The Role of AI in Cyber Threat Detection

The global cyber threat landscape is undergoing a twofold evolution: Threats are not only more frequent but also significantly more complex than they used to be. To substantiate: Q2 2024 marked a striking 30% increase in cyber attacks worldwide, with an average of 1,636 attacks made on an organization every week. On top of that, as per the 2020 Webroot Threat Report, 93.6% of malware attacks in 2019 were polymorphic in nature, i.e., they contained self-adjusting codes to avoid detection. As these challenges escalate, the role of Artificial Intelligence, or AI, becomes indispensable to threat intelligence.

Artificial Intelligence, at its core, enables machines to mimic human intelligence (our ability to reason, decide, and recognize patterns). In cybersecurity, this means that AI can not only replicate the cognitive functions of human analysts but also exceed human limitations in computation and speed. One subset of AI that makes this all the more efficient is Machine Learning (ML). ML enables machines (in this case, AI-powered cybersecurity systems) to learn and evolve on the fly without the need for constant human programming. Systems are fed large amounts of data to learn how to spot patterns, predict behaviors, and understand deviations. Machine learning can further be categorized into three types:

1. Supervised Learning: The system is trained using labeled data. This requires human assistance and is best for making algorithms understand the relationship between the inputs and the outputs.
2. Unsupervised Learning: The system is trained using unlabeled data. This is not supervised by a human, and helps identify patterns that have not been discovered yet. Best suited for detecting new risks.
3. Reinforcement Learning: In this type of learning, the algorithm is trained using a trial-and-error method, where it receives a reward for correct actions, while a penalty is imposed for incorrect ones.

Below are the top four benefits of introducing artificial intelligence for cyber threat detection:

1. Enhanced accuracy in identifying threats with reduced false positives AI maximises the productivity of security teams by instantly integrating multiple data sources to understand the context behind an alert. This reduces unnecessary alerts and helps focus on real threats that pose potential damage to the organization. For example, AI can quickly differentiate between a legitimate login attempt and a suspicious one by analyzing the user’s past behavior and location.

2. Speed and efficiency in processing and analyzing large volumes of data Compared to traditional threat detection, where human analysts spent ages gathering and interpreting data, AI revolutionizes cybersecurity. It can collect security data from various sources, clean and standardize it, and analyze both quantitative and qualitative data at an unimaginable speed. This superhuman efficiency equips the security teams with meaningful insights into where the system currently stands without any hassle.

3. Proactive threat detection through predictive analytics Predictive analytics, a set of technologies that use current and historical data to predict future performance, is a game-changer in cyber threat detection. Organizations can now evaluate which vulnerabilities are most likely to be targeted, identify emerging malware by analyzing the existing strains, as well as accurately detect anomalies to flag suspicious or malicious activity.

4. Scalability to adapt to evolving cyber threats Cyber threat detection systems that use machine learning models can effectively evolve themselves on the go as they counter more threats and get more data to learn from. This dynamic approach enables systems to automatically refine their detection capabilities and adapt to the changing and more sophisticated cyber threat landscape.

Let’s understand the role of AI in detecting cyber threats at a more practical level:

AI improves network security mainly by identifying anomalies in the network traffic and creating micro segments to reduce the attack surface, and automating network and infrastructure monitoring. Let’s break this down.

• Anomaly Detection: AI pulls in data on network traffic, system logs, and user interactions to lay a baseline of typical network activity. Any deviations from this norm would mean potential threats and security issues.

• Network Microsegmentation: Automated identity-based recommendations, user grouping, and zero-trust security are some ways to break down large networks into manageable segments and reduce the overall attack surface.
• Automated Network Security Monitoring & Management: Organizations can deploy AI-driven threat detectors that automatically monitor the network security in real time, detect malfunctions, track non-compliance, and even respond to certain threats.

The rise in remote/hybrid work models and Bring Your Own Device (BYOD) policies necessitates the tightening of endpoint security. This is where Next-Generation Antivirus (NGAV) emerges as a truly advanced solution for securing endpoints in a network. Combining AI, ML, and behavioral analytics with other endpoint security tools, such as MacKeeper, helps block both existing and new threats in user devices. Most importantly, NGAV has a cloud-based architecture that not only allows organizations to deploy it almost instantly and remotely, but also provides real-time threat intelligence. For an in-depth look at one of the leading NGAV solutions, check out Cybernews’ Bitdefender review to learn how it provides robust endpoint protection. Besides NGAV, Endpoint Detection and Response (EDR) can also be integrated with AI to flag and mitigate threats at network endpoints using a central management hub.

Machine learning has become a powerful tool in detecting and preventing fraud. It works by analyzing large volumes of transactional and behavioural data across various customer touchpoints—such as login patterns, purchase behaviour, and payment methods. Over time, ML models learn what a “normal” transaction looks like for a given user or system.
Once these patterns are established, the models can quickly flag unusual activity—like sudden location changes, unexpected spending spikes, or irregular login attempts—as potentially fraudulent. One emerging threat in this space is AI-powered voice spoofing, where attackers use synthetic voices to impersonate real people. To address this, ML models can be trained using a variety of voice samples to detect fake audio. Tools like a free AI voice generator can provide realistic examples that help the model learn the subtle differences between genuine and synthetic voices. This added layer of voice verification is increasingly important for securing voice-based transactions and identity checks.

AI plays a defining role in behavioral analytics—whether that’s of a user, an entity, or a system. Based on the object of analysis, BA can be divided into the following three categories:

• User & Entity Behavior Analytics (UEBA): Organizations leveraging UEBA can monitor and analyze the behavior of a user or an entity (devices, applications) to look for malicious activity. For example, UEBA can help differentiate between an unusual login and a suspicious login attempt. This especially takes place during the app development as it's an important part of security.

If you're wondering what does a web developer do in this context—it includes integrating behavioral analytics tools and ensuring the application is resilient against threats like session hijacking or unauthorized access

• Network Behavior Analytics: By analyzing network traffic, AI can flag network patterns that deviate from the standard. For example, it can alert the security teams when someone tries to export an unreasonably large amount of data (say images) to a recipient unknown to the network.

• Insider Threat Behavior Analytics: Also known as ITBA, it assists organizations in identifying users who might be misusing their privileges, indicative of potential insider threats. As a result, you can find out if someone is illegally accessing sensitive information, leaking data, installing unknown software, wiping out critical system files, etc.

However, AI-driven cyber threat intelligence has its limits. Below are four major challenges with using AI to detect cyber threats:

The math is simple: If ML models are trained on prejudiced data to detect cyber threats, the system will only reinforce that bias in its workings. For instance, if the system is trained on past network traffic patterns when 99% of users operated on Windows, it will inadvertently flag a login attempt from a Linux-based device as a potential threat.

Another significant challenge to introducing AI into cyber threat detection is the increasing volume of adversarial attacks. Threat actors use these attacks to disrupt the input data on which the ML algorithms train, so that the output (decisions or predictions made by the AI) are also incorrect.

Popularly known as the “black box” problem, complex machine learning algorithms lack transparency. This means that it’s impossible to understand how the model made a particular decision, which in turn, makes it difficult to fix such systems when they deviate from expected functioning. As a result, analysts may find it difficult to understand and respond to flagged threats if the reasoning behind their detection is unclear.

AI-based cyber threat monitoring and detection involves data collection that may unwittingly pave the way for numerous ethical and privacy concerns. These include excessive surveillance on individuals and their personal information, gathering more data than necessary for analysis, and collecting user data without their consent.

With cybersecurity solutions like predictive analytics, behavioral analytics, and real-time anomaly detection, artificial intelligence continues to redefine cyber threat intelligence. However, proactive adaptation and innovation in AI-driven cybersecurity systems are indispensable to truly combat the dynamic threat landscape. At the same time, organizations must learn to balance technological advancement with ethical responsibility to build a more secure digital world.

About the Author:
Prateek Arora is a content marketing specialist at thestartupinc.com, where he delves into B2B and SaaS topics that transform website visitors into paying customers. With a passion for exploring innovative marketing strategies, Prateek enjoys researching and crafting content that resonates with target audiences. In his free time, he loves driving around the city and hanging out with friends, finding inspiration in the vibrant urban landscape.