Certain organizations attempt to integrate threat data feeds into their network; however, they often struggle with managing the substantial influx of data. Consequently, analysts may find it challenging to determine what information is pertinent and what is not, thereby burdening them further due to the lack of suitable tools to process and prioritize the data.
The most effective solutions leverage machine learning to automate the collection and processing of data. These solutions are capable of integrating with pre-existing systems and consolidating unstructured data from a variety of sources. By analyzing this information, they can provide context on indicators of compromise (IoCs) and the tactics, techniques, and procedures (TTPs) used by threat actors, effectively connecting the dots and aiding in incident response.
If threat intelligence is treated as a separate function within a security team, instead of being considered an integral component that complements all other functions, it may result in many individuals who could benefit from it not having timely access to it. Over time it is becoming a central resource in the structural cyberdefence.
Threat intelligence is typically divided into three distinct subcategories:
Stakeholders and consumers of strategic intelligence can include:
Stakeholders and consumers of tactical threat intelligence can include:
Stakeholders and consumers of operational threat intelligence can include:
Distinguishing relevant threats and helping triage incidents/events.
Away from 'patch everything'
Threat actors are also quicker — it now only takes fifteen days on average between a new vulnerability being announced and an exploit targeting it appearing.
This has two implications:
5 Use Cases