AI-Powered Threat Hunting: Unveiling Hidden Threats Through Advanced Analytics

Posted on by Isla Sibanda

The number of cybercrime attacks, and attempts, too, has risen astronomically in recent years. Hidden threats like phishing and malware lurk among the most innocent seeming of professional correspondences and online interactions. Ransomware, as always, is a major threat to almost every industry; in March 2023 alone there were almost 460 reported incidents of ransomware attacks. 


Savvy cybercriminals are adept at identifying and exploiting vulnerabilities and security weaknesses in broad systems, such as supply chain networks or international healthcare organizations. And the consequences of a successful cyberattack can be severe, causing lasting financial, reputational, and structural damage to organizations that extends well beyond simple data breaches.


To keep up with these growing threats, cybersecurity professionals have no other option but to harness the latest AI and ML innovations


Potential Risks of AI in Cybersecurity

The use of AI in cybersecurity is a double-edged sword. While it can provide huge advantages for cybersecurity professionals, criminals can also benefit from these evolving technologies. They can use AI to enact major cyberattacks, with significant consequences. 


Take, for instance, the growing trend where cybercriminals use AI and advanced analytics to mimic the websites of legitimate LLC services. The criminals employ machine learning algorithms to scrape vast amounts of data from actual LLC service websites, enabling them to understand the visual layout, user flow, and even the specific language used in customer interactions.


Advanced analytics then allow these impostors to optimize the mimicry based on user interaction data, making the fraudulent sites progressively more convincing over time. Unsuspecting entrepreneurs, lured by the credibility of these fake websites, may inadvertently divulge sensitive personal and financial information. 


In this manner, AI-powered analytics become a dual-use technology; while they are indispensable for threat-hunting to identify zero-day vulnerabilities, they also potentiate the arsenal of cybercriminals seeking to exploit the very systems they are meant to protect.


Phishing is another example of AI’s double-edged nature. While hackers could previously be thwarted in their phishing attempts by obvious grammatical errors or a suspicious lack of natural-sounding English language, today’s cybercriminals use LLMs to craft highly convincing emails to trick unsuspecting victims into infecting their devices. And since AI can generate code, one need not be Linus Torvalds—you just need to know how to prompt the AI to get the right output. 


In addition, cybercriminals have begun to use AI to quickly develop advanced-level malware that can avoid detection. These new zero-days are equipped with polymorphic properties, allowing them to evade even the most advanced filters. While all this sounds worrying, white hats and other experts haven’t been sitting idly.  


Cutting Edge AI-Driven Threat Detection Tools

To effectively combat advanced cyberthreats, it's crucial for cybersecurity professionals to utilize the most sophisticated tools available, which now increasingly rely on AI and advanced analytics. Below are some of the cutting-edge, analytics-driven AI tools designed for threat detection and mitigation.


1. Darktrace's Cyber AI Platform

Darktrace uses machine learning algorithms and advanced analytics to detect anomalies in network behavior. By monitoring data in real-time, this platform can uncover hidden vulnerabilities, zero-day exploits, and sophisticated attacks. Its AI-driven analytics provide detailed threat visibility, allowing for preemptive actions against potential threats.


2. CrowdStrike Falcon

Utilizing the power of cloud-based analytics, CrowdStrike Falcon offers real-time threat monitoring. It not only identifies known threats but also utilizes advanced analytics to predict future attack vectors. It's particularly effective at tracking the behavior of potentially compromised devices within a network.


3. Palo Alto Networks' Cortex XDR

This platform combines machine learning and big data analytics to provide a holistic view of a network's security posture. Cortex XDR can identify complex attack patterns by analyzing disparate sets of data from various sources within an organization's network, making it adept at identifying otherwise hidden threats.


4. Cybereason's Defense Platform

Leveraging behavior-based analytics, Cybereason’s platform goes beyond signature-based detection. Its AI algorithms can track unusual behavior within a network, providing early warnings of an internal or external threat. This makes it effective for identifying and mitigating advanced persistent threats (APTs).


5. Cynet 360

Cynet 360 uses machine learning and advanced correlation algorithms to provide a unified threat detection and response platform. It's able to sift through large volumes of data to identify subtle, complex attack vectors, offering both pre- and post-attack analytics.


Machine Learning for Threat Analysis

AI and ML go hand in hand, which is why machine learning is becoming a cornerstone in the realm of threat analysis, offering cybersecurity professionals tools that can process and analyze vast amounts of data at unprecedented speeds. But how exactly does machine learning contribute to a more robust, proactive cybersecurity approach?


Anomaly Detection

Traditional security measures often rely on established patterns of malicious activity, but machine learning takes this a step further by continuously analyzing network behavior to detect anomalies. If a user is suddenly accessing data they've never accessed before, or if there's a surge in data transfer from a particular server, machine learning algorithms can flag these as potential threats, enabling quicker responses.


Predictive Analytics

Beyond identifying current anomalies, machine learning can utilize historical data to predict future threats. Advanced predictive models can determine the likelihood of specific types of attacks based on current behavior patterns within the network, allowing for preemptive measures to be put in place.


Zero-Day Vulnerability Identification

Machine learning excels in pattern recognition, making it useful in identifying zero-day vulnerabilities. By analyzing code behavior and comparing it to known vulnerabilities, machine learning algorithms can detect new, unknown threats, giving cybersecurity teams a critical advantage.


Phishing Detection

Traditional email filters may fall short when it comes to identifying sophisticated phishing attempts. Machine learning can analyze the nuances of an email's text, sender details, and even the behavior of the user receiving the email to assess the likelihood that it's a phishing attempt, flagging suspicious emails for further investigation.


User Behavior Analysis

Machine learning algorithms can learn the typical behavior patterns of users within a network. Any significant deviation from these patterns—such as unusual login times or data access—can be flagged for review, which becomes useful for detecting insider threats or compromised accounts.


Automated Incident Response

Once a threat is identified, machine learning can also assist in automating the incident response. Algorithms can determine the severity of the threat, suggest appropriate countermeasures, and in some cases, execute predefined actions to contain the threat. The most advanced systems can even send automated reports to a cyber insurance provider. 


Risk Assessment and Optimization

Machine learning models can provide a global overview of an organization's cybersecurity posture by continuously assessing the risk levels associated with various assets and operations. This enables organizations to focus their efforts where they are most needed and gather analytics data accordingly.


Final Thoughts

While sophisticated cybercriminals may be able to make use of advanced-level technological capabilities, twisting AI and machine learning tools to their advantage, cybersecurity is keeping up with the latest threats. 


Whether it’s anomaly detection, predictive analytics, or even risk assessment, there’s a range of tools that provide businesses of all sizes with the AI-powered defense they need. By working with these tools and strategies, cybersecurity experts can be both ready for the threats of tomorrow and the job market of tomorrow. 

Isla Sibanda

Freelance Writer,

Machine Learning & Artificial Intelligence

threat intelligence artificial intelligence & machine learning Orchestration / Automation security analytics intrusion prevention/detection endpoint detection visibility & response intrusion detection

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