What is security analytics?

Security analytics is the practice of collecting, analyzing, and leveraging data from security events to detect threats and improve security measures. It combines large amounts of an organization's data with advanced threat intelligence to mitigate targeted attacks, insider threats, and persistent cyber threats. The key aspects of security analytics are data analysis, proactive threat detection, AI and machine learning, compliance support, forensic features, and response enablement.

Security analytics is a multi-step process that uses multiple data sources to detect and proactively prevent potential threats.

1. Collect data

   First, establish visibility across your entire attack surface. To do this, ingest as much data as possible and store it efficiently. This includes things like your apps and services, databases, and infrastructure.

2. Normalize the data

   Comparing data stored in different formats isn't just difficult, it's inefficient. The solution: normalizing your data into a common schema that lets you uniformly analyze your security data.

3. Enrich the data

   Context plays a major role in security analytics, so it's important to enrich your data. This means supplementing it with layers of extra information like threat intelligence, user context, and asset context. This will make your data and alerts more meaningful and actionable.

4. Detect threats

   To tackle any threats, you need to know where they are. Find potential threats fast by automating detection using AI, machine learning, and other threat-hunting technology. This makes it possible to proactively detect threats or vulnerabilities before they can cause any damage.

5. Investigate suspicious activity

   Once a potential threat is detected, it's important to investigate the suspicious activity quickly and efficiently. Instead of manual investigation, tools like advanced search, AI, and alert triaging help sift through your environment to find the potential threat. Every investigation should also be tracked in a collaborative case management tool.

6. Respond quickly

   Suspicious activity turned into a verifiable threat? The last step in security analytics is incident response. Once a threat has been confirmed, you can take decisive action, stopping the attack before it gets started.

Security analytics is important because it makes it possible to proactively detect and identify threats in real-time. AI and machine learning can help identify threats by analyzing patterns and anomalies before the threat can progress. By providing a unified view of security events across various sources, it also facilitates enhanced investigation and response. This allows you to make faster, more informed decisions when incidents occur.

In broader terms, it gives you improved cybersecurity resilience. It does this by strengthening your overall security posture across your IT environment, making it possible to adapt to evolving threats and attack techniques. It also protects your company by meeting compliance requirements through various regulatory bodies.

Faster threat detection and response

Security analytics makes it possible to carry out real-time analysis of data across multiple sources, which means you can quickly identify potential threats before they can do any meaningful damage. Supplemented with advanced pattern recognition and anomaly detection, an effective security analytics practice significantly reduces your mean time to detect (MTTD) and mean time to respond (MTTR), dramatically reducing the risk to your business and customers.

Reduced operational costs

The less time it takes to detect, investigate, and respond to a threat, the less it's going to cost your business in both time and resources. This not only applies to the headline-grabbing breaches (resulting in the familiar loss of business, fines, and reputational damage), but even lesser incidents that may still divert valuable security operations center (SOC) resources to resolve.

Enhanced operational resilience

When attacks are successful, it impacts operations across the entire company. By enhancing your overall security posture, security analytics reduce the risk of attacks succeeding. This helps you to maintain system availability, which helps business operations run more smoothly. It also supports your compliance efforts, which helps you avoid issues with regulators.

Informed decision-making

Security analytics gives you data-driven insights, which can be used to further inform your security investments and define your strategies. With the comprehensive view it provides of your organization's security posture, effective security analytics facilitates more informed decisions on risk management.

AI-driven security analytics

AI-driven security analytics enables security operations teams to examine data from across the environment. Such solutions can monitor data across multiple sources, like network traffic, endpoint logs, user context, and cloud telemetry. These tools apply visualizations, alerting, machine learning, and artificial intelligence to analyze vast amounts of data to detect complex patterns and anomalies that other techniques miss.

Like a SIEM, AI-driven security analytics also correlates data to detect known threats and applies advanced analytics to spot anomalous — and potentially malicious — activity. Together, these capabilities lead to fewer missed threats and lower dwell times.

Security information and event management (SIEM)

A SIEM is a centralized platform that collects and normalizes data from across a company’s IT infrastructure, analyzes it to detect threats, and powers both automated and user-driven correlation of data. It enables several core security operations functions, including real-time monitoring, automated threat detection, and incident response. SIEMs are a crucial tool in the SOC, but they vary widely, so organizations must be careful to pick one that helps them efficiently and effectively detect, investigate, and respond to threats.

User and entity behavior analytics (UEBA)

A UEBA (User and Entity Behavior Analytics) solution is a cybersecurity process or tool that uses machine learning and statistical analysis to identify anomalous behavior or activities carried out by users or entities within an IT network. The primary focus of UEBA is to detect insider threats, compromised accounts, and privilege misuse by analyzing data patterns and understanding typical user behavior.

UEBA assesses user and entity behavior like file access patterns, login times, and application usage. From this data, it establishes baselines of what constitutes “normal” behavior — both across time for a specific user and for a group of peers — and then compares new activity to spot unusual and potentially suspicious behavior.

Threat intelligence

Threat intelligence provides vital context for handling potential threats. It helps the SOC detect, prioritize, and respond to threats by identifying indicators of compromise (IOCs) like malicious IPs or file hashes linked to cyberattacks. Additionally, threat intelligence feeds can provide insights into the tactics, techniques, and procedures (TTPs) used by specific threat actors, enabling organizations to anticipate and counter targeted attacks. Overall, threat intelligence can significantly reduce the likelihood and impact of security incidents.

Having a robust security analytics process is essential to safeguarding infrastructure, digital assets, and operations. Across industries, teams use security analytics to improve threat detection, enhance incident investigation, and support compliance.

Continuous monitoring

To keep your data and systems secure, it's important to be able to keep an eye on all of your security data in real time. This way, you can maintain visibility across the entire IT infrastructure to detect potential threats, investigate them quickly, and respond to incidents before they escalate. It also enables teams to comply with regulatory requirements by providing an auditable trail of activities and response actions. Security analytics makes this possible with continuous monitoring across security-relevant data, ensuring a clear look at your entire attack surface.

Automated threat detection 

Automated threat detection refers to the use of technology, particularly software and algorithms, to identify potential security threats without human intervention. This technology is crucial for handling the vast amount of data and complex threat landscapes that organizations face today. Automated threat protection extends to ransomware, malware, and other common attacks.

Insider threat detection

Insider threat detection refers to the process of identifying and mitigating risks posed by individuals within an organization who might intentionally or unintentionally compromise the organization's security. These individuals could be employees, contractors, business partners, or any other entities with inside access to the organization's systems and data.

Threat hunting 

Using machine learning as part of your security analytics lets you proactively hunt for threats and weaknesses within your infrastructure. Leveraging petabytes of data over many years, you can identify key insights and use threat intelligence to find and assess potential risks.

Incident response

Incident investigation and response requires a security analytics solution that gives your team access to the data and tools they need to collaboratively address advancing threats. Key features such as real-time analytics, case management, and automated response enable security teams to quickly pinpoint the source of an incident, understand its scope, and take action. This integration of technology, automation, and teamwork is crucial for effectively managing and neutralizing security incidents in a timely manner.

Implementing security analytics doesn't have to be intimidating. Despite all the steps taken in the security analytics process, the process relies on selecting the right tools and determining the appropriate use cases for your needs.

1. Assess current security posture: First, set clear goals and use cases for your security analytics solution, and then identify your existing security gaps and challenges. This will be the framework for the rest of your implementation.
2. Select appropriate tools: Once you know your gaps, you need to find the right tools for the job. Look at different security analytics solutions and compare things like supported data sources, analysis capabilities, and scalability. You should also consider compatibility with your existing security infrastructure.
3. Plan data collection and integration: Next, determine your data sources. List all of your relevant data sources, such as network logs, endpoint data, and cloud services, then identify the method and the frequency for collecting this data.
4. Configure and customize the solution: Configure data integrations and get immediate visibility with dashboards and reports. Automate detection with prebuilt alerting rules and machine learning jobs. Adopt AI-driven features, connect workflows with third-party tools, and harness prebuilt playbooks and automations.
5. Train your personnel: Organize training for the team members using the security analytics solution or reviewing the data it outputs. A good tool will help your team efficiently triage alerts and perform investigation and response.
6. Continuously monitor and iterate: To get the best out of your security analytics, you should regularly review and update your analysis rules and thresholds. Gather feedback from users and stakeholders to identify where improvements can be made, and stay informed about new threat research so you can adjust accordingly.

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