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What is Cybersecurity Analytics? The Ultimate in Data Defense.

Safeguarding organizational information is as old as business itself. While digital technologies have transformed data management, they’ve also meant an exponential rise in cybersecurity risks. Social engineering, malicious insiders, APTs and advanced malware, unpatched vulnerabilities, and compromised credentials are just a few of the threats data-dependent organizations face every day.  

Today, digitally transformed companies must perform an intricate balancing act, using all of technology's advantages while walking a high wire between opportunity and evolving threats. Achieving this equilibrium demands a proactive, adaptable approach that prioritizes robust data protection without compromising operational agility.

Amidst what can sometimes feel like a three-ring circus, organizations are finding that traditional security measures are no longer enough. Firewalls, antivirus software, and employee training, while essential, aren’t providing the comprehensive protection required to fend off modern cyber threats. Safeguarding digital assets and sensitive data requires solutions that enable businesses to anticipate, detect, and respond to emerging risks before they cause harm.

Cyber analytics has emerged as an invaluable tool in the ongoing struggle against cyber criminals, harnessing advanced technologies to identify potential threats, uncover hidden vulnerabilities, and empower organizations with insights that help them fortify their defenses and mitigate risks proactively.

Cybersecurity Analytics Overview

Who doesn’t love a good mystery yarn? Just as a detective gathers evidence, looks for patterns, and then uses them to piece together a crime’s story arc, cybersecurity software looks for data patterns and trends from various sources and analyzes them to uncover the story and motive behind a potential cyber threat.

Cybersecurity analytics lets you see the big picture. For instance, an unusual pattern of login attempts from another country is like a series of suspicious footprints leading to and from a crime scene. A detective and cybersecurity analyst must both use their skills in pattern recognition and interpretation to determine their next steps.

Cybersecurity analytics is the collection, processing, and analysis of security-related data to provide actionable insights and enhance an organization's security posture. It’s a critical tool for leveraging data analysis, machine learning, and statistical techniques to detect, prevent, and respond to cyber threats and vulnerabilities. Its importance at a time when cyber threats are becoming increasingly sophisticated and frequent cannot be overstated.

Key components include:

  • Data collection. Gathering security-related data from various sources, including network traffic, logs, endpoints, and cloud services, to create a comprehensive dataset for analysis.
  • Data processing. Normalizing and filtering collected data to remove irrelevant information and consolidate data from different sources to prepare it for analysis.
  • Data analysis. Applying machine learning algorithms, statistical models, and other analytical techniques to processed data to identify patterns, anomalies, and trends indicating potential security threats.
  • Threat intelligence. Integrating external threat intelligence feeds to enrich the analysis with information about known threats, vulnerabilities, and attack techniques.
  • Visualization. Presenting analysis results in a user-friendly format, including dashboards and reports, to facilitate the interpretation and communication of findings to decision-makers.
  • Automation. Automating the detection and response processes to enhance the speed and efficiency of cybersecurity operations.

Benefits of Cybersecurity Analytics

Cybersecurity analytics is a virtual game-changer in digital security, making it easier to identify and mitigate potential security breaches before they cause significant financial, legal, and reputational damage. By analyzing patterns and trends in data, it uncovers hidden threats, reduces false positives, and prioritizes risks, enabling a more efficient and effective response to cyber incidents.

Enhanced threat detection and response

Organizations can proactively identify potential threats by using real-time monitoring and advanced analytics techniques to sift through vast amounts of data to identify subtle patterns and anomalies. Swift detection of suspicious activities facilitates prompt response times, mitigating risks and minimizing the impact of cyber threats. For example, a sudden spike in network traffic from an unknown IP address could be flagged for further investigation, enabling immediate action before any damage is done.

Improved incident management

When a security incident occurs, real-time cybersecurity analytics provides comprehensive visibility into its nature, scope, and root causes, enabling organizations to make better decisions and implement remediation measures to contain and recover from the breach. Security teams can quickly prioritize and address the most critical threats, reducing time to resolution. For instance, the ability to detect a ransomware attack in its early stages allows teams to isolate the affected systems and prevent the malware from spreading further.

Effective risk assessment and mitigation

Continuous monitoring and analysis help enterprises gain deeper insight into their cybersecurity posture, vulnerabilities, and potential attack vectors. They’re better equipped to proactively assess and prioritize risks, develop targeted mitigation strategies, and allocate resources effectively to fortify their security defenses, such as patching software vulnerabilities or enforcing stricter access controls, to mitigate potential risks. A retail company, for example, might use analytics to identify patterns of credit card fraud and implement more robust authentication measures to protect customer data.

Implementing Cybersecurity Analytics Tools

Developing a security analytics strategy that goes beyond reactionary measures is imperative for identifying vulnerabilities, implementing robust defenses, and responding quickly to potential breaches. Key elements of any business’s security approach should include:

  • Data classification. Not all data is created equal. Categorizing data based on sensitivity and criticality ensures optimal resource allocation and application of security measures.
  • Access control. Strict access controls, such as user authentication, role-based access, and continuous data inventory monitoring, ensure that only authorized individuals can access sensitive data.
  • Encryption. Encrypting at-rest and in-transit data adds an extra layer of data protection that makes it more difficult, if not impossible, for unauthorized users to make heads or tails of intercepted data.
  • Company-wide security training and awareness. For all of technology’s advantages, it’s people that remain an organization’s first line of defense. Comprehensive security training from the C-suite down should educate team members on potential threats, best practices, and the critical role each person plays in maintaining data security.
  • An incident response plan. Unfortunately, data breaches still occur despite robust defenses. An incident response plan outlines the steps to be taken once a security incident occurs. It defines roles, responsibilities, and procedures that minimize damage and facilitate swift recovery.

How do security analytics tools work? They start by collecting data from numerous sources such as:

  • Business applications
  • Contextual data
  • Endpoint and user behavior data
  • External threat intelligence
  • Firewalls
  • Operating system event logs
  • Routers
  • Virus scanners

They then combine the data into a single dataset that security teams can use to apply the most appropriate algorithms for creating searches that identify early attack indicators.

Types of security analytics tools

As with most business tools, choosing the right analytics tool for your organization depends on factors like your specific needs and objectives, the size and complexity of your data, the skills and expertise of your team, the level of integration required with existing systems, and the budget allocated for analytics solutions.

While features vary from solution to solution, most security analytics platforms offer:

  • Application access and analytics
  • Automated or on-demand network traffic analysis
  • DNS analysis
  • Email analysis
  • File access
  • Geolocation, IP context
  • Identity and social persona
  • Threat intelligence
  • User and entity behavior analytics (UEBA)

When making the investment, key features to consider in cybersecurity tools for your organization are:

  • Behavioral analytics examines user patterns and trends, devices, and applications to identify abnormal behavior or other indicators of a security breach or attack.
  • External threat intelligence, while not exactly security analytics, supplements the process by offering insights into emerging threats, weaknesses, and attack routes from external sources.
  • Forensic tools investigate ongoing or prior attacks to determine how cyber criminals infiltrated and compromised one or more systems. They also identify cyber threats and security vulnerabilities that could leave a company susceptible to future attacks.
  • Network analysis and visibility (NAV) tools analyze end-user application traffic as it flows across an organization’s network.
  • Security information and event management (SIEM) uses various tools to provide real-time security alert analyses.
  • Security orchestration, automation, and response (SOAR) connect data-gathering capabilities, analyses, and threat responses.

Technologies like AI and machine learning (ML) enable deeper dives into networks, monitoring suspicious activity and enhancing these and other tools’ functionalities to allow for better threat detection and prioritization. They can also steer and develop response strategies based on learned patterns and historical data, extracting, visualizing, and analyzing data in real-time to deal with current threats and predict future ones.

Cybersecurity Analytics vs. SIEM

Cybersecurity analytics and Security Information and Event Management (SIEM) have common cybersecurity goals but are also distinctly different.

  • Cybersecurity analytics focuses on using data analysis tools and techniques to collect, process, and analyze data security to identify patterns and anomalies that may indicate a security breach and, if necessary, respond to those cyber threats. Put another way, this proactive and predictive approach leverages advanced analytics, AI, and ML to provide insights into potential threats.
  • SIEM is a more traditional data security approach that focuses primarily on real-time monitoring and management of security events and logs. It gathers and correlates data from various sources like servers, network devices, and security systems to identify and respond to security incidents. Reactive in nature, SIEM provides alerts and facilitates incident response after an event has occurred.

The two tools complement one another, forming a well-rounded data security strategy and enhancing an organization's ability to detect and respond to threats more effectively. By combining the predictive capabilities of analytics with SIEM real-time monitoring, businesses achieve a more robust and proactive security posture.

Cybersecurity Analytics Use Cases

By leveraging advanced analytics, organizations can more easily and quickly uncover hidden threats, monitor real-time risks, and detect suspicious activities that might indicate potential security breaches.

These use cases highlight the importance of cybersecurity analytics in maintaining a robust security posture.

Analyzing Traffic to Identify Patterns That May Indicate Attacks

Cybersecurity analytics analyze network traffic patterns to detect anomalies that could signal potential attacks. For example, it can identify unusual spikes in traffic volume, suspicious IP addresses, or data transfers to unauthorized locations, which might indicate a data exfiltration, distributed denial-of-service (DDoS) attack, or other malicious activity.

Real-Time Threat Monitoring

By continuously monitoring and analyzing data from various sources like network logs, endpoint activity, and security tools, cybersecurity analytics provide real-time visibility into potential threats, enabling businesses to quickly detect and respond to emerging risks before they can cause significant damage.

Detecting Insider Threats

Cybersecurity analytics analyzes user behavior, access patterns, and data movements to help organizations identify potential insider threats. For instance, it can detect unusual activities like unauthorized access attempts, large data transfers, or suspicious email communications, which might indicate a disgruntled employee or a compromised account.

Identifying Data Exfiltration Attempts

A primary objective of cybersecurity analytics is detecting and preventing data exfiltration or unauthorized transfer of sensitive data outside an organization's network. Analytics identifies patterns indicative of data exfiltration, including abnormal file transfers or unusual network traffic patterns involving critical systems or databases.

Monitoring Remote and Internal Employee Activity / Identifying Insider Threats

The rise of remote work and the increased use of cloud-based services have made advanced cybersecurity solutions more vital than ever. Cyber analytics monitors the activities of remote and internal employees, helping companies detect potential threats like unauthorized access attempts, policy violations, or suspicious behavior that could compromise data security.

Detecting Compromised Accounts

Cybersecurity software analyzes user behavior, login patterns, and other indicators of suspicious activity to identify accounts that have been compromised. This helps prevent further unauthorized access and mitigates risks associated with compromised accounts.

Demonstrating Compliance

With its detailed logs and audit trails, cybersecurity analytics assists organizations in demonstrating compliance with security standards and regulations, including with GDPR, HIPAA, CCPA, and PCI DSS. This can be especially important for companies that must adhere to their industry’s strict data privacy and security requirements.

Investigating Incidents

When a security incident occurs, cybersecurity analytics helps in the investigation process by providing insights into the nature and scope of the incident, identifying potential root causes, and recommending appropriate remediation steps.

Detecting Improper Use of User Accounts

Cybersecurity solutions monitor user account activity to detect improper or unauthorized use, such as attempting to access restricted data or systems, sharing credentials, or engaging in other activities that violate security policies.

Advanced Persistent Threat (APT) Detection

APTs are sophisticated, targeted cyber attacks adept at evading traditional security measures. By analyzing complex patterns and identifying subtle indicators of these advanced threats, cybersecurity analytics helps businesses detect and respond to APTs effectively.

Leveraging Big Data in Cybersecurity Analytics

Fortunately, the tools for managing and analyzing data continue to strengthen and evolve, providing organizations with more sophisticated and effective ways to use their data for strategic decision-making and improved operational efficiency. However, businesses still face significant challenges when analyzing their data for cybersecurity purposes, such as:

  • Data volume. Data can accumulate rapidly from various sources, including network traffic logs, endpoint activity, and security tools, resulting in massive volumes of data that need to be processed and analyzed.
  • Data variety. Data comes in various formats such as structured (e.g., logs), semi-structured (e.g., XML), and unstructured (e.g., emails, documents), making it challenging to integrate and analyze.
  • Data velocity. Data is generated at high speeds, requiring real-time or near-real-time analysis to detect and respond to threats promptly.
  • Data integrity. Ensuring data accuracy and reliability can be challenging due to the presence of inconsistencies, noise, and potential tampering.
  • Data privacy and compliance. Handling sensitive data requires strict adherence to data privacy regulations and compliance standards.

To leverage big data for cybersecurity analytics, organizations have at their disposal several techniques:

  • Distributed computing frameworks. Systems like Apache Hadoop and Google Cloud Dataflow allow for the distribution of computational tasks across multiple machines or network nodes. They make it easier to process large datasets and execute complex algorithms, dividing the work into smaller and more manageable chunks that can be processed simultaneously while providing scalability and high-performance computing capabilities.
  • In-memory computing. Technologies like Apache Ignite and GridGain enable organizations to store and process large amounts of data in memory, enabling real-time analysis and decision-making.
  • Stream processing. Platforms like Apache Kafka and Apache Flink facilitate the intake and processing of continuous data streams, enabling real-time threat detection and response.
  • ML and AI. These advanced analytics models help organizations identify complex patterns, anomalies, and potential threats within large datasets.
  • Data visualization and reporting. Tools like Elasticsearch, Kibana, and Splunk, help organizations understand and communicate insights gleaned from cybersecurity data analysis.

The benefits organizations enjoy when applying big data analytics to cybersecurity are numerous. By leveraging advanced techniques to process and analyze large volumes of diverse data, they:

  • Gain a holistic understanding of their cybersecurity posture.
  • Can detect and respond to threats more effectively by identifying complex patterns and anomalies that may be difficult to uncover through traditional methods.
  • Employ real-time analysis and continuous monitoring capabilities to proactively mitigate risks and respond swiftly to emerging threats.
  • Can uncover valuable insights into user behavior, network activity, and potential vulnerabilities, allowing them to strengthen their security measures and enhance their overall cybersecurity resilience.

While AI and ML have long been essential components in cybersecurity analytics, the platforms using them are still in their infancy. As attackers develop increasingly more sophisticated tools that can avoid data breach detection by existing security tools, experts will need to keep researching and studying how these emerging threats are deployed and managed so cybersecurity solutions can be updated to keep pace. This is particularly essential as remote and hybrid work models become the norm, making it more challenging for security teams to ensure the continuity and security of an organization’s environment.

Cybersecurity Analytics: Invest in the Future Today

Cyber attacks are occurring faster and cyber criminals are continually finding novel ways to wreak havoc. Cybersecurity risks are everywhere, impacting everything from data management to customer relations and employee experience. Veritas provides cloud-based solutions that help businesses build resilient cybersecurity strategies that leverage advanced tools like AI and ML to help them proactively identify, mitigate, and respond to emerging cyber threats, ensuring maximum protection of their digital assets while maintaining customer and stakeholder trust.

Veritas offers an integrated portfolio of compliance and governance solutions that consolidate intelligence across data sources to surface relevant information, deliver actionable insights, and reduce the risk of costly regulatory fines. We’re proud to be named a Leader in the Gartner Magic Quadrant for Enterprise Information Archiving, as it recognizes our commitment to delivering market-leading, cloud-centric solutions that address data and regulatory complexity for our customers.

Contact us online to learn more about how we can help your enterprise take a holistic approach to cybersecurity analytics.


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