Cybersecurity has long been an essential element of organizational defense, with the growing complexity and frequency of cyberattacks propelling the development of cybersecurity practices. Among these practices, Threat Intelligence (TI) has become a central element, helping organizations anticipate, understand, and counter various cyber threats. As we approach 2025, however, a new evolution in threat intelligence is emerging: Predictive Threat Intelligence (PTI).

While traditional Threat Intelligence (TI) focuses on collecting, analyzing, and sharing data on cyber threats after they occur, Predictive Threat Intelligence goes a step further. It uses advanced techniques, particularly AI (artificial intelligence) and machine learning (ML), to predict cyber threats before they materialize. This field holds great promise for proactively strengthening an organization’s cybersecurity posture by providing early warnings, reducing damage from potential attacks, and enabling defense strategies based on anticipatory insights.

What Is Cyber Threat Intelligence (CTI), and how is it Different from Predictive Threat Intelligence (PTI)?

Cyber Threat Intelligence (CTI) is the practice of collecting, analyzing, and sharing data about cyber threats. By gaining insights into threat actors’ behavior and tactics, techniques, and procedures (TTPs), organizations can better understand potential cyber threats, allowing them to prepare, respond, and mitigate potential attacks.

Traditional Threat Intelligence tends to focus on reactive measures, where security teams analyze attack patterns after a breach or threat occurs. In contrast, Predictive Threat Intelligence (PTI) takes a more proactive stance. By leveraging AI and ML, PTI not only understands current cyber threats but also forecasts future attacks before they materialize.

Machine learning algorithms analyze large datasets, including historical threat data and emerging patterns, to predict the types of threats organizations might face in the near future. For example, if an AI model detects a surge in phishing attacks against a particular industry, it can alert organizations in that sector to prepare for a potential escalation in attacks. This predictive capability allows organizations to take precautionary measures before a threat becomes imminent.

Predictive Threat Intelligence enhances the traditional threat intelligence model by offering actionable, anticipatory insights that enable proactive security measures, such as patching vulnerabilities or reinforcing defenses against specific attack vectors before they are widely exploited. This shift from reactive to proactive cybersecurity is positioned to transform the way organizations approach risk management and threat mitigation.

Why Is Cyber Threat Intelligence (CTI) Important?

Understanding the importance of Cyber Threat Intelligence (CTI) is important to appreciating its role in the cybersecurity ecosystem. As cyberattacks become increasingly damaging, the need for effective threat intelligence grows. Without comprehensive CTI, organizations would be left scrambling to respond to attacks, often too late to prevent significant damage.

CTI provides essential insights into cyber threats, including information about threat actors, their motives, and the vulnerabilities they exploit. With this knowledge, organizations can develop more rugged defense mechanisms and avoid becoming targets for specific types of attacks.

The most compelling reason for investing in CTI is its ability to elevate organizational security beyond reactive measures. By enabling organizations to recognize online threats early, CTI empowers security teams to adopt a proactive security posture. Proactive defense strategies allow vulnerabilities to be patched before they can be exploited and preparations to be made for impending threats, all of which contribute to reducing the overall risk of a breach.

How Does Predictive Threat Intelligence Work?

Predictive Threat Intelligence works by combining AI, machine learning, and advanced analytics to analyze vast amounts of historical and real-time threat data. By understanding the TTPs of cyber adversaries, these tools can identify patterns that signal emerging threats. Here’s how it works in practice:

1. Data Collection: Predictive threat intelligence platforms collect data from diverse sources, including the surface web, deep web, and dark web, as well as intelligence from private threat-sharing organizations and public cybersecurity resources. These datasets provide crucial insights into potential vulnerabilities and attack vectors.
2. Data Processing and Analysis: AI models and machine learning algorithms process the collected data, identifying potential threats based on historical attack patterns and emerging trends. For instance, if a surge in phishing attacks targeting a specific industry is detected, AI models can recognize similar characteristics or tactics that might indicate future attacks.
3. Threat Forecasting: Predictive intelligence platforms then forecast potential threats based on identified trends. For example, AI can predict that a new form of ransomware is gaining traction among cybercriminals, alerting organizations to prepare for a possible attack.
4. Proactive Response: Once potential threats are identified, the predictive system provides actionable intelligence to help organizations bolster their defenses. These could include patching known vulnerabilities, updating defense strategies, and alerting stakeholders to prepare for specific attack scenarios.

The Role of Artificial Intelligence and Machine Learning in Predictive Threat Intelligence

While Predictive Threat Intelligence (PTI) involves more than just AI, artificial intelligence and machine learning play a crucial role in its development. AI’s strength lies in its ability to analyze massive volumes of data, recognize patterns, and make predictions about future events, including cyberattacks.

However, despite the potential, AI and ML alone are not enough to guarantee a fully predictive threat intelligence model. Predictive intelligence is complex, and building reliable, actionable insights requires a balanced integration of human intelligence and automated systems.

The role of AI and machine learning in predictive intelligence includes:

• Threat Detection: AI can identify anomalous behavior in network traffic, suggesting potential attack attempts.
• Risk Analysis: By analyzing attack vectors and patterns, AI models can prioritize potential risks based on the severity of the threats and their likelihood of occurring.
• Automation: Machine learning models can automate certain security functions, such as scanning for vulnerabilities and patching security gaps, without the need for human intervention.

The Challenge of Implementing Predictive Threat Intelligence

While predictive threat intelligence is a highly promising approach, it faces several challenges, especially in terms of implementation.

1. Data Availability: One of the primary hurdles is the availability of quality data. AI and machine learning models require large, diverse datasets to learn and predict threats accurately. However, data is often fragmented and may not be available in a standardized format, making it difficult for predictive systems to integrate and analyze it effectively.
2. Complexity of Predictive Models: Predicting future threats is an inherently complex task. As with any prediction, there is a degree of uncertainty, and not every forecast will be accurate. The dynamic nature of cybersecurity means that there will always be a level of unpredictability when it comes to forecasting attacks.
3. Human Expertise: Although AI and machine learning are powerful tools, human expertise is still necessary to interpret the data and provide context. Human analysts play a critical role in identifying nuanced threats and validating AI predictions to ensure the intelligence is actionable.
4. Data Privacy and Sharing: Threat intelligence requires data from multiple sources, including potentially sensitive or confidential data. Therefore, sharing threat intelligence can raise privacy concerns, especially in industries like finance or healthcare. Developing systems that allow for safe and ethical sharing of threat data is essential for the success of PTI.

The Future of Predictive Threat Intelligence in 2025

As we look toward 2025, the role of Predictive Threat Intelligence (PTI) in cybersecurity will become increasingly important. By predicting threats before they materialize, PTI will enable organizations to stay one step ahead of cybercriminals, minimizing the risks of cyber threats.

In the near future, advancements in AI-powered threat intelligence will allow organizations to:

• Improve the automation of cybersecurity workflows, enabling faster, more accurate threat detection and mitigation.
• Enhance the integration of AI and human expertise, creating a more effective hybrid threat intelligence model.
• Develop better predictive models that consider a wider array of threat actors and attack vectors, leading to more accurate forecasts.
• Better share threat intelligence across industries, increasing collaboration and improving overall cybersecurity resilience.

Cyble, an industry leader in Cyber Threat Intelligence, has been at the forefront of this evolution. Cyble’s Cyber Threat Intelligence Platform provides real-time insights into potential threats, combining historical threat data with AI-driven analysis to deliver actionable, predictive intelligence. By integrating diverse data sources, Cyble enables organizations to identify potential threats, prioritize risks, and take proactive measures to mitigate potential breaches.

Why Choose Cyble?

Cyble offers a comprehensive cyber threat intelligence solution that empowers organizations to tackle cyber threats more effectively. With features like dark web monitoring, vulnerability management, and AI-driven analysis, Cyble helps companies not only detect threats but also predict and prevent them before they cause damage.

Cyble’s platform integrates seamlessly with your existing security infrastructure, enabling you to:

• Gather intelligence from various sources, including the deep and dark web, to identify emerging threats.
• Augment data with contextual insights for better decision-making.
• Receive timely notifications about potential threats and vulnerabilities, enabling proactive defense strategies.

Cyble is ready to help businesses understand and walk through this dynamic landscape and stay protected against cyber threats in 2025 and beyond.

Conclusion: Stay Ahead with Cyble

Predictive Threat Intelligence is the future of threat Intelligence. By leveraging advanced technologies like AI and machine learning, organizations can anticipate threats before they emerge, minimizing the damage caused by cyberattacks. As we move towards 2025, Predictive Threat Intelligence will be an essential tool in every cybersecurity strategy.

If you want to strengthen your organization’s defenses and stay protected from upcoming threats, Cyble’s threat intelligence platform is your go-to solution. Schedule a demo today and discover how Cyble can help you proactively secure your assets against the threats of tomorrow.

The post Predictive Threat Intelligence – Predictions for 2025: The Future of CTI appeared first on Cyble.

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The cybersecurity research team of ANY.RUN found and analyzed a bunch of emerging threats with the help of our mighty Interactive Sandbox and Threat Intelligence Lookup.

We’ve been sharing their findings via X and in our blog. Here is a summary on the most interesting insights from December 2024.

Phishing Campaigns targeting Microsoft’s Azure Blob Storage

Original post on X

Cyber criminals are abusing Microsoft’s cloud-based file storage solution by hosting phishing pages on the service, employing techniques like HTML smuggling.

The phishing pages are HTML documents that contain a block input element with the ID attribute “doom”. The pages include information about users’ software obtained via JScript (OS and browser), to make them more convincing.

Phishing pages on Azure Blob Storage typically have a short lifespan. Attackers may host pages with redirects to phishing sites. With minimal suspicious content, these pages can evade detection slightly longer.

See the analysis session in the ANY.RUN sandbox.

• Threat actors leverage the *.blob.core.windows[.]net subdomain to store documents.
• Company logos are extracted using email address parsing and loaded from the logo[.]clearbit[.]com service.
• To collect and store stolen data, an HTTP POST request is sent to nocodeform[.]io for collecting form submissions.

Use the following Threat Intelligence Lookup query to find threats targeting the set of requested domains:

And this search request to find links to HTML pages hosted on Azure Blob Storage.

Microsoft’s OneDrive also fell victim to HTML Blob Smuggling Campaign

The original post on X

As in the attack above, threat actors make victims believe they are logging into a legitimate platform.

Using ANYRUN’s MITM feature, we extracted base.js from the traffic and decoded it. The attack begins with a bait placed on OneDrive. After clicking the link, the user is redirected to the main page containing the HTML Blob Smuggling code. After entering their credentials, victims are redirected to a legitimate website.

Stolen credentials are sent via an HTTP POST request to the C2 server.

The website’s design, background, and icons are stored on IPFS, while lure images, mimicking real services, are hosted on imgur .com.

View the attack unfold in the wild: one, other, or yet another sandbox session.

Phishing links in Microsoft Dynamics 365 web forms

Original post on X

And again, a Microsoft service utilized for malicious activity. Phishers create forms with embedded links on *.microsoft.com subdomains. The links that users receive look legitimate, so people feel safe opening them.

With TI Lookup, we uncovered a link that tricked users into attempting to access a non-existent PDF file hosted on a Microsoft website.

Phishing URL: hxxps://customervoice.microsoft[.]com/Pages/ResponsePage.aspx?id=N_pyUL0QJkeR_KiXHZsVlyTB1Qoy7S9IkE8Ogzl8coFUNVIzNlI5MEhCNlBPRFMwMklUV0JZVTkxVS4u

Use this simple query for TI Lookup to find attacks employing this technique and view them unveiled in our sandbox.

Anatomy of a fresh LogoKit

Original post on X

LogoKit is a comprehensive set of phishing tools known for using services that offer logos and screenshots of target websites. Our team has researched the algorithm of such an attack.

Let’s look at the example run in our sandbox.

• The company’s logo is fetched from a legitimate logo storage service: hxxps://logo.clearbit[.]com/<Domain>.
• The background is retrieved via request to a website screenshot service, using the following template: hxxps://thum[.]io/get/width/<DPI>/https://<Domain>.

• The domain chain is led by a decoder-redirector: hxxps:// asiangrocers [.]store/fri/?haooauvpco=bWlubmllQGRpc25leS5jb20. It is a fake Asian food store website built on a #WordPress template, with a domain age of around four years. The template contains email addresses filled with typos.

The decoder-redirector shields the page from analysis and redirects the victim to the actual phishing page.

In our example, the real content of the phishing page and the associated scripts are hosted on the Cloudflare Pages platform. They are stored in the assets/ folder, which contains styles, images, and scripts

Three scripts with random 10-character names are designed to protect the page from analysis and send stolen data to the threat actors:

• assets/js/e0nt7h8uiw[.]js
• assets/js/vddq2ozyod[.]js
• assets/js/j3046eqymn[.]js

The stolen authentication data is sent to a remote Command and Control server controlled by the attackers via an HTTP POST request containing the following parameters: fox=&con=

Manufacturers, beware: an attack combining Lumma and Amadey is targeting you

The cybercriminals’ tactics of attacking the manufacturing industry are recently evolving from data encryption to snatching control over critical infrastructure and stealing sensitive information.

The consequences of such attacks can be severe, leading to theft of intellectual property, disruption of operations, financial losses, and compliance violations. Businesses need to take the threat most seriously, understand it and get prepared.

This December, we have analyzed a new attack aimed at industrial market players. The mechanics are based on Lumma Stealer and Amadey Bot. The former hunts for valuable information, the latter takes control over the infected systems. View analysis.

• It all starts with phishing emails with URLs leading users to download LNK files disguised as PDFs;
• The malicious LNK file, once activated, initiates PowerShell via an ssh.exe command. Following a chain of scripts, a CPL file is downloaded;
• PowerShell and Windows Management Instrumentation (WMI) commands are utilized to collect detailed information about the victim’s system.

For the details, read our blog post, view analysis session in our sandbox and dive deeper with TI Lookup. Use the search query with the name of the threat and the path to one of the malicious files used in the attack.

About ANY.RUN  

ANY.RUN helps more than 500,000 cybersecurity professionals worldwide. Our interactive sandbox simplifies malware analysis of threats that target both Windows and Linux systems. Our threat intelligence products, TI Lookup, YARA Search and Feeds, help you find IOCs or files to learn more about the threats and respond to incidents faster.  

With ANY.RUN you can: 

• Detect malware in seconds
• Interact with samples in real time
• Save time and money on sandbox setup and maintenance
• Record and study all aspects of malware behavior
• Collaborate with your team 
• Scale as you need

Get 14-day free trial of ANY.RUN’s Interactive Sandbox →

The post 5 Major Cyber Attacks in December 2024 appeared first on ANY.RUN’s Cybersecurity Blog.

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