity analytics in action

The strength of cybersecurity analytics comes from helping security experts find and stop threats early when used with external threat detection and response. Explore examples of how organizations can use cybersecurity analytics.

External threat detection

By monitoring network traffic patterns, cybersecurity analytics can identify potential attacks or anomalies—like a distributed denial-of-service (DDoS) attack, adversary-in-the-middle attack, malware, and ransomware—that may indicate security breaches.

Compromised account detection

Direct assaults on networks aren’t the only types of threats that can impact a business. Phishing attacks and social engineering scams can trick users into sharing privileged data or making their own systems vulnerable. Cybersecurity analytics constantly monitors for such events.

Internal threat detection

Cybersecurity analytics helps track user and entity behaviors within the network, allowing early detection of suspicious activities or insider threats.

Incident response and digital forensics

Security teams can use cybersecurity analytics in incident responses by delivering robust insights needed to resolve an attack. Deep forensic reviews help security teams understand the nature of incidents to their security posture and help ensure all compromised entities are remediated.

Risk assessment

Machine learning tools automate the generation and analysis of threat intelligence, categorizing and storing detected threats for future reference. This enhances the system's ability to recognize similar threats and assess their level of risk.

Security compliance and reporting

A cybersecurity analytics solution can increase an organization’s ability to comply with industry regulations and demonstrate transparency with automated reporting.

Types of cybersecurity analytics tools


Organizations have access to a range of cybersecurity analytics tools, each with functionalities that address different needs. Some tools go beyond analysis to provide automated protection and threat response.

Endpoint detection and response

Endpoint detection and response (EDR) is software that protects end users, endpoint devices, and IT assets by using real-time analytics and AI-powered automation. EDR protects against threats that are designed to bypass traditional antivirus software and other conventional endpoint security tools.

Extended detection and response

Extended detection and response (XDR) is a tool that automatically identifies, assesses, and remediates threats. XDR broadens the scope of security by extending protection across a wider range of products than an EDR, including an organization’s endpoints, servers, cloud applications, and emails.

Network traffic analysis

Network traffic analysis is the process of monitoring network traffic to extract information about potential security threats and other IT issues. It provides valuable insights into network behavior, enabling security experts to make decisions about protecting network infrastructure and data.

Security information and event management

SIEM helps organizations detect, analyze, and respond to security threats before they harm business operations. It combines both security information management (SIM) and security event management (SEM) into one security management system.

Security orchestration, automation, and response

Security orchestration, automation, and response (SOAR) refers to a set of tools that automate cyberattack prevention and response by unifying systems for improved visibility, defining how tasks should be run, and developing an incident response plan that suits your organization’s needs.

Threat hunting

Cyber threat hunting is the process by which security teams proactively detect, isolate, and neutralize advanced threats that might evade automated security solutions. They use a variety of tools to search for unknown or undetected threats across an organization’s network, endpoints, and data.

Threat intelligence

Threat intelligence is information that helps organizations better protect against cyberattacks. This includes analytics that give security teams a comprehensive view of the threat landscape so they can make informed decisions about how to prepare for, detect, and respond to attacks.

User and entity behavior analytics

UEBA is a type of security software that uses behavioral analytics, machine learning algorithms, and automation to identify abnormal and potentially dangerous behavior exhibited by both users and devices within an organization’s network.

Vulnerability management

Vulnerability management is a process that uses tools and solutions to continuously and proactively keep computer systems, networks, and enterprise applications safe from cyberattacks and data breaches.

Continuous monitoring

Cybersecurity analytics tools can monitor an organization’s entire environment—on-premises, clouds, applications, networks, and devices—all day, every day, to uncover abnormalities or suspicious behavior. These tools gather telemetry, aggregate the data, and automate incident response.

Benefits of cybersecurity analytics tools


Cybersecurity analytics tools offer security teams a variety of benefits for both protecting organizational data and improving overall security processes.

Some of these key benefits include: 
 
  • Faster threat detection. The top benefit of using analytics enhanced by machine learning and behavioral analysis is to get ahead of risks before they become problems. Proactive monitoring helps security teams identify and respond to risks faster than ever before. 
  • Improved incident responses. Sometimes threats get through security systems and impact organizational data. But faster response times can limit damage, isolate affected areas, and prevent threats from spreading within organizational systems.
  • Risk assessment. Not all threats are equal. Cybersecurity analytics tools help IT professionals assess which risks they need to address and in what order of priority.
  • Streamlined processes and resource allocation. Cybersecurity analytics tools help security teams more efficiently and effectively collect, correlate, and analyze massive amounts of organizational data. By simplifying the process, these tools help give time back to security teams who can then focus on systems or incidents that require their attention.
  • Increased threat awareness and visibility. The automated nature of cybersecurity analytics affords security teams visibility into risks without the labor of having to continuously test and track them down. Machine learning and behavioral analysis models are continuously adapting to provide organizations with more comprehensive cybersecurity awareness.

Best practices for cybersecurity analytics


As with any tool, the technology alone is not enough to help ensure success. To be most effective, cybersecurity analytics tools require some preparation before implementation and maybe some changes to current business practices after they’re in place. Some best practices include:
 
  • Data classification. Ensure that organizational data is properly classified and meets any internal or external compliance standards. Also, define access controls for sensitive information. Organizations that use data security tools may already have processes in place to meet classification and compliance requirements. 
  • Extended retention periods. Hold onto event logs that may be needed in the future for threat hunting or compliance audits. The length of time that organizations should retain logs may vary by industry, compliance regulation, or agency. 
  • Zero Trust. Protect all environments with Zero Trust architecture that protects each file, email, and network by authenticating every user identity and device.
  • Current intelligence. Use threat intelligence—the most current data providing a comprehensive view of the threat landscape—to inform security decisions. 
To get started with cybersecurity analytics, organizations should:
 
  1. Identify their needs. Each organization has its own security goals—whether it’s faster response times or improved transparency for regulatory compliance. The first step to effective cybersecurity analytics is identifying all of those goals and holding those outcomes as priorities throughout the process of selecting and implementing new tools.
     
  2. Identify data sources. This process can be demanding, but it’s essential for effective cybersecurity analytics. The more comprehensive the data sources, the greater the visibility into risky behaviors and unusual activity that could indicate a threat.
     
  3. Choose a tool that fits their circumstances. The variety of cybersecurity analytics tools speaks to the variety of needs and situations of the organizations using them. A new company may need a comprehensive solution that handles all threat assessment and response. But a more established company may already have cybersecurity solutions in place—in this case, the right tool might be one that is designed to integrate with existing systems and enhance, rather than replace, those investments.

Challenges in cybersecurity analytics


Organizations striving for quality cybersecurity analytics face a number of challenges, including data privacy concerns, skill gaps, and evolving threats.

Data privacy concerns

With data breaches frequently making international headlines, it’s no wonder customers and end users are concerned over how companies use and protect their personal information. Add to that the complications of local or industry compliance regulations, which may go into effect faster than an organization can update their data management systems. A solution to these challenges could be a cybersecurity analytics system with built-in compliance features and data protection that both limit internal access and proactively prevent external attacks.

Skill gaps

While cybersecurity is not a new concept, contemporary technologies and systems are evolving at breakneck pace to keep up with both internal needs and external threats. The shortage of skilled cybersecurity analytics professionals means that organizations are increasingly relying on manual processes and outdated systems just to keep up. The first solution that may come to mind is more training for employees. However, a more efficient approach may be to implement a user-friendly tool that can automate common cybersecurity analytics processes and includes out-of-the-box features like pre-built connectors to CDR, cloud data, and servers, just to name a few possible integrations.

Evolving threats

The pace at which cyberattacks evolve is staggering. And traditional security analytics are limited by an organization’s ability to identify, comprehend, and respond to threats that are more sophisticated than their internal systems. The solution is a cybersecurity analytics approach that evolves to keep pace with threats. Machine learning and behavioral analysis drive proactive, preventative threat analysis that can stop attacks before they impact an organization. Threat intelligence platform solutions aggregate threat indicator feeds from different sources and curate the data to apply to solutions like network devices, EDR and XDR solutions, or SIEMs.

Cybersecurity analytics solution

 
Incorporating cybersecurity analytics into a new or existing security process is critical for helping keep organizations safe and compliant with current applicable regulations. By identifying patterns, anomalies, and threats with machine learning and behavioral analysis, security experts can more easily protect their data and help ensure continuity of business. Microsoft Security offers a unified security operations platform which incorporates cybersecurity analytics to give organizations the threat protection capabilities they want.
RESOURCES 

Learn more about Microsoft Security

  1.
A person with short hair is working at a computer in a dimly lit room.
Solution

AI-powered, unified security operations

Outpace cyberthreats with one powerful security operations platform.
Learn more
A man with a beard sits at a desk using a laptop and desktop computer with multiple monitors.
Product

Microsoft Sentinel

Identify and stop cyberattacks faster with a powerful cloud-native SIEM enriched by AI.
Learn more
Three Professional sitting around a table talking with each other.
Product

Microsoft Copilot for Security

Empower security teams to detect hidden patterns and respond to incidents faster with generative AI.
Learn more
Back to RESOURCES section

Frequently asked questions

  • Cybersecurity analytics is the way that organizations can find patterns and spot risks from across their entire digital estate. Machine learning and behavioral analysis provide information to catch events early and enable security teams to prevent them from causing major damage. These tools can help analyze vast amounts of data to help organizations respond faster and stay more secure.
  • Cybersecurity analytics is important because it helps security teams protect organizational and customer data as well as improve cybersecurity response processes. Key benefits of cybersecurity analytics include faster threat detection, improved mean time to respond, risk assessment, streamlined processes, and increased threat visibility. These all help to improve protection of an organization’s critical infrastructure, reducing the risk of an attack that can impact an organization’s productivity and bottom line. Analytics are also critical for compliance needs and threat hunting.
  • AI and machine learning are used to aggregate, analyze, and draw insights from large quantities of organizational and customer data. The sheer volume of data generated by sources like endpoints, users, and routers presents a scaling challenge for cybersecurity professionals looking for trends or insights that could indicate threats. AI and machine learning models can be trained to identify trends or draw insights from the wealth of data managed by an organization. New generative AI tools can help to further improve the speed and quality of security work, while increasing the skill set for junior security analysts.
  • Cybersecurity analytics can help to proactively detect threats before they disrupt an organization. By correlating data across sources, security teams get a clearer picture of how an attacker is moving across vectors, ultimately giving a more comprehensive view of an attack and its severity. Using automation workbooks can help reduce the time to respond to common tasks, speeding the mean time to respond.

Follow Microsoft Security