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Technology underpins nearly every facet of our lives. Especially in the business world, as you know, the benefits of automation and interconnectedness are undeniable. But this digital revolution has also given rise to a parallel world of cyber threats. These threats, ranging from sophisticated cyberattacks to common malware infections, endanger the security of individuals and organizations. 

Threat intelligence, often referred to as open-source intelligence (OSINT), is the critical knowledge that equips us to safeguard against and respond to these threats. Rooted in data, threat intelligence in cybersecurity offers valuable context, such as identifying the source of an attack, the motivation behind it, and indicators of compromise within business IT systems. 

A Definitive Guide to Understanding Threat Intelligence

In a previous blog post, we provided a basic rundown of cyber threat intelligence. This blog goes a step further in exploring its importance and the various forms it takes, helping organizational leaders like yourself to understand it as a vital component of your cybersecurity strategy.

Why is Threat Intelligence Important for Cybersecurity?

The modern cybersecurity landscape is fraught with challenges. The adversaries are becoming increasingly persistent, devious, smart and resourceful. The attack surface has expanded, encompassing not only digital threats but also physical security and operational disruptions, among others. In this complex environment, threat data feeds often overwhelm organizations. Many struggle to process this data effectively, hampering their analysts who lack the tools to discern what requires immediate attention and what can be safely ignored.

Vast volumes of data flood into security systems, many of which are unconnected and fragmented, leading to false alarms. Moreover, there is a severe shortage of skilled professionals in the industry. 

This is where robust threat intelligence comes into play. Threat intelligence is actionable, meaning it is timely, contextual and comprehensible to decision-makers, enabling them to make informed choices about their organization’s security. The best cybersecurity solutions employ machine learning to automate data collection and processing, integrate seamlessly with existing security systems, and gather unstructured data from diverse sources. They then provide essential context regarding Indicators of Compromise (IoCs) and the tactics, techniques and procedures (TTPs) employed by cybercriminals. 

Who Can Benefit From Threat Intelligence?

The realm of threat intelligence is not limited to elite analysts. It has value across the spectrum of security functions in organizations of all styles and sizes. When threat intelligence is treated as a separate entity within your organization’s broader cybersecurity system, many individuals who could benefit from it the most are left without access at critical times.

Cybersecurity operations teams sometimes find it challenging to process the multitude of alerts they receive. But threat intelligence seamlessly integrates with existing security software and strategies, facilitating automatic prioritization and threat filtering. IT teams can pinpoint the most critical vulnerabilities by leveraging external insights and context provided by threat intelligence. 

Additionally, fraud prevention, risk analysis and other high-level cybersecurity processes are THAT MUCH STRONGER with the understanding of the current threat landscape offered by threat intelligence. Threat intelligence enriches security functions and offers key insights into threat actors, their tactics, procedures and more, sourced from across the web.

The Cyber Threat Intelligence Cycle

To understand the production of threat intelligence, one must recognize that raw data is distinct from intelligence. Threat intelligence represents the final product that emerges from a six-stage cycle, encompassing data collection, processing, and analysis. This cycle allows for continual refinement over time. 

To maximize the value of produced threat intelligence, it’s crucial to identify use cases and objectives at the outset.

1. Planning & Direction

The first step in producing actionable cyber threat intelligence is formulating the right questions. These questions should focus on specific facts, events or activities rather than broad, open-ended inquiries. 

Cyber intelligence objectives are prioritized based on factors such as alignment with an organization’s core values, the impact of the resulting decisions, and the urgency of those decisions. The audience who will receive and implement the finished intelligence is an essential consideration during this stage.

2. Collection

Once the intelligence objectives are established, the next phase involves gathering raw data that satisfies the requirements set in the planning stage. Data collection probes a range of sources, including internal sources like network event logs and records of past incident responses, as well as external sources from the open web, dark web and technical resources.

3. Processing

After collecting all the raw data, it must be sorted, organized with metadata tags, and screened to remove redundant information, false positives and negatives. With the volume of data processed daily, especially by small organizations, automation becomes essential. 

Tools like SIEMs can be beneficial for structuring data with correlation rules for a few different use cases, but collecting unstructured data from various internal and external sources necessitates a more comprehensive solution, employing machine learning and natural language processing.

4. Analysis

Processed data is then examined to identify potential security issues and notify relevant teams in a format that aligns with the established intelligence requirements. Threat intelligence can take various forms, depending on the objectives and intended audience, ranging from simple threat lists to peer-reviewed reports.

5. Dissemination

The completed intelligence is disseminated to its intended consumers. To ensure that threat intelligence remains actionable, it must reach the right people at the right time and be tracked for continuity between intelligence cycles. The use of ticketing systems that integrate with other security systems is highly beneficial in tracking each step of the intelligence cycle, enabling effective coordination among teams.

6. Feedback

The final stage of the intelligence cycle involves reviewing the intelligence product by the initial requestor to determine if their questions were addressed. This feedback drives the objectives and procedures of subsequent intelligence cycles, emphasizing documentation and continuity.

The Types of Threat Intelligence

As demonstrated by the intelligence lifecycle, the final product of threat intelligence takes various forms based on intelligence requirements, information sources and intended audience. Threat intelligence is often categorized into three subtypes:

1. Strategic Threat Intelligence

This category provides a broad overview of an organization’s threat landscape. It’s designed to inform high-level decisions made by executives and other decision-makers within an organization. 

Strategic intelligence is generally less technical and is presented in the form of reports or briefings. It offers insights into risk areas, such as the risks associated with certain courses of action, overarching patterns in threat actor tactics and targets, and geopolitical events and trends.

2. Tactical Threat Intelligence

Tactical threat intelligence outlines the tactics, techniques and procedures (TTPs) employed by threat actors. It offers specific details on how an organization might be attacked and the best methods to defend against or mitigate those attacks. 

This form of cyber intelligence is more technical and is primarily used by personnel directly involved in an organization’s defense, such as system architects, administrators and security staff.

3. Operational Threat Intelligence

Operational intelligence provides specialized insights that help incident response teams understand the nature, intent and timing of cyber attacks. It delves into technical details about specific attacks and campaigns, often referred to as technical threat intelligence. 

The stakeholders and consumers of operational threat intelligence include security leaders, SOC managers, threat hunters, cyber threat intelligence teams and incident responders.

Tactical and operational threat intelligence often overlap, as technical information is relevant to both. While technical threat intelligence typically derives from data feeds, operational threat intelligence incorporates various sources, such as the interception of threat group communications.

Machine Learning & Automation for Enhanced Threat Intelligence

Given the scale of data processing today, comprehensive automation is essential. Threat intelligence solutions combine data from numerous sources to form the most comprehensive strategy possible. Machine learning plays a crucial role in improving the usefulness of this data, bringing structure to a broad pool of unstructured data, and automating the process of searching for specific entities and events.

1. Entity & Event Recognition

Threat intelligence solutions leverage machine learning to recognize entities and events in unstructured text, such as news articles, blogs and forums. Natural language processing techniques identify names, properties and relationships, making them easier to sort into hierarchies of sets. Entities and events enable powerful searches over categories, allowing cybersecurity experts (like us) to focus on the broader picture instead of manually sifting through data.

2. Structuring Text in Multiple Languages Through Natural Language Processing

Natural language processing allows for the structuring of text from sources in multiple languages into a structured database. It can classify text into various categories and disambiguate entities with the same name, such as distinguishing between a company name and the name of a fruit.

3. Classifying Events & Entities to Prioritize Alerts

Machine learning and statistical methods are used to classify entities and events based on their importance. Risk scores are calculated through rules based on human intuition and experience, as well as machine learning trained on vetted data. This classification automates the process of determining which alerts require immediate attention.

4. Forecasting Events & Entity Properties Through Predictive Models

Machine learning generates models that predict future events and entity properties by drawing on extensive datasets. As more data sources are integrated, these predictive models become increasingly accurate, helping organizations stay ahead of evolving threats.

Threat Intelligence Use Cases

The multifaceted nature of cyber threat intelligence makes it an indispensable resource for cross-functional teams within an organization. It provides immediate value by preventing attacks, but it is also crucial in triage, risk analysis, vulnerability management and high-level decision-making.

1. Incident Response

Cybersecurity analysts tasked with incident response often grapple with high levels of stress, given the rising incidence of cyber threats. Threat intelligence simplifies their work by automatically identifying and dismissing false positives, enriching alerts with real-time context, and comparing information from internal and external sources. 

Professionals using threat intelligence can identify risks ten times faster on average, affording them more time to respond to threats effectively.

2. Security Operations

Security operations teams typically receive a deluge of alerts, many of which are never investigated due to the phenomenon of “alert fatigue.” Threat intelligence aids these teams by gathering information about threats swiftly and accurately, filtering out false alarms, expediting triage, and simplifying incident analysis. As a result, security analysts can stop wasting time pursuing alerts that are more likely to be innocuous than malicious.

3. Vulnerability Management

Effective vulnerability management involves prioritizing vulnerabilities based on actual risk, rather than adopting an impractical (and irresponsible) “patch everything, all the time” approach. Threat intelligence identifies the vulnerabilities that pose the most significant risk to an organization by combining internal vulnerability scanning data, external data, and additional context about threat actors’ TTPs. 

Organizations utilizing threat intelligence can identify 22% more genuine threats before they cause substantial damage.

4. Risk Analysis

Risk modeling is essential for organizations to set investment priorities. However, many risk models lack transparency and quantified output. Threat intelligence brings context to risk models, helping organizations make well-defined risk measurements and fostering clarity about assumptions, variables, and outcomes. Organizations that use threat intelligence see an 86% reduction in unplanned downtime.

5. Fraud Prevention

Preventing fraudulent use of data or brand impersonation is as crucial as detecting and responding to threats. Threat intelligence gleaned from underground criminal communities provides insights into the motivations, methods, and tactics of threat actors. It aids in preventing payment fraud, monitoring compromised data and identifying typosquatting domains, thereby averting potential financial and reputational damage. 

By avoiding more breaches with threat intelligence, organizations can save nearly $10 million on average per potential breach in fines, penalties and lost consumer trust.

6. Security Leadership

Chief Information Security Officers (CISOs) and other security leaders must manage risk by balancing available resources against the need to secure their organizations from evolving threats. Threat intelligence assists in risk assessment, strategy identification, and risk communication to top management and board members. 

It offers valuable insights into emerging threats, attack trends, successful security practices, and technologies, helping security leaders make more informed decisions. Threat intelligence automates labor-intensive tasks and enables junior personnel to upskill, significantly improving overall efficiency.

7. Reducing Third-Party Risk

Organizations today are increasingly reliant on third parties for various services, posing new security challenges. Traditional third-party risk management practices often lack real-time context. Threat intelligence provides transparency into the threat environments of third parties, offering real-time alerts on threats and changes to their risk profiles, thus aiding organizations in evaluating their relationships effectively.

Your Guide to Implementing Cyber Threat Intelligence in Your Business Cybersecurity

In a world where digital interconnectedness brings convenience and opportunity, it also exposes us to evolving cyber threats. Threat intelligence stands as a critical defense mechanism, empowering organizations to protect your digital assets and make informed security decisions. 

Threat intelligence bolsters your cybersecurity efforts, saving time and resources while enhancing your ability to safeguard your organization from cyber attacks and devastating data leaks. Incorporating machine learning and automation further amplifies the value of threat intelligence, making it an essential component of modern cybersecurity strategies. As the cyber threat landscape continues to evolve, organizations must adapt by leveraging the power of threat intelligence to stay one step ahead of potential adversaries.

Business cybersecurity is a multifaceted process that involves many complicated steps. There’s no shame in asking for help. Call Acme Business at (716) 372-1325 and connect with us on LinkedIn. Our experts can create a specialized system to fit your unique cybersecurity needs.