Are Cybersecurity Analytics Missing from Your Security Strategy?

Cybersecurity analytics uses advanced data science and artificial intelligence (AI) methods to collect, process, and analyze big data from various sources and systems to better determine how to proactively improve threat detection, defense, and response strategies.

By correlating, aggregating, and visualizing the information contained in datasets from across your environments, cyber analytics can provide holistic, more profound insights than traditional network security solutions focused solely on monitoring one aspect of an IT estate.

The need for robust cybersecurity analytics has never been more urgent than it is today: cyber threats are constantly mutating and evolving at a rate that’s rendering many traditional cybersecurity tools obsolete, digital transformation initiatives have inadvertently expanded cyberattack surfaces, and security experts are still in high demand due to an ongoing gap in sufficient cybersecurity talent. These and many other security issues are now compounding with one another, making it even more challenging for small and large organizations alike to adequately identify and manage cyber risks.

This comprehensive guide will go into more depth about what you need to know about cybersecurity analytics, including the importance and benefits of integrating cybersecurity analytics into your existing risk management, cyber defense, intrusion detection, and incident response processes. We’ll also discuss the critical role Autonomous Endpoint Management (AEM) will play in revolutionizing security analytics to help companies manage and secure endpoints and their IT environments against hackers more effectively.

• What are cyber analytics?
• How do cyber analytics work?
• Security analytics vs. SIEM
• Why cyber analytics are important
• Benefits of cybersecurity data analytics
• How Tanium will play a crucial role in the future of cybersecurity analytics

Introduction to cybersecurity analytics

Learn about the core concepts of cyber analytics, including what they are, how they work, and some key differences between cybersecurity analytics and Security Information and Event Management (SIEM) tools.

What are cyber analytics?

Cybersecurity analytics involves using data science techniques, machine learning (ML), and other types of AI algorithms to gain insights from massive amounts of data. This can enable organizations to create proactive cybersecurity strategies and protocols that better detect, mitigate, and stop cyber threats.

[Read also: Machine learning in cybersecurity: Your all-in-one guide]

How do cyber analytics work?

Cybersecurity analytics tools employ advanced algorithms that can sift through vast amounts of historical and real-time data, such as network traffic, endpoint devices, user activity and behavior, event logs, external threat intelligence feeds, and business applications, to provide a fuller view of an organization’s security posture. This consolidated analysis can enable a more proactive approach to threat detection and response.

Some common data science techniques used with cyber analytics include:

• Statistical modeling: Used to better understand and test assumptions by generating sample data, such as for measuring vulnerability and risks
• Behavior analysis: Used to identify unusual patterns that deviate from understood baseline behaviors through pattern recognition and anomaly detection
• Predictive analytics: Used to better understand past incidents to predict and prevent future attacks
• Data mining: Used to extract useful insights by searching and analyzing large amounts of raw data
• Machine learning: Uses the insights provided by data science techniques to train, improve, and automate processes

When learning about cybersecurity analytics, you may also run into another related term: SIEM. While SIEM and security analytics are related, they are two distinct concepts.

Security analytics vs. SIEM

Security analytics and SIEM systems can play similar roles in the cybersecurity ecosystem when it comes to data collection, analysis, and improving threat response. This section discusses some of the differences and how each security solution contributes to a robust cybersecurity approach.

Traditional SIEM systems are designed to gather and analyze data flows, log data, alerts, and event data from many monitored sources, such as network devices, computers, storage systems, and firewalls. By consolidating this information, SIEM can help with compliance efforts, provide an overview of network health, and identify security-related incidents on individual devices. Legacy SIEM tools typically use rule-based methods built to match patterns of previously detected threats to detect deviations from normal operations.

However, with the composition of the typical corporate network infrastructure changing to support remote, hybrid, and in-office employees, SIEM solutions may need help keeping crucial data up-to-date, especially when populating configuration management databases (CMDBs).

[Read also: Learn how Tanium integrations are helping organizations maximize their ServiceNow investments with real-time endpoint data]

When identifying cyber threats, legacy SIEM systems are often limited to identifying only known threat patterns through signature-based, also known as rule-based, detection. Signature-based detection requires that any threat patterns behave a certain way and looks for these behaviors to detect attacks, meaning SIEM systems may struggle to detect new types of cyberattacks that do not fit the mold of how traditional attacks work — like malware that can evade detection by disguising itself as legitimate files or phishing scams that leverage social engineering techniques to trick employees into conducting attacks through malicious links. Not only does this inability to detect new cyberattacks lead to false positives due to the amount of data the SIEM is trying to parse through, but it can also lead to many false negatives and missed opportunities to remediate potential incidents caused by emerging threats.

Like SIEM solutions, cyber analytics tools can process multiple data types, including data from internal and external sources. Since this processing power also involves big data capabilities, cybersecurity analytics can provide a greater depth of analysis to enhance predictive efforts and better inform proactive security incident prevention and response.

Through machine learning and behavioral analytics, cyber analytics tools can also continually learn how to identify new patterns, anomalies, and emerging threats, tune alerts to only escalate the highest priority issues, and eliminate the creation of false positives, meaning every alert is more meaningful, actionable, and further able to protect your environment (without adding noise to your workload).

Today, modern SIEM systems are integrating key security analytics features to address limitations posed by legacy SIEM solutions. For example, some SIEM tools are incorporating AI capabilities and increasing the amount of data sources that can be analyzed, closing the gap between what SIEM and cybersecurity analytics tools can do.

Overall, many cybersecurity professionals still consider SIEM solutions to be a reactive and compliance-oriented approach to security data management, while cybersecurity analytics is emerging as a more proactive and business-oriented approach to help organizations optimize their cybersecurity strategies, cyber hygiene, and cyber defense.

Why cyber analytics are important

The growing number of threats and substantial costs of cybersecurity incidents can be seen as the catalyst for the rising interest in the field of cyber analytics. Ransomware attacks, for example, have doubled every year since 2019, according to cybersecurity trend research from McKinsey.

The advancement of big data security analytics is also driven by the increased use of AI by cyber attackers. By harnessing the power of AI in cybersecurity, enterprises must work to level the playing field when defending their assets and data from cybercriminals.

Bottom line: Without a comprehensive understanding of your attack surface, monitoring, safeguarding, and taking the actions needed to protect IT environments is nearly impossible. By collecting, processing, and analyzing data from various sources, cybersecurity analytics can provide actionable insights that enable organizations to make more informed decisions and take faster, more effective responses and proactive measures.

[Read also: Here’s the 3 biggest GenAI threats to know today – and how to defend against them]

Benefits of cybersecurity data analytics

While detecting security threats faster is often seen as the primary benefit of cybersecurity analytics, these deeper insights can also support other important use cases, such as providing predictive analytics to help security teams anticipate breaches before they occur, bolstering proactive decision making using data-driven insights, and assessing improvement opportunities to strengthen future defenses.

Let’s explore some of the fundamental use cases for cybersecurity analytics and how these features can help you fortify your organization’s cyber resilience, improve threat detection, prioritize the most critical actions, and measure the impact of security investments.

Faster threat response

It’s true — the bedrock of effective cybersecurity is fast detection and mitigation of threats.

Cybersecurity analytics tools can help organizations accelerate mean-time-to-respond (MTTR) by using machine learning capabilities to triage more quickly and prioritize alerts based on severity and risk. By prioritizing incidents based on the business context of what’s happening within environments and the highest risk areas, teams can respond to the most impactful threats during the early stages of attacks and well before malicious actors can compromise the confidentiality, integrity, and availability of critical data and systems.

Forecast cyber threats through anomaly detection

Security analytics platforms can be used to hunt for potential threats by correlating information, analyzing patterns, uncovering trends, and identifying potential indicators of compromise, such as unusual user traffic or suspicious activity, which might go unnoticed by conventional cybersecurity solutions or manual data analysis.

Using behavior-based detection methods, cyber analytics can reduce mean-time-to-detect (MTTD) and help organizations identify zero-day vulnerabilities and unknown attacks faster. By predicting potential threats before they can happen, cybersecurity analytics insights enable security teams to act before threats escalate into breaches. Instead of responding to threats as they occur, cybersecurity analytics insights become a strategic tool to help organizations shift their cybersecurity postures from reactive to proactive — an increasingly necessary security approach for today’s ever-changing threat landscape.

Build a proactive cyber defense

Cybersecurity analytics helps support a proactive approach to cybersecurity by generating insights that can be used to bolster the continuous improvement and optimization of cybersecurity policies, frameworks, procedures, and controls.

By measuring performance and benchmarking against industry standards and best practices, cybersecurity analytics can help organizations evaluate and optimize the effectiveness of existing security tools and processes. This foresight allows organizations to identify potential gaps, shore up defenses in high-risk areas, adjust security policies, remediate vulnerabilities, and conduct targeted employee training to mitigate risks and better prevent cyber incidents.

Improve decision making

Robust cybersecurity analytics can provide a data-driven foundation for strategic decision making, such as:

• Where to prioritize security investments and allocate resources for maximum impact
• Identifying redundant systems that can be consolidated or replaced to improve IT cost optimization efforts
• Balancing security measures with operational efficiency by assessing the effectiveness and ensuring security protocols do not hinder productivity

Organizations can use cybersecurity analytics to maintain strong defenses against cyber threats while aligning security goals and metrics with business objectives and outcomes.

Deeper forensic analysis

In the aftermath of a security incident, big data analytics can aid in forensic and root cause analysis efforts by helping organizations piece together the timeline, method of attack, and attack impact, such as the scope of potential data loss, using contextual information from data sources located across the environment.

Machine learning models can also be used to analyze past security incidents and simulate different attack scenarios to help teams better predict where vulnerabilities may be exploited, uncover insights to strengthen defenses, and enable faster and more accurate responses to new cyber incidents.

Support compliance

Cybersecurity analytics can help demonstrate compliance by providing evidence of how an organization follows best practices and guidelines for protecting its data and systems. By collecting, processing, and analyzing data from different sources, organizations can use cybersecurity analytics insights to create reports that show the status and performance of existing security controls, policies, regulatory requirements, industry standards, and procedures. These reports can prove compliance and help auditors, regulators, and customers verify the security and privacy of an organization.

In an era where regulatory compliance and data protection standards are increasingly stringent, cybersecurity analytics tools can help organizations identify and prioritize areas needing improvements, address gaps, and resolve other risks affecting compliance, protecting them from potential legal and financial repercussions by avoiding penalties or breaches.

How Tanium will play a crucial role in the future of cybersecurity analytics

Cybersecurity analytics tools are indispensable for safeguarding digital assets, maintaining operational continuity, and preserving organizational reputation in a landscape where cyber threats constantly evolve and expand. However, an equally important task needed to reap the most benefits of cybersecurity analytics is the ability to act on these insights to shore up all the possible entry points into your environment in a faster, easier, and safer way: introducing Autonomous Endpoint Management (AEM).

AEM… blends unified endpoint management, digital employee experience, and AI, facilitating a comprehensive and autonomous endpoint management approach.¹

To address this industry need, our methodology around AEM represents an innovative leap forward in AI-driven automation for the digital workplace, marking a significant shift in data handling and operational efficiency.

Our goal for AEM at Tanium is to extend cybersecurity analytics even further by incorporating composite AI to deliver intelligent automation and decision-making for IT endpoint management.

The Tanium model for AEM evolves from our cloud-first XEM platform, enhancing efficiency with AI-powered, autonomous features like configuration enforcement and threat resolution. Its foundational capability will leverage live data and AI insights to autonomously recommend actions aligned with each organization’s success metrics and risk tolerance.

Autonomous Endpoint Management allows you to get ahead of routine IT and security issues, but in a way that is always going to be safe, where you’re always going to be in control.

Our vision is to enable security teams to make smarter, more efficient decisions, decrease reliance on manual processes, and boost security and operational performance by proactively addressing IT and security tasks in a secure and controlled manner.

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Our approach to AEM is currently under development and is slated for release in the summer of 2024. We invite you to contact us for a demo or register for an upcoming event near you to see it in action.

¹McDowell, Steve. “Tanium’s First-Mover Advantage in Autonomous Endpoint Management.”Forbes, Published 2024 年 1 月 18 日.