Cyber threat data from multiple sources overwhelm today's Security Operations Centers (SOCs). Bombarded by millions of threat data points every day, it can seem impossible for security teams to look through it all manually in order to understand and prioritize what matters most to their organizations. Moreover, one of the most daunting challenges for security analysts is making sense of all the threat intelligence derived from a variety of sources that their organizations have subscribed to—commercial feeds, open source, government, industry sharing groups, researcher’s blogs, etc. It becomes monotonous and takes a lot of time as analysts struggle to separate the important information from the noise, amongst a barrage of threat data and false positives. The problem is while too much data is consumed on a daily basis, they need to make sense and help in connecting the dots to maintain a threat-free environment. Otherwise, this only leads to information overload and alert fatigue which further increases the chance of overlooking a genuine threat.
How Does Normalization Solve this Problem?
Threat data comes in various formats. It can be structured data such as a compressed JSON file, a txt file, STIX format, Snort signature, and Yara rules from GitHub repositories and network logs. Some are also available in an unstructured format, aggregated from news articles, blogs, tweets, security industry reports, emails, and so on. To streamline security operations and accelerate detection and response against threats, it is imperative to structure the threat data into a common standard for advanced analysis, correlation, sharing and actioning. Normalization helps get rid of the complexities posed by unstructured data by organizing the ingested threat data in a structured manner so that it can be consumed, analyzed, shared, or actioned easily. And it isn’t just about the structure. The volume of information across the threat intel landscape is high and at times this leads to confusion when different threat groups use different names to refer to the same thing. Normalization also compensates for this by bringing all related data closer to a standard state which can be used further by security teams for deeper investigation.
What is Threat Data Normalization?
Threat data normalization is the process of transforming threat data from its original format into a standardized and structured format to create meaningful intel. This is a significant stage in the cyber threat intelligence lifecycle as normalization makes the flood of data processable, enabling organizations to tease out accurate and actionable threat intelligence from raw data.
Formatting Threat Data into STIX
Structured Threat Information eXpression, commonly referred to as STIX is one of the popular formats followed to normalize and organize the threat data. STIX format was developed by MITRE for describing cyber threat information. With STIX structured, security teams can describe a threat in various aspects such as:
Within the STIX framework, each piece of threat information is categorized under specific attributes. Thus, security teams can get better insight into ingested threat data that are linked to indicators, incidents, TTPs, specific threat actors, adversary campaigns, specific targets, data markings, courses of action, and other key aspects of an attack chain. In other words, it conveys data about cybersecurity threats in a common language that can be easily understood by humans and security technologies.
Role of Threat Intelligence Platform in Automating Threat Intel Normalization
A threat intelligence platform (TIP) automatically ingests and normalizes data from both internal and external sources to create meaningful actionable intel. Usually, a common schema or format such as STIX is followed for normalization so that security teams can contextualize and prioritize intelligence without any hassle. In other words, TIP eliminates the mundane task of sifting threat data manually, while enabling security teams to focus on other relevant tasks. An advanced TIP offers the ability to automate the normalization of both structured and unstructured threat data into STIX format for easier threat data interoperability. Modern threat intelligence platforms can collect threat data from disparate sources and support a wide range of formats to normalize all the threat information in a common and standard language. The data is collected through Open API integrations, STIX integrations, or via security orchestration, automation and response (SOAR) orchestrations. Nowadays, many SOAR platforms provide vendor-agnostic low-code and no-code security automations enabling easy and smooth integration for threat intelligence platforms with internally deployed security technologies such as SIEM platforms, firewalls, EDR platforms, incident response platforms, and others. With automation, threat intelligence platforms can interact with large volumes of multi-source and multi-format ingested threat intel and normalize it to deliver the exact information that is needed to perform advanced threat analysis and investigations, and prioritize threat response. The format-agnostic threat intelligence ingestion capabilities of TIPs enable the conversion of structured and unstructured information to various formats, such as STIX 1.x/2.0, XML, MAEC, YARA, MISP, CSV, PDF, JSON, OpenIOC, Email, and CybOX. With this, security teams across different organizations can establish and streamline their analysis, enrichment, correlation, and dissemination/sharing activities that are part of the threat intelligence lifecycle. This feature also offers the capability to sieve out unnecessary information and compare it with curated information, identifying correlations and connecting the dots to determine hidden threat patterns.
Benefits of Threat Intelligence Normalization
Simplifies the review process
The standard simplifies the review process of structured and unstructured information regarding cyber threat activity from a variety of sources and different formats. This not only improves the view of threats that your organization may face but also allows security teams to focus on relevant tasks needed to mitigate a cyber threat.
Improves threat analysis and investigation
The standard provides a way to perform advanced analysis on data that is collected from multiple sources. By normalization of threat data into a common structure is becomes convenient for security teams to perform advanced analysis on the entire data at one go while identifying hidden threat patterns. Such analysis is usually done by threat intelligence paltforms that leverage machine learning algorithms to connect the dots between reported threats and incidents with enriched threat intelligence to derive contextual threat intelligence and help prioritize and triage threats .
Facilitates threat intelligence sharing
The ability to seamlessly share real-time threat intelligence is a pivotal step in elevating any security program. By structuring the raw data in a machine-readable format, STIX’s framework enables sharing of appropriate cyber threat indicators and other cyber threat information that can be used among security teams to gain appropriate levels of consistency, context, and control about a cyber threat.
Removes duplicate data
Normalization weeds out duplicate data and makes sure that each is in a standard format. This way, when correlated, the information can be easily compared with everything else. Even if the data is from different sources (systems with varying configurations, solutions from different vendors, etc.), normalization ensures that interrelationships can still be formed.
Assists in effective correlation
Normalization enables the correlation of threat data by enabling security analysts to identify links or related Indicators of Compromise (IoCs) between pieces of information. With a better visual picture, analysts can focus on the threats that matter to them the most and perform triage, incident response, and incident remediation quickly.
To generate meaningful intel, a cyber threat intelligence program must be capable of converting threat data from various sources and in various formats. It should remove all redundant noise and make data actionable for better decision-making beyond the disparate unrelated data points. With the right kind of threat information in their hands, cybersecurity teams can stay ahead of the evolving attack landscape and ultimately bolster the security posture of their organizations. To learn more about normalization and other phases of the threat intel lifecycle, book a free demo.