Leveraging Data Enrichment in Cyber Threat Intelligence

Posted on by

Cyber Threat Intelligence (CTI) is a critical component of any cybersecurity strategy. It helps security teams identify, analyze, and mitigate potential threats (which may try to sneak through in the form of raw, unprocessed data) before they can inflict damage. However, raw threat data alone lacks the context, subtlety, and meaning needed to make it useful for operational decisions. This is where data enrichment comes into play. 

Enrichment — enhancing raw threat data with context, relevant information, and deeper insights — transforms it into actionable intelligence that assists threat detection, incident response, and overall decision-making. For example, an IP address flagged as suspicious becomes much more valuable when enriched with geographic data, previous activity logs, or its association with known malicious campaigns or threat actors.

This blog explores how data enrichment can be leveraged in CTI, focusing on:

  • the operational advantages
  • constraints related to specific types of data sources
  • the challenges in contextualizing disparate sources
  • its broader impact on security operations

Types of Data Enrichment Applied to Threat Intelligence

Several types of data enrichment can be applied to CTI, each bringing unique value to the intelligence process:

  1. Geolocation data: Enriching data with geolocation details helps analysts determine where an IP address or domain operates. An example might be tracing a phishing campaign back to an IP in a region known for cybercrime activity.
  2. Threat actor information: Associating threat data with known threat actors or groups provides insights into their tactics, techniques, and procedures (TTPs). For instance, recognizing a particular malware strain as part of an advanced persistent threat (APT) group’s arsenal may help analysts anticipate the threat actor’s likely next move.
  3. Domain and IP reputation: Enrichment with domain and IP reputation databases can indicate whether an entity has a history of being active in attacks, phishing, or command-and-control (C2) infrastructure breach attempts. This insight helps prioritize threat response efforts by highlighting more dangerous, persistent, and/or immediate threats.
  4. Malware and file hash information: Enriching indicators of compromise (IOCs) like file hashes with metadata — such as the malware family they belong to or their behavior in sandboxes — allows analysts to quickly categorize and understand the pedigree and, therefore, the severity of a threat.
  5. Vulnerability data: Mapping common vulnerabilities and exposures (CVEs) to threat data can help organizations understand if their systems or software are susceptible to particular exploits being used by an attacker. For instance, an enriched alert that ties a known exploit to a vulnerability in a widely used application helps the security team zero in on where they should prioritize patching efforts.

Constraints of Specific Data Sources and Types

Despite the benefits, data enrichment in CTI faces several constraints, depending on the data source and type. Here are some examples:

  1. Unstructured data from dark web sources: Dark web forums and marketplaces often contain rich intelligence, but the data is highly unstructured and difficult to process. Those who frequent these sites use colloquial language and often keep conversations intentionally fragmented and cryptic, making it tedious to extract meaningful enrichment. Additionally, the veracity of this data is often questionable, posing a risk of misinformation.
  2. Encrypted traffic data: The increasing use of encryption (e.g., HTTPS) can obscure traffic that may provide insights into malicious activity. While IP addresses or metadata can be enriched, the hidden content limits the utility of the information.
  3. Temporal context: Many sources of CTI data, such as phishing URLs or malicious domains, have a deliberately short lifespan. Timely enrichment is critical since the indicators are relevant only during a short window. Outdated enrichment data may lead to false positives or a focus on irrelevant threats.
  4. Incomplete or missing data: Sometimes, data sources lack critical elements, such as the specific malware variant used in an attack or the full details of a network traffic pattern. This can hinder the ability to enrich the data fully and lead to challenges in drawing accurate conclusions.

Challenges of Contextualizing Disparate Data Sources

CTI systems often ingest data from open-source threat feeds, proprietary intelligence, internal logs, government reports, and third-party vendor databases. However, integrating and contextualizing this data into a common operational framework presents several challenges:

  1. Data standardization: Different sources provide data in various formats, ranging from structured (e.g., JSON, XML) to unstructured (e.g., PDFs, text). Standardizing this information into a consistent format for enrichment can be challenging. For example, a report from a government agency (e.g. CISA) may not align with the data format used by a commercial threat feed.
  2. Data volume and velocity: The sheer volume of data from multiple sources can overwhelm the enrichment process. Security information and event management (SIEM) systems and threat intelligence platforms (TIPs) ingest vast amounts of threat data at high velocity, making it difficult to enrich and correlate in real time without significant computing resources.
  3. Conflicting information: Different intelligence sources often provide conflicting information. For example, one intelligence feed may mark an IP as malicious, whereas another may label it as benign or attribute it to a different threat actor. Resolving these discrepancies during enrichment requires sophisticated validation mechanisms.
  4. Contextual gaps: While enriching data, there may be gaps in context. For instance, information from a third-party vendor may highlight a phishing domain but not provide the associated payload details or victim profile, limiting its usefulness in a larger threat landscape.

Advantages of Data Enrichment in CTI

The operational advantages of enriching CTI data are significant, especially when considering how enriched data can impact threat detection, incident response, and proactive defense:

  1. Improved threat prioritization: Enriched data helps analysts prioritize threats more effectively. For example, a basic IOC can be enriched with threat actor attribution, reputation scores, and exploit kits used, allowing security teams to focus on the most dangerous threats.
  2. Accelerated incident response: Contextualized and enriched data leads to faster triage of alerts. For instance, if an alert for malicious activity on an endpoint is enriched with details about the malware’s TTPs, the response team can quickly deploy the correct mitigation measures. While speed always matters, it matters here the most.
  3. Enhanced correlation and detection: Enriched CTI provides a clearer picture of the threat landscape, enabling better event correlation in SIEMs. By correlating enriched threat data across network traffic, endpoint logs, and external feeds, analysts can uncover sophisticated but subtle attack patterns and detect threats earlier in the kill chain.
  4. Automation of low-level decisions: Enrichment supports response workflow automation in security orchestration, automation, and response (SOAR) systems. Enriched indicators can trigger automated workflows for common threats, reducing manual workloads and enabling teams to focus on higher-order threats.

Real-World Examples of Threat Intelligence Enrichment

  1. Anomali ThreatStream: Anomali ThreatStream enriches raw threat data from multiple sources with external intelligence feeds and internal data such as historical logs. This helps organizations identify new threats based on the enriched indicators and respond faster to potential incidents through near real-time correlation with security analytics applications. 
  2. FireEye and APT29: FireEye’s identification of APT29’s (Cozy Bear) cyber-espionage activities involved enriching threat data with information on the group’s past behavior, infrastructure, and TTPs. This allowed security teams to recognize and respond to indicators pointing to APT29 more rapidly.
  3. CrowdStrike’s Falcon Platform: The Falcon platform uses data enrichment to contextualize threat data from endpoint logs with information on attack techniques, adversary group attribution, and vulnerability data. This enriched intelligence enables organizations to detect advanced threats more efficiently and adapt defensive strategies accordingly.

Automate Threat Intelligence Enrichment with Anomali 

Data enrichment is a critical component of operationalizing CTI effectively. Anomali’s TIP, ThreatStream, automates data enrichment to deliver more refined threat intelligence to various stakeholders within an organization, improving and accelerating situational awareness and decision-making.

Anomali’s Security Analytics can correlate enriched threat intelligence from ThreatStream with logs and events, leading to more accurate detection of suspicious behavior across the network ecosystem. Drawing the connection between the external threat to internal telemetry makes the whole process immediately relevant.