Cyber threat hunting is a proactive approach to internet security that involves actively searching for security risks within an organization’s network. Unlike passive strategies like automated threat detection systems, threat hunting aims to find previously undetected or unknown threats that may have evaded traditional defenses. It is an essential component of any defense strategy as it helps organizations stay ahead of the latest cyber threats and rapidly respond to potential attacks.
What Is Cyber Threat Hunting?
Cyber threat hunting is the practice of proactively searching for cyber threats that may be lurking undetected in a network. It goes beyond relying solely on automated defenses and digs deep to find malicious actors who have successfully evaded initial endpoint security measures. Once an attacker infiltrates a network, they can remain undetected for months, collecting data, looking for confidential material, or obtaining login credentials to move laterally across the environment. Threat hunting aims to identify and neutralize these threats before they cause significant damage.
How Does Cyber Threat Hunting Work?
Cyber threat hunting combines the human element with the processing power of software solutions. Human threat hunters leverage data from complex security monitoring and analytics tools to proactively identify and neutralize threats. They use their intuition, strategic thinking, and creative problem-solving skills to complement automated threat detection tools. This human-powered effort enables organizations to implement threat resolutions faster and more accurately.
Threat Hunting Methodologies
There are three main methodologies in threat hunting:
a. Hypothesis-driven investigation:
Threat hunters investigate new threats identified through a large pool of crowdsourced attack data. They look for specific attacker behaviors within their own environment. This approach helps identify if the attacker’s tactics, techniques, and procedures (TTP) are present in their network.
b. Investigation based on known Indicators of Compromise or Indicators of Attack:
Threat hunters leverage tactical threat intelligence to catalog known IOCs and IOAs associated with new threats. These become triggers for uncovering potential hidden attacks or ongoing malicious activity.
c. Advanced analytics and machine learning investigations:
This approach combines powerful data analysis and machine learning to sift through a massive amount of information. It helps detect irregularities that may suggest potential malicious activity. Skilled analysts investigate these anomalies to identify stealthy threats.
These methodologies combine threat intelligence resources with advanced security technology to proactively protect an organization’s systems and information.
Threat Hunting Steps
The process of proactive cyber threat hunting typically involves three steps: trigger, investigation, and resolution.
a. Trigger:
A trigger identifies a specific system or area of the network for further investigation. It can be based on unusual actions detected by advanced detection tools or a hypothesis about a new threat. For example, a security team may search for advanced threats that use fileless malware to evade existing defenses.
b. Investigation:
During the investigation phase, threat hunters use technology like Endpoint Detection and Response (EDR) to conduct a deep dive into potential malicious compromises of a system. The investigation continues until the activity is deemed benign or a complete picture of the malicious behavior is obtained.
c. Resolution:
The resolution phase involves communicating relevant intelligence about malicious activity to operations and security teams. This enables them to respond to the incident and mitigate threats effectively. The data gathered about both malicious and benign activity can also be used to improve the effectiveness of automated technology without further human intervention.
What Are the Top Challenges of Cyber Security Hunting?
Implementing a successful cyber threat hunting program comes with its own set of challenges. The top challenges organizations face include:
Deploying expert threat hunters:
Finding and retaining skilled threat hunters who possess deep knowledge of the threat landscape and can quickly identify warning signs of sophisticated attacks can be a challenge. The shortage of experienced threat hunters in the cybersecurity industry adds to the difficulty.
Collecting comprehensive data:
To effectively hunt for threats, organizations need access to a wide range of data that provides visibility across their entire infrastructure. However, collecting and aggregating this data from various sources can be a complex and resource-intensive task.
Staying up-to-date with threat intelligence:
Threat hunters need access to the latest threat intelligence to compare it with internal data and identify potential network threats. However, keeping up with constantly evolving threats and trends requires continuous monitoring and analysis of threat intelligence sources.
Overcoming these challenges requires organizations to invest in recruiting and retaining skilled threat hunters, implementing robust data collection and aggregation processes, and establishing strong partnerships with threat intelligence providers.
How Does Extended Storage Help With Threat Hunting?
Extended storage of security data plays a crucial role in threat hunting. By retaining security data for extended periods, threat hunters can extract enhanced visibility and threat context from real-time and historical data. This enables them to proactively search and uncover hidden threats in the environment, detect irregularities that may suggest potentially malicious behavior, and prioritize and address vulnerabilities before they can be exploited.
Extended storage allows threat hunters to quickly search and correlate disparate data sets, gaining new insights and a clearer understanding of the environment. By unifying multiple log sources, including security detections and threat intelligence, threat hunters can better define and narrow the scope of detections, resulting in fewer false positives. With enriched security telemetry and extended storage, security teams gain the necessary visibility and context for their investigations, accelerating the detection and response to potential threats.
Threat Hunting Maturity Model
The threat hunting maturity model defines an organization’s level of maturity in terms of its cyber threat hunting capabilities. The model takes into account factors such as the quantity and quality of data collected, the organization’s ability to hunt and respond to threats, the toolsets and technologies used, and the level of analytics employed.
The SANS Institute outlines the following levels of maturity in the threat hunting maturity model:
Initial: At this level, the organization relies primarily on automated reporting and does little or no routine data collection.
Minimal: The organization incorporates threat intelligence indicator searches and has a moderate or high level of routine data collection.
Procedural: The organization follows analysis procedures created by others and has a high or extremely high level of routine data collection.
Innovative: The organization creates new data analysis procedures and has a high or extremely high level of routine data collection.
Leading: At the highest level of maturity, the organization automates the majority of successful data analysis procedures and has a high or extremely high level of routine data collection.
