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Threat hunting refers to the practice of cyber threat hunting wherein cyber security experts proactively try to identify unknown or ongoing threats that have so far eluded an organization’s security network. Security analysts often use cyber threat hunting as an active information security strategy that focuses primarily on finding indicators of compromise (IoCs), hacker tactics, techniques, and procedures (TTPs), and threats such as Advanced Persistent Threats (APTs). Threat hunting works through searching the organizational network iteratively to find threats eluding the current security system. For a more detailed understanding of threat hunting in your specific organizational context, please refer to Managed IT Services Joplin.

The importance of threat hunting

As cyber-attacks have peaked alongside the pandemic, the discovery of increasingly sophisticated threats has put businesses and security analysts on high alert. Threat hunting enables analysts to remain constantly on the lookout for sophisticated threats that may have the ability to get past all automated cybersecurity measures. Even with advanced threat protection, automated security tools and tier 1 and 2 security operations centers (SOCs) are only able to deal with about 80% of threats. It’s the remaining 20% that worries security analysts as they are much more likely to contain advanced threats capable of causing the greatest damage. Given historical trends, these kinds of threats are sophisticated enough to infiltrate any network and avoid detection for up to 280 days on average. With the right threat hunting strategy in place, companies can effectively reduce the time from intrusion to discovery – potentially mitigating a significant portion of the damage.

As cyber defense strategies have grown more capable of repelling generic attacks, attackers have chosen to become more patient and focus on avoiding detection at all costs. Malicious actors now deliberately choose to lurk for weeks, or months to slowly gain access to more secure parts of the network or slowly leech away data and confidential information before discovery. Estimates from the “Cost of a Data Breach Report 2020,” indicate that the average cost of a data breach is almost USD 4 million and the cumulative impact can linger for years. Needless to say, the longer the time period between threat activation and response deployment, the larger is the financial and cumulative impact for the organization. If you’re looking for proactive threat hunting services, please contact Business Continuity Services.

Key Cyber Threat Hunting Characteristics

Being proactive pays

Effective security analysts realize that they simply cannot wait around for an alert from existing security tools. By then, it may already be too late. They need to be constantly on the hunt for threats proactively so that they can respond well in time to mitigate the impact of the threat.

Learning to listen to the gut

Some of the most effective threat hunters in the business generally tend to avoid relying too heavily on conclusive alerts from smart tools and rule-based detections. Their approach could be seen as ‘old world’, but they continue to sniff around for technical signs and listen to their gut. Once they hit upon a concrete bedrock, they can use their findings to build stronger automated threat detection rules.

Following traces

Threat hunting relies on the assumption that the compromise has happened and there must be traces left in the network that hunters can follow to detect the breach. This makes it compulsory for security analysts to follow up on all traces and leads fully.

Being Creative

Attackers capable of evading sophisticated defense mechanisms need threat hunters who are not limited by rules and are always looking to be inventive and creative with their methodologies (established or not).

What are Prevalent Threat Hunting Techniques?

Know how to threat hunt herewith:


Searching refers to looking for specific artifacts with the help of clearly-defined search criteria and querying evidential data, such as full packet data, flow records, logs, alerts, system events, digital images and memory dumps. This is where the skill of the threat hunters comes into play as they have no starting point from which to start looking for threats. It is critical for them to balance out between not making the search criteria too broad or too constricted. Either of those can lead to suboptimal results as it’s for threat hunters to get overwhelmed by too many results or miss out on critical threats with too few.


This technique makes use of machine learning and AI technology to separate clusters of similar data points on the basis of distinct characteristics from a larger data set. Clustering enables analysts to more effectively leverage the full picture of the available data to find the relevant parts, find similarities and/or unrelated correlations, and navigate those insights cleverly to glean a comprehensive view of the happenings within the organization’s network.


This technique uses pre-set search criteria to take multiple unique artifacts and pinpoint all instances of multiples of them appearing together. Grouping is distinct from clustering in as much as it involves searching an explicit set of items already marked suspicious.

Stack Counting

Stack Counting or Stacking counts the instances of occurrences for values of a particular type of data and analyzes the outliers of those results. Stacking is useful when dealing with data sets capable of producing a finite number of results with specific inputs. For more elaboration on trait hunting techniques, please refer to IT Support Joplin.