Author: Erik Leong

As digital transformation continues to accelerate, cybersecurity has become increasingly crucial. With the rise of sophisticated and frequent attacks, artificial intelligence (AI) has emerged as a powerful tool for detecting and preventing potential threats. However, as cybersecurity and AI become more intertwined, there’s also a growing risk of malicious actors using these technologies for nefarious purposes. 
Recently, there’s been increased discussion of how AI is a double-edged sword, including a call from 1,800 AI leaders to pause development on AI more sophisticated than GPT-4. In an open letter, these leaders indicated that AI brings “profound risks to society and humanity”, and a pause would allow for safety controls and regulation. 

While there’s been no agreement to pause as of the time of writing this post, regardless, AI is here to stay. In the cybersecurity market, AI offers a way to act as a benevolent agent extending protection, but in the wrong hands, it can be used for nefarious purposes. 
In this post, I’ll walk you through the major benefits and drawbacks of cybersecurity and AI, including how to stay ahead of these rapidly evolving threats.  

The Dark Side of Cybersecurity and AI  

As you can see, the case for using AI for cybersecurity is compelling, but like anything, there’s a dark side.  

A cybersecurity report on the Future of Cybersecurity from the World Economic Forum calls AI and ML a “double-edged sword.” It shares how AI will improve cybersecurity and “lead to innovation in cybercrime, and ML models could train themselves to achieve illicit or devious ends.” 

There’s no clear standard on how AI should be used safely and ethically, and bad actors are already finding vulnerabilities.  

Here are a few examples of how cybersecurity and AI can be exploited. 

Deep-Fake Creation 

AI can be used to create sophisticated deep fake data. For example, AI can create realistic synthetic audio and video conversations by mixing different people’s speech patterns and facial expressions to generate convincing results.  

This technology has become increasingly popular among political campaigns, allowing for the creation of viral videos that spread false information quickly throughout online platforms. When people realize the original video is fake, the damage is done, and the seeds of misinformation are planted.  

Bridgepointe The Dark Side of Cybersecurity and AI

Malware for Malicious Purposes 

AI-enhanced malware can remain undetected while also evading detection from anti-virus solutions, as it can adapt quickly. 

The use of AI in cybersecurity has the potential to enable more targeted attacks on specific users or organizations. For example, hackers can use AI algorithms to study the behavior of an individual user or organization and identify their weaknesses. Then they can create malware that exploits these weaknesses for malicious purposes.                                       

Also, AI algorithms can help hackers discover new attack vectors and even predict what security measures will be implemented so they can adjust or modify their malicious code accordingly. 

One particularly concerning development is using AI-based Generative Adversarial Networks (GANs), designed to generate malicious code that current security tools will not flag. GANs work by creating two competing neural networks, one which generates malicious code and another which evaluates its effectiveness against existing defenses.  

The result is an endlessly evolving stream of sophisticated malware that security solutions often struggle to detect. 

Stealth Attacks  

Due to their automated nature and ability to rapidly analyze complex data sets, AI-based cybersecurity systems can orchestrate highly targeted cyber attacks that evade traditional anti-virus protection measures. 

By leveraging algorithms, these attacks can learn from past attacks and adapt to the environment to remain undetected by the target system. This makes it difficult for human analysts to detect or anticipate them and respond accordingly. 

Tech Cracking CAPTCHA keys 

Utilizing sophisticated ML algorithms, AI models can correctly identify even the most difficult captcha codes with high accuracy. These systems can learn from the input data and recognize patterns that would be difficult for humans to detect.  

For instance, some captcha codes may contain random numbers or letters, but AI systems can distinguish between these elements and communicate the result organizationally. 

In addition to recognizing individual characters, AI cybersecurity helps reduce security threats by detecting malicious user behavior or anomalies associated with suspicious activity.  

Luckily, AI models are being used to detect fraudulent logins, automate code reviews and monitor online transactions for suspicious activities. This helps to prevent cyber attacks and improve overall system security. 

Email Phishing  

AI-driven phishing campaigns are becoming increasingly sophisticated, featuring customized messages that scammers can use to target specific individuals or groups. With automated systems, scammers can use AI to generate hundreds of personalized messages in seconds. 

Another issue with AI-based cybersecurity is its potential to create false negatives. False negatives occur when an algorithm incorrectly labels a potentially malicious email as safe, allowing it to pass through an organization’s security system undetected and unchecked.  

In addition, using AI for cybersecurity may make organizations more vulnerable due to reliance on automation. If an attacker can create a virus that bypasses the automated security system, the results could be catastrophic.                 

Exploiting Neural Networks  

AI Cybersecurity has been increasingly utilized in recent years to exploit neural networks to stimulate bio-authentication data. This type of data used to access secure accounts and services is highly vulnerable and can be easily compromised by malicious AI systems.  

For example, AI-powered facial recognition systems have been used to fake identities and gain access to restricted areas and databases. 

Furthermore, AI technology has enabled cybercriminals to develop deep fakes that can deceive facial recognition algorithms. 

Exploring the Advantages and Applications of AI in Cybersecurity 

Digital transformation has led to the adoption of new technology, rapidly changing security requirements, and potential threats. The sheer size of the potential attack surface and the need to protect data, digital assets, and network infrastructure means greater opportunities for cyber attacks.  

As a result, AI is becoming an essential tool for cybersecurity, as evidenced by its market growth. According to IDC, AI in the cybersecurity market will reach a market value of $46.3 billion in 2027, with a 23.6% CAGR. 

AI is so appealing as it can automate routine tasks while enhancing overall security. It’s also a powerful way to improve compliance through continuous monitoring and reporting of security metrics, and it helps make more informed business decisions.  

With that in mind, here are just a few of the advantages and applications of cybersecurity and AI.  

Fighting Spam  

The role of AI in cybersecurity and spam detection has become increasingly important in combating hackers and other malicious actors. AI can help detect and prevent social engineering attacks.  

For example, AI can detect when someone is pretending to be a government employee, hospital employee, or person of authority to manipulate a vulnerable person for sensitive information.  

By leveraging large datasets, machine learning (ML) models can identify abnormal activity indicating an impending attack and take protective measures before damage is done. 

Anomaly Detection  

By leveraging sophisticated algorithms, AI-driven cybersecurity solutions can detect potential threats and anomalies faster and more accurately than traditional rule-based or statistical methods.  

With the help of machine learning, AI can analyze data from multiple sources—including user behavior, network traffic, and transactions—to identify malicious activity and alert security teams in real-time. 

Preventing DNS Data Exfiltration

AI cybersecurity solutions such as deep packet inspection (DPI) can be used to monitor DNS traffic and detect anomalies or patterns that indicate an attack.  

By doing this, organizations can identify when data is being exfiltrated from their network before it reaches its intended destination or before any damage has been done.  

Advanced Malware Protection

Unlike traditional methods, AI cybersecurity can learn to recognize patterns of malicious behavior and flag suspicious activities even before they have been identified as malicious. 

For example, an AI-based cybersecurity system can detect when a file is being sent through email or downloaded from the web and determine if it contains malicious code. This means malicious files will be blocked before reaching the user’s device or network. 

Additionally, such systems can detect unauthorized access to data and networks. When combined with other security measures, such as firewalls and encryption, this advanced cybersecurity makes it more difficult for hackers to access sensitive information or steal valuable data from organizations.

Reduction of Alert Fatigue

AI can help prioritize alerts based on severity and allow security teams to focus on the most urgent threats first. Alert fatigue is a phenonmenon where cybersecurity personnel become overwhelmed by the sheer volume of alerts and can become desenstized. This can result in critical incidents being missed, or a delayed response.  

By using AI to prioritize alerts, alert fatigue can be greatly reduced and helps to ensure that no threat goes unnoticed or unaddressed. 

Plus, AI-based technologies can provide additional context about an alert or incident, which makes it easier for analysts to assess its severity and identify any potential indicators of compromise (IOCs).

Zero-Day Exploits   

With the help of AI, cybersecurity experts can automate the process of identifying new, previously unknown vulnerabilities (aka zero days) to prevent malicious attacks. Through deep learning and natural language processing algorithms, AI-based security solutions can analyze large datasets of security logs and detect suspicious patterns which could indicate the presence of a zero-day exploit.  

By leveraging automated methods such as ML and Natural Language Processing (NLP), organizations can more quickly and accurately identify threats posed by zero-day exploits, significantly reducing the incidence of malicious breaches.

Enhanced Fraud Detection 

With the rising prevalence of cyberattacks, AI-driven cybersecurity plays an increasingly important role in identity analytics and fraud detection. AI tools are capable of identifying suspicious activities that would otherwise go unnoticed by traditional security systems. 

For example, AI algorithms can generate detailed user behavior profiles to detect anomalies and anomalies associated with fraudulent activity.  

AI-based visual recognition systems (such as the ones used on cell phones) are also being developed for identity authentication. These systems use facial or voice recognition to detect discrepancies between users’ identities and their actual physical or online presence. 

These efforts help reduce the risk of identity theft and allow organizations to secure their networks against malicious actors better.

Advanced Malware Protection Bridgepointe

Asset Discovery  

AI is now vital in asset discovery, helping organizations to effectively identify, detect, and monitor the vast array of connected devices and resources within their digital environment.  

With AI-driven asset discovery solutions, organizations can comprehensively understand the scope and scale of their digital infrastructure and detect potential vulnerabilities or threats. 

By leveraging AI-assisted automated processes, organizations can quickly and accurately scan for an ever-growing list of assets across multiple networks and locations.

These algorithms can detect Web Application Firewalls (WAFs), Network Access Control Lists (ACLs), cloud configurations, privileged accounts, hardware identity certificates, and more.  

This enables the discovery of not only the physical hardware assets on the network but also all the cloud-based data that live in other environments. 

AI Technology’s Role in Cybersecurity is Here to Stay 

When utilized correctly, AI-powered cybersecurity offers  significant advantages. By rapidly and accurately detecting threats, organizations can proactively stay ahead of malicious actors and mitigate potential risks before they escalate into major problems. The fast response times enabled by AI technology allow for timely intervention, reducing the risk of damage or compromise. 

While cybersecurity and AI present new opportunities, they also create potential vulnerabilities that must be proactively addressed and minimized to prevent harm. 

Bridgepointe’s cybersecurity experts can help you leverage AI while ensuring protection against emerging threats. With our proven process for evaluating, implementing, and managing technology from the world’s leading providers, we’ll help you reduce risk and drive sustainable business results. Learn more here.