What is Cyber Threat Hunting?

Cyber threat hunting is a proactive security strategy that involves searching for threats within a network before they can cause significant damage. Unlike traditional methods, which are reactive and wait for an alert before taking action, threat hunters seek to actively identify and mitigate hidden threats that have evaded initial security measures. Threat hunters leverage continuous monitoring and data analysis to spot suspicious behavior from malicious actors that may indicate a cyber attack.

In this blog, we’ll explore everything you need to know about threat hunting and how it helps organizations like yours improve their cyber resilience.

Why is threat hunting important?

In today’s complex digital landscape, cyber threats are continually evolving, and traditional defense mechanisms alone are not sufficient. Threat hunting provides an additional layer of security, and here are a few reasons why it’s crucial.

Understanding the critical benefits helps organizations prioritize their security investments effectively.

• Proactive Defense: Instead of waiting for an alarm, threat hunting actively searches for potential threats, allowing for faster detection and response.
• Advanced Threat Detection: Threat hunting can detect advanced persistent threats and persistent threats that traditional methods often miss. By reducing dwell time, the period between initial compromise and detection, organizations can significantly limit potential damage from data breaches.
• Risk Reduction: Threat hunting can mitigate damage and reduce risk by identifying threats earlier.
• Better Understanding of Threats: Threat hunting can help your organization understand the types of threats it faces, improving future defenses and strategies.

Collectively, these benefits strengthen an organization’s overall information security posture and resilience against evolving cyber threats.

What are the types of threat hunting?

Based on their methodological approach, threat hunting can be categorized into two main types. Each type serves different organizational needs and complements existing security frameworks.

Hypothesis-based hunting

Hypothesis-based hunting involves creating a hypothesis about a potential threat based on threat intelligence, prior knowledge, or instinct, and then investigating whether the threat exists in the network. This approach often incorporates threat modeling methodologies to systematically analyze potential attack vectors and prioritize hunting activities based on organizational risk profiles.

Analytics-driven hunting

Analytics-driven hunting is when security teams use advanced analytical tools and security analytics platforms to find patterns or anomalies in data that may signify a security threat.

Both approaches work most effectively when integrated with comprehensive security data collection and analysis capabilities.

What are threat-hunting examples?

Threat hunting can take many forms. One example might involve a security analyst investigating an unexpected increase in outbound network traffic from a particular server, which could indicate data exfiltration by an attacker. 

Another could involve an analyst following up on a suspicious login attempt from an unfamiliar IP address, potentially suggesting a brute-force attack on the network.

Threat hunters may also focus on detecting insider threats by analyzing user behavior patterns, access logs, and data movement to identify employees or contractors who may be acting maliciously or whose credentials have been compromised. These investigations often reveal subtle anomalies that automated systems miss, such as unusual file access patterns or off-hours system usage that doesn’t align with normal job functions.

What are threat-hunting techniques?

Some of the more common threat hunting techniques include anomaly detection, pattern recognition, threat intelligence feed comparisons, and hypothesis creation. Modern hunting approaches also leverage frameworks like MITRE ATT&CK to systematically identify adversary tactics and techniques across the attack lifecycle. Each has its own advantages, and knowing when and where to use them can significantly help your organization.

Anomaly detection

Anomaly detection is a technique used in data analysis to identify patterns or observations in a dataset that deviate significantly from established norms. These detected anomalies, often referred to as outliers, may indicate critical incidents such as bank fraud, medical problems, or errors in a text. 

In cybersecurity, anomaly detection is particularly valuable for identifying suspicious activities that could represent threats or intrusions, offering a proactive approach to threat detection and mitigation.

Pattern recognition

Pattern recognition is a branch of machine learning that focuses on the automatic detection of regularities or repeating elements in data. It involves training a model using a dataset so that the model can later recognize and classify new data based on the learned patterns. Applications of pattern recognition span various fields, such as image and speech recognition, bioinformatics, data mining, and cybersecurity, where it can be used to identify and respond to threats based on previously observed patterns of malicious activity.

Threat intelligence feed comparison

Threat feeds comparison refers to the process of evaluating and contrasting different sources of threat intelligence data in order to determine their relative value, accuracy, and usefulness for an organization’s security needs. These feeds provide timely and actionable information about known threats such as IP addresses, URLs, or file hashes associated with malicious activities. 

By comparing various feeds, organizations can assess their coverage, false positive rates, update frequency, and other factors, helping them to select the most suitable threat intelligence sources and optimize their cybersecurity strategy.

Hypothesis creation

Hypothesis creation is a fundamental step in the scientific method and research processes. It involves formulating a testable prediction based on observation or existing knowledge. This process requires developing a proposed explanation or educated guess for a specific phenomenon, which can then be tested through experimentation or further observation. 

In the context of data science, AI, or cybersecurity, hypotheses might be created to explain certain trends in data, anticipate user behavior, or predict potential security threats.

What tools are used for threat hunting?

The tools that you use depend on your organization’s attack surface and how much you are looking to protect. Several tools can assist in cyber threat hunting, such as SIEM systems, EDR systems, AI and machine learning, and threat intelligence platforms. Organizations often integrate vulnerability scans, penetration testing results, and red teaming exercise findings to enhance their hunting capabilities and validate security controls.

Security Information and Event Management (SIEM) systems

Security Information and Event Management (SIEM) systems are integral tools in cybersecurity that provide real-time analysis of security alerts generated within an organization’s network. These systems collect and aggregate log data generated across the network’s hardware and software infrastructure, from host systems and applications to network and security devices. 

By identifying patterns and anomalies that may suggest a security threat, SIEM tools aid in detecting and responding to incidents, providing valuable security data for compliance reporting, and improving overall security posture.

Endpoint Detection and Response (EDR) systems

Endpoint Detection and Response (EDR) systems are cybersecurity tools that monitor and analyze endpoint device activities to identify, prevent, and respond to potential threats. These systems collect and store data from endpoints, employing advanced analytics to detect suspicious behavior or indicators of compromise. 

In the event of a security incident, EDR systems provide comprehensive insight and response capabilities, allowing security teams to investigate and mitigate threats rapidly.

Artificial Intelligence and Machine Learning tools

Artificial Intelligence (AI) and Machine Learning (ML) tools are computational technologies used to create systems capable of learning from data, making predictions, and automating decision-making processes. 

AI is the broader concept of machines performing tasks that would normally require human intelligence, while ML is a subset of AI involving using algorithms to parse data, learn from it, and then make a determination or prediction. 

These tools are widely used across various industries, from healthcare to finance, enabling advancements in areas like image recognition, natural language processing, and predictive analytics.

Threat Intelligence Platforms

Threat Intelligence Platforms (TIPs) are security tools that help organizations collect, correlate, and analyze threat data from various sources to support defensive actions against cybersecurity threats. They gather data on potential threats, such as indicators of compromise (IOCs) and tactics, techniques, and procedures (TTPs) used by attackers, and provide actionable insights to proactively defend against future attacks. TIPs aid in real-time threat detection and contribute to strategic decision-making, incident response, and risk management by providing a better understanding of the threat landscape.

What is threat hunting vs detection?

Understanding the distinctions between various security approaches helps organizations build comprehensive defense strategies.

• Threat detection is a passive process that relies on automated systems to identify threats and generate alerts.
• Threat hunting is a proactive process in which security analysts actively search for threats that may have bypassed the initial security measures.

This proactive approach complements other information security practices and provides deeper visibility into potential security gaps.

What is cyber threat hunting vs. TTP hunting?

TTP stands for Tactics, Techniques, and Procedures, and it refers to the patterns of behavior exhibited by cyber attackers. TTP hunting is a part of cyber threat hunting, focusing on identifying these behaviors to predict and prevent attacks.

While cyber threat hunting refers to the broader proactive pursuit of threats in a network, TTP hunting is a more specific methodology that focuses on detecting the modus operandi of attackers.

What is the difference between threat hunting and threat intelligence?

Clarifying these related but distinct concepts helps organizations understand how they work together.

• Threat intelligence is the process of gathering and analyzing information about potential threats to inform security decisions. It’s about understanding the landscape of threats, including the tactics, techniques, and procedures attackers use.
• Threat hunting is the proactive search within your network for threats that have evaded your security measures. It uses insights derived from threat intelligence to actively search for hidden threats in the network.

These approaches work synergistically to provide comprehensive threat awareness and response capabilities.

In summary, threat hunting is a crucial aspect of an effective cybersecurity strategy, given the sophisticated and evolving nature of modern threats. By employing the right techniques and tools and continually refining your practices, you can better protect your network and your organization’s valuable data.

Take your threat hunting to the next level with SecurityScorecard

Modern threat hunting requires more than traditional tools. You need intelligence that actually moves the needle. We’ve spent over a decade building the infrastructure that gives security teams the visibility they need to hunt effectively and respond faster to emerging threats.

Here’s what makes the difference in your hunt.

Real intelligence that matters

Our threat hunters work with threat hunters. The intelligence we deliver gets collected, curated, and analyzed by teams who understand exactly what you’re facing. When adversaries evolve their tactics, techniques, and procedures, we’re already tracking those changes across our global sensor network.

This isn’t just another threat feed. It’s intelligence that helps you identify the patterns and anomalies that actually matter to your organization.

Stop the manual research grind

We know how much time gets wasted digging through vulnerability data. Our vulnerability intelligence puts everything you need in one central hub with the context that illuminates every facet of a vulnerability.

Less time hunting means more time remediating. The insights we provide help ensure nothing gets missed in your threat hunting activities.

Seamless integration with your existing stack

Your SIEM, SOAR, and threat intelligence platforms work better when they have quality data. Our intelligence feeds pipe real-time, context-rich cyber intelligence directly into your existing security infrastructure.

The integration happens seamlessly. Your threat hunting workflows get enhanced without disrupting what already works.

See beyond your perimeter

Today’s threats don’t respect organizational boundaries. Your supply chain represents one of the largest attack surfaces your organization faces.

We provide comprehensive visibility into threats targeting your entire ecosystem. You get contextualized insights and a global view of threat actors, CVEs, and attack patterns across all third-party vendors.

The visibility extends your hunting capabilities far beyond what traditional tools can deliver.

Ready to see what better threat hunting looks like? Our threat landscape intelligence strengthens security operations and improves your organization’s ability to proactively identify and mitigate threats.