Cyber threat intelligence is defined as cyber threat data classified based on reliability which is rigorously analyzed by cybersecurity experts using structured tradecraft techniques and secondary data gathered from trustworthy sources. This article explains cyber threat intelligence, its main objectives, architectural components, key challenges, and the best practices for cyber intelligence threat management in 2022.
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What Is Cyber Threat Intelligence?
Cyber threat intelligence is cyber threat data classified based on reliability which is rigorously analyzed by cybersecurity experts using structured tradecraft techniques and secondary data gathered from trustworthy sources.
Cybersecurity teams leverage cyber threat intelligence to mitigate the risk of cyberattacks by studying threat data and the various methods used by bad actors. This enables enterprises to identify the likelihood of an attack and prepare to counter the expected attack vectors. Simply put, cyber threat intelligence helps prevent cyberattacks through the analysis of data on attackers, their motives, and their capabilities.
Types of Cyber Threat Intelligence
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Cyber threat intelligence works by helping users generate useful data about existing as well as emerging threats to their organization. This allows for faster and more informed decision-making related to cybersecurity, shifting a company’s security posture from a reactive to a proactive approach. A report by Grand View Research, Inc. pegs the global threat intelligence market at $12.6 billion by the year 2025.
The post-pandemic corporate world has seen enterprises invest generously in cybersecurity solutions. However, many organizations remain susceptible to cyberattacks for several reasons, including a remote work environment and the continued use of traditional security systems. These gaps can be plugged with cutting-edge measures such as cyber threat intelligence and predictive analysis.
Cyber threat intelligence bolsters enterprise security at all levels, including cloud and network security. These solutions prepare security teams to deal with vulnerabilities by giving a clear picture of the threat indicators, methods used to execute threats, and the techniques that can be used to counter cyberattacks swiftly and effectively. Preventing attacks and limiting the damage caused by successful attacks are two key outcomes of cyber threat intelligence.
Users can rely on cyber threat intelligence systems to track cybersecurity activity, share threat information swiftly, and analyze threats effectively. These solutions can also compare data from the feed with internal metrics and alert security personnel in case of a likely threat. The ultimate aim of cyber threat intelligence is to minimize the potential loss of money and goodwill due to cybersecurity incidents.
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5 Key Objectives of a Cyber Threat Intelligence System
Cyber threat intelligence systems boast diverse use cases, making them a popular solution for organizations across industry verticals. Below are the five key objectives of leveraging a cyber threat intelligence system.
Key Objectives of a Cyber Threat Intelligence System
1. Effective incident response
The post-pandemic corporate world has seen many cybersecurity professionals deal with increasing incidents of cyberattacks. This, combined with mounting cases of false positives, can negatively affect the quality of incident response. The fact that the security personnel spends a lot of time and effort sorting threat data manually does not make matters easier.
Cutting-edge cyber intelligence systems automate various threat detection processes, potentially giving security teams up to a few days to respond to incidents in situations where every second counts. A cyber threat intelligence system makes incident response more effective by automatically detecting and discarding false positives. Further, threat alerts are infused with real-time contexts, such as a custom risk score, to make them more effective. Finally, these solutions allow users to compare data from internal and external sources to ensure a comprehensive response to cybersecurity incidents.
2. Robust vulnerability management
Traditional vulnerability management measures focus on detecting and patching weaknesses in IT systems, often without prioritizing them based on actual risk. This makes the process a continuous, often endless fight against new vulnerabilities.
However, not all vulnerabilities are targeted by threat actors. In fact, attackers are often more likely to target the same vulnerabilities that have been targeted in the past. This is because these exposures are easier to find and execute. Further, attackers are more likely to target a newly discovered vulnerability only when it is new since they expect security teams to move fast and patch them within the first few weeks. These insights give cybersecurity personnel a more effective method to prioritize patch management.
Cyber threat intelligence systems automate this process and make identifying critical vulnerabilities easy. This is achieved by combining traditional vulnerability management techniques, such as CVE scoring, with internal and external data and additional information about the expected tactics of threat actors.
3. Efficient security operations
Security teams deal with several alerts daily. Classifying these network-generated alerts is a time-intensive task, leading to many potential threats never being investigated. Naturally, this ‘alert fatigue’ can have grave consequences for an organization’s security posture.
Cyber threat intelligence systems address this concern by making threat data collection swift and accurate. These solutions also triage threats quickly, filtering out false alarms and making incident analysis straightforward. Non-malicious threats, cyberattacks that are not likely to affect the organization, and threats that are already well-defended against are also classified as such to help security personnel focus on important alerts. Finally, these systems make the analysis and containment of security incidents easier for security teams.
4. Powerful fraud prevention
Fraudulent activity is a considerable threat to organizational integrity, especially in the post-pandemic corporate world where remote work is still popular, and many colleagues have never met each other in person.
Cyber intelligence systems leverage threat data to notify cybersecurity teams of the methods and motivations of bad actors. This information is augmented with data from reliable secondary sources, further enhancing its dependability. Preventing cyber fraud helps enterprises avoid legal penalties and loss of consumer trust.
Cyber threat intelligence systems help prevent fraudulent activity in many ways, including:
- Detecting credential leaks: Compromised corporate data is often uploaded in bulk on the dark web. Threat intelligence solutions can help detect instances of leaked credentials, proprietary code, or other sensitive information. This helps take suitable countermeasures to prevent fraudulent misuse and future leaks.
- Countering financial fraud: Cyber intelligence solutions can be used to monitor underground communities for leaked card details, bank details, or other indicators of upcoming payment fraud.
- Preventing phishing: Threat intelligence solutions alert security teams of freshly registered typosquatting and phishing domains in real-time. This helps counter impersonation attacks that aim to defraud employees or clients.
5. Effectual risk analysis
Today, many enterprises rely on risk modeling to make important policy decisions. However, risk models are often prone to generating outputs that are unactionable, non-quantified, based on incomplete data, compiled in a hurry, or the result of unfounded assumptions.
Cyber threat intelligence systems help enhance risk analysis by providing context that boosts the transparency of the variables and outcomes. These solutions identify likely threat actors, the targeted industries and vulnerabilities, frequency of attacks, past attack outcomes, and progression of specific trends.
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Key Architectural Components of Cyber Intelligence
The specific architecture of a cyber threat intelligence system enables it to mitigate the risk of threats effectively. Its architectural components are as follows.
Architectural Components of Cyber Intelligence
1. Data aggregation component
The ‘threat data aggregation’ component is an important architectural element in any cyber threat intelligence system. The first step of the cyber intelligence process is the collection of threat data. Cyber intelligence solutions put together numerous threat intelligence feeds to enable consistent categorization and characterization of cyber threat events. Such a ‘mega feed’ helps cybersecurity teams identify trends and fluctuations in the activities of malicious actors. More threat history data means more actionable intelligence for cybersecurity teams. Cyber threat analysis is more effective with larger datasets, especially if augmented with machine learning.
2. Threat analysis component
Once the threat database is ready, cyber intelligence solutions use the threat analysis component to parse this data, classify it based on risk, and combine it with meaningful context to generate useful insights into the threats faced by the organization. With industries expected to face increasing cybersecurity threats in 2022 and beyond, dealing with sophisticated and well-coordinated attack attempts is critical. Threat analysis enables security teams to stay forewarned and prepare countermeasures for specific, likely cyberattacks.
3. Machine learning component
The advent of machine learning (ML) has expanded the capabilities of cyber intelligence solutions manifold. The ML component addresses two big problems in threat defense: rapid evolution of common cyber threats and ever-increasing incidence of these threats. The near-real-time pattern recognition and threat prediction capabilities of ML using large datasets help cybersecurity professionals identify and prioritize cyber threats rapidly and act on the ones that require human intervention.
4. Automation component
Finally, the automation component brings the other three architectural components together. This allows cyber intelligence solutions to analyze cyber threats by passing large threat datasets through the machine learning component. Automation enables the system to proactively detect and block cyber threats and notify security teams when their intervention is required.
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5 Key Security Challenges to Cyber Intelligence
While cyber intelligence solutions are highly effective at countering cybersecurity threats, they come with their own set of challenges. Below are five key security challenges to cyber intelligence.
Cyber Intelligence Challenges
1. Data complexity
The COVID-19 pandemic and ensuing lockdowns have pushed organizations to establish or enhance their digital footprint. As such, the amount of enterprise data being generated is higher than ever. The collection, processing, and analysis of extremely large batches of data for cybersecurity purposes are highly complex. It might even lead to operational paralysis, preventing security teams from identifying meaningful data or patterns and thereby falling prey to preventable attacks. Using the latest cyber threat intelligence solutions can help mitigate this data complexity to a certain degree, allowing for the effective filtering of data generated by applications, systems, and networks.
2. Evolving threats
The year 2022 is expected to see rapid evolution of the cyber threat landscape. The frequency of cyberattacks is steadily increasing, and the attack vectors being used are more advanced than ever before. While enterprises are leveraging artificial intelligence (AI) and machine learning to bolster their security posture, cybercriminals are using the exact same technology to identify and exploit vulnerabilities swiftly. Therefore, not staying up to date in terms of cybersecurity can make it extremely difficult to detect and prevent cyberattacks in time.
3. Skill crunch
The demand for intelligent automation technologies is outpacing the supply of skilled AI and ML professionals in certain sectors. As a result, there is often a scarcity of the skill-sets required to correctly build and program the algorithms used to identify and mitigate malicious behavior. As technology advances and threat actors rush to keep up, it can become difficult to determine the correct data sets, data sources, and data quantities needed for adequately training ML-powered cyber threat intelligence solutions. However, there is hope. As the field of deep learning evolves, it can be relied on to fill the gaps left behind by machine learning and ensure robust threat intelligence.
4. Adversarial ML
Cybercriminals strive to detect faults in ML-augmented cyber threat intelligence models in the hope of bypassing enterprise cybersecurity measures. Adversarial ML is a malicious machine learning exploit that compromises the integrity of ML models. This technique uses obtainable model data to execute malicious attacks and potentially cause the ML model to malfunction. Adversarial ML works because ML models are normally trained to process data from similar original distributions. By feeding the model with malicious inputs, attackers can increase the misclassification rate of cyber threat systems and achieve outcomes such as data leaks.
5. Lack of transparency
Cyber intelligence solutions rely heavily on ML-augmented algorithms to ensure effective threat management. One concern that often arises when it comes to the workings of ML is the lack of transparency. The entire learning and training process of ML-based cyber intelligence systems can be a ‘black box’, i.e., nobody can quite figure out the processes or logic used to reach the output. While this does not make ML results any less reliable, it could lead to certain regulatory or operational complexities associated with risk management and incident response. However, progress is being made on the ‘explainable ML models’ front, which means this challenge may be overcome as the underlying technology continues to progress.
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Top 10 Best Practices for Cyber Intelligence Threat Management in 2022
Cyber threat intelligence solutions are expected to see an exponential rise in popularity in 2022 and beyond as businesses work toward bolstering their online security in an increasingly remote world. Listed here are the top 10 best practices for cyber intelligence threat management in 2022.
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Cyber Intelligence Threat Management Best Practices
1. Infuse intelligence into your SOC
A security operations center (SOC) is a centralized group of cybersecurity professionals that protects an organization from threats. By infusing your SOC with cyber intelligence capabilities, you are adding useful context to threat data. Leveraging threat intelligence is particularly useful for security analysts who work with extremely large datasets. ML-backed threat detection and prevention solutions prioritize alerts using security information and event management (SIEM) systems and escalate those needing analyst intervention. This allows your SOC to focus on countering likely threats instead of wasting resources on triaging threat data.
2. Enhance your IT security management capabilities
Implement cyber threat intelligence to boost your IT security management workflow. This will enable IT personnel to prioritize the adoption of the right security countermeasures at the right time. For instance, if your cyber threat intelligence solution indicates a high likelihood of your organization being targeted by ransomware, you will have ready access to information on the tactics that the group is likely to use to infiltrate your systems. Your IT security management team can then leverage this operational intelligence to mobilize its defense accordingly.
3. Ensure adequate vulnerability management
The bigger an organization’s digital footprint, the greater the number of vulnerabilities in its systems. The right cyber threat intelligence system can help you prioritize the patching of vulnerabilities according to the likelihood of them being exploited. This can help your organization strengthen its security posture and patch management processes without undertaking a vast, dedicated vulnerability management program.
4. Bolster your investigation & response capabilities
Cyber intelligence can help you establish an efficient investigation and incident response workflow. Such a system would help your security team understand the procedures used by threat actors and leverage this data to counter threats proactively. Further, intelligence augmented vulnerability management measures will give you an understanding of the intent and capabilities of bad actors. This enables the most appropriate response to a security event and allows you to minimize its impact.
5. Engage in regular resilience exercises
Cyber threat intelligence systems provide technical descriptions of likely attack vectors targeting important organizational systems. Some solutions even outline the rationale behind the predicted scenario and the potential business impact.
Use these insights to arrange for regular resilience testing activities. These exercises can be formulated using the data available on threat actors, their likely targets, and their preferred attack methodologies. Such activities can also help shape your penetration testing and business continuity policies. Finally, regular resilience exercises can help you shift from a compliance-based cybersecurity approach to a realistic approach that helps defend your enterprise against actual attacks.
6. Create a robust cybersecurity strategy
Strategic intelligence provides an understanding of changes in the behavior of threat actors. Using a cyber threat intelligence system can help security professionals spot overarching trends—such as an increase in the abuse of instant messaging channels—and allow them to develop appropriate countermeasures. Gathering and analyzing strategic intelligence can help your organization draft a robust cybersecurity policy. Policies drafted using actionable threat intelligence would also help you accurately predict your long-term cybersecurity costs.
7. Strengthen your risk assessment
Cyber threat intelligence solutions can be leveraged to strengthen your risk assessment process at the enterprise level. Begin by identifying key organizational assets. Next, use the insights gleaned from threat intelligence solutions to assess the likelihood of bad actors targeting these assets and the methodologies they might use to do so. Finally, convene with all relevant stakeholders to analyze this information, assess the level of risk faced by these key assets, and formulate an effective remediation strategy.
8. Refine your development operations
Application development operations can be refined with the help of cyber threat intelligence systems. Your developers can begin by understanding the threats faced by the current version of your application and similar applications in the market. These insights can then be used to create applications with stronger cybersecurity features that are thereby resistant to the most likely attacks.
9. Ensure seamless compliance
Cyber intelligence solutions can help enterprises ensure seamless regulatory compliance. The insights gained from threat intelligence systems can show you the gaps in the compliance status of your organization. This is especially useful for companies that need to strictly adhere to rigorously enforced policies, such as the Directive on Security of Network and Information Systems (NIS Directive) or the General Data Protection Regulation (GDPR). The ability to proactively identify and counter potential breaches can also help avoid punitive regulatory action.
10. Enhance employee awareness
Finally, cyber threat intelligence systems can help enhance cybersecurity awareness among your workforce. Remote work is expected to stay popular in the near future, which could leave employees who are not all that tech-savvy accessing sensitive information without effective cybersecurity training.
In such work environments, intelligence on the latest cybercriminal techniques targeting employees could prove to be invaluable. Preparing targeted training modules that are easy to understand for all employees is often more effective than general cybersecurity training modules that try to cover everything. Preparing your workforce for the most likely scenarios can help fill the gaps left by more traditional awareness initiatives.
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Takeaway
The effective use of cyber threat intelligence solutions allows cybersecurity professionals to create robust defense mechanisms against the latest threats. This is achieved by leveraging the predictive capabilities of these solutions to create tailor-made defenses that can preempt potential cyberattacks.
As an increasing number of enterprises digitalize more and more of their business processes, the collective attack surface of the corporate world is becoming larger than ever. This gives cybercriminals more incentive to locate and exploit possible vulnerabilities. Cyber threat intelligence systems can help protect organizations from such malicious actors across industry verticals.
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