What is Cyber Threat Intelligence?

Data that is gathered, processed, and examined to understand the goals, objectives, and attack patterns of a hacker is known as “Threat Intelligence”. It helps us to move our habits from reactive to proactive behavior in the battle against cyber threats and makes security decisions faster, informed, and supported by facts.

What is Threat Intelligence?

Threat Intelligence, commonly referred to as “Threat Intel” or “Cyber Threat Intelligence” (CTI), is extensive & useful threat data that people can use to stop & neutralize cyberattacks against their companies.

Cyber Security Teams can become more proactive by using Threat Intelligence to take quick & data-driven action to stop cyberattacks before they even happen. Moreover, it helps boost an organization's ability to identify & stop active attacks.

Threat Intelligence is created by security analysts by compiling unfiltered threat & security-related data from various sources, evaluating & correlating the information to find patterns, trends, and connections. As a result, they can offer a comprehensive picture of the risks that are real or could arise. The intelligence results are as follows:

1. Organization-Specific: It focuses on specific vulnerabilities in the attack surface of the business, the attacks they enable, and the assets they expose rather than on generalities.
2. Detailed & Contextual: It includes threats that are directed at the company & hackers that could carry them out, the Tactics, Techniques, and Procedures (TTPs) that these hackers might employ, and Indicators of Compromise (IoCs) that could indicate a specific cyberattack.
3. Actionable: Information Security Teams can use it to address vulnerabilities, rank risks, take corrective action, and even assess new or current cybersecurity products.

The average cost of a data breach to its victims is $4.35 million, according to IBM's Cost of a Data Breach 2022 research. The largest cost percentage ($1.44 million) is credited to the attacks’ detection & escalation expenses. Threat Intelligence can lower detection costs & minimize the effect of successful breaches by giving security teams the knowledge they need to identify attacks sooner.

Types of Threat Intelligence

Threat Intelligence comes in various forms, ranging from high-level, non-technical data to technical specifics of individual attacks. Here are some categories of Threat Intelligence you should be aware of:

1. Strategic Threat Intelligence: This high-level information provides conditions for the threat. It’s non-technical data that a company could provide to the board of directors. The risk analysis of how a business action might expose the company assets to cyberattacks is an example of Strategic Threat Intelligence.
2. Tactical Threat Intelligence: It includes the details of how threats are being executed & defended against, including attack vectors, tools, and infrastructures attackers are using, types of businesses or technologies that are targeted, and avoidance strategies. It also helps an organization understand how likely they are to be a target for different types of attacks.
3. Operational Threat Intelligence: An IT department can use this information to take precise action against an attack as part of Active Threat Management. It is details regarding the attacking strategy, attack kind, and attack timing. Also, it is difficult to collect this information, as it's best obtained directly from the attackers.
4. Technical Threat Intelligence: It collects specific proof that an attack is taking place, often known as Indications of Compromise (IOCs). Artificial Intelligence (AI) is used by some Threat Intelligence technologies to search for these signs, which might include C2 infrastructures’ IP addresses or email content from phishing campaigns.

The Threat Intelligence Lifecycle

The process by which security teams create, distribute, and enhance their Threat Intelligence is what we know as the Threat Intelligence Lifecycle. Although the specifics may differ from company to company, the majority of them adhere to this 6-step procedure.

Step 1: Planning

To define the necessary intelligence, security analysts join organizational stakeholders such as department heads, IT and security team members, and executive leaders who are involved in the cybersecurity decision-making process. Typically, these comprise cybersecurity-related queries that stakeholders require or desire responses for.

Step 2: Threat Data Collection

In this, the cybersecurity team gathers any unprocessed threat information that may contain or add to the answers of stakeholders. In keeping with the previous scenario, if a security team is looking into a new ransomware attack, they may collect data on the ransomware gang responsible for it, the kinds of companies they've previously attacked, and their attack routes. This dangerous information may originate from several places, such as insider threats, viruses, worms, botnets, drive-by download attacks, etc.

1. Threat Intelligence Feeds — It provides real-time threat data streams. Sometimes the name is deceptive: Some feeds contain raw threat data, while others contain processed or evaluated Threat Intelligence. Usually, security teams follow several commercial and open-source feeds. Each of these streams can help us comprehend risks on a deeper level.
2. Information-Sharing Communities — forums, trade associations, and other groups where experts from around the globe exchange personal accounts, observations, and Threat Intelligence. Through the National Council of ISACs (NSI), several industry-specific Information Sharing and Analysis Centers (ISACs) across the US's critical infrastructure sectors collaborate.
3. Internal Security Logs — Internal security information from systems for compliance and security, including the ones listed below:

1. SIEM (Security Information and Event Management)
2. SOAR (Security Orchestration, Automation and Response)
3. EDR (Endpoint Detection & Response)
4. XDR (Extended Detection & Response)
5. Attack Surface Management (ASM) Systems

This data gives the company a history of the cyberattacks it has encountered and can be used to identify evidence of internal or external risks that were previously missed. Sometimes, data from these various sources is combined into a single dashboard for easier management.

Step 3: Processing

To facilitate analysis, security analysts now compile, normalize, and correlate the raw data they have collected. This could entail removing false positives or analyzing data related to a prior security event using a Threat Intelligence framework like MITRE ATT&CK. By using Artificial Intelligence (AI) and Machine Learning to correlate threat information from many sources and spot early patterns in the data, many Threat Intelligence tools help automate this process.

Step 4: Analysis

When threat data is analyzed, it transforms from raw data to actual threat information. At this point, security analysts test & validate the trends, patterns, and other insights they may utilize to provide suggestions and respond to security requirements from stakeholders.

Step 5. Dissemination

The relevant stakeholders receive insights and recommendations from the security team. Based on these suggestions, steps might be taken, such creating new SIEM detection rules to target recently discovered IoCs or upgrading firewall blacklists to stop traffic coming from recently discovered suspect IP addresses.

Step 6. Feedback

Stakeholders & data analysts evaluate whether the requirements were fulfilled at this point by considering the latest Threat Intelligence cycle. The following cycle of the lifecycle will consider any new queries that crop up or intelligence gaps that are discovered.

Key Components for Actionable Threat Intelligence

Here are some key components you should consider while using an Actionable Threat Intelligence:

Threat History Data

An abundance of threat history data is necessary for Actionable Threat Intelligence. Machine learning & cyber threat analysis capabilities give insightful data. With larger data sets, both get better. But it is only possible for the cyber Threat Intelligence to proactively block 10 threats if it only possesses 10 threat datasets. The Threat Intelligence will get more knowledgeable about harmful attacks that could harm your network as the data collection grows. Furthermore, as more data is collected, ML-based analytical algorithms will improve.

Automated Detection/Blocking

Extensive threat history data, machine learning powers, and accurate cyber threat analysis are great. But the Cyber Threat Intelligence system must use these tools to automate actions. To permanently block attacks, it must not only respond to pre-discovered threats but also take proactive measures for future attacks.

For the foreseeable future, there will probably be an exponential increase in cyber threats. Manual work just cannot keep up with the pace. Due to this, companies need to implement a single & integrated threat management system that can recognize a threat in Asia and immediately stop it in South America.

Cyber Threat Analysis

A well-designed cyber threat analysis is a prerequisite for cyber Threat Intelligence. Since more data is being handled by businesses than ever before, hackers have more financial motivation than before, which is why they are developing greater sophistication and coordination. This creates additional difficulties that call for more creative approaches to cyber threat assessments.

Machine Learning Capabilities

The volume of threats is increasing, and prevalent dangers are evolving quickly. These are two of the most alarming developments in threat defense. Cyber Threat Intelligence must use machine learning in threat scenarios to stay up to date with current trends.

Massive data sets can be analyzed at machine speed by machine learning, which can identify trends and anticipate risks. This can be used by the security operations teams to quickly identify and rank advanced threats that need careful human investigation. To enhance their machine learning capabilities, companies had to consider the subsequent prerequisites:

1. Dataset Diversification & Precision. For thorough coverage, it is imperative to show malware that is encountered by companies of diverse sizes, industries, and regions, and that is transmitted through a variety of attack routes.
2. Multilayered Processing. To make sure that security teams are dealing with prioritized and context-rich detections, every processing step in a machine learning pipeline should increase the fidelity and accuracy of detections.
3. Correlation of Endpoint & Network Data. The system should be able to identify more threats more quickly, increase precision and self-learning capabilities, and strengthen detections by linking the outcomes of multilayered processing.
4. In-Depth Domain Expertise & Continuously Trained Classifiers. When developing a strong machine-learning system that is difficult to manipulate, domain expertise and ongoing learning are essential components of the puzzle.

Who Benefits from Threat Intelligence?

Organizations of all sizes gain from Threat Intelligence by processing threat data to comprehend attackers more fully, react to incidents more quickly, and anticipate the next move of hackers. This data gives SMBs access to a level of protection that would not otherwise be possible. On the other hand, by utilizing external Threat Intelligence and improving the efficiency of their analysts, businesses with sizable security teams can lower the expense and necessary expertise. Every member of a security team can benefit from Threat Intelligence differently, from top to bottom, including:

1. Sec/IT Analyst
2. SOC
3. CSIRT
4. Intel Analyst
5. Executive Management

The following are the advantages for each role and the particular use cases that relate to each:

Function

Benefits

Sec/IT Analyst

Optimize prevention and detection capabilities and strengthen defenses

SOC

Prioritize incidents based on risk and impact to the organization

CSIRT

Accelerate incident investigations, management, and prioritization

Intel Analyst

Uncover and track hackers targeting the organization

Executive Management

Understand the risks the organization faces and what the options are to address their impact

Common Indicators of Compromise

If security officers check in the correct places for aberrant activity, they can often find signs that an attack has occurred or is imminent. With this endeavor, artificial intelligence can be of great assistance. Among the common IOCs are:

1. Unusual Privileged User Account Activity: Attackers frequently attempt to increase their account privileges or switch from a compromised account to one with more authority.
2. Login Anomalies: Unauthorized file access attempts made after hours, many unsuccessful login attempts from various IP addresses across the globe to the same account, and unsuccessful login attempts using fictitious user accounts are all signs that something is wrong.
3. Increases in Database Read Volume: A significant rise in database read volume may be a sign that an abnormally large amount of data is being extracted, like all of the credit card numbers stored in a database.
4. Unusual Domain Name System (DNS) requests: Red signals include large increases in DNS requests from a particular address and patterns of DNS requests to external hosts, as these could indicate that command and control traffic is being sent by someone outside the company.
5. Large Numbers of Requests for the Same File: Repeated attacks are a common component of cybercrime, and they may be a sign that someone is looking for a weakness. 500 requests for the same file can mean that multiple people are looking for vulnerabilities.
6. Unexplained Configuration or System File Changes: Finding a credit card harvesting tool is hard, but finding modifications to system files caused by the tool's installation is simpler.

Available Threat Intelligence Tools

Through the open-source community, a range of Threat Intelligence products are either free to use or accessible for purchase. Their methods for obtaining Threat Intelligence vary slightly among them:

1. Malware Disassemblers: These technologies allow security engineers to decide how to fight against similar attacks in the future by dissecting malware and learning how it operates.
2. Security Information & Event Management (SIEM) Tools: Security personnel may keep an eye on the network in real time with SIEM technologies, collecting data on anomalous activity and questionable traffic.
3. Network Traffic Analysis Tools: Tools for network traffic analysis gather data about the network and log network activity to produce insights that facilitate the identification of intrusions.
4. Threat Intelligence Communities & Resource Collections: Publicly available Websites that compile information about threats and known compromise indicators might be a great way to obtain Threat Intelligence. A portion of these groups encourage cooperative research and offer practical guidance on mitigating or preventing risks.

Organizations can stop an attack before it starts if they are aware of new dangers and know how to avoid them. All organizations should incorporate the collection and evaluation of Threat Intelligence into their enterprise security plan.