AI and Machine Learning: The Double-Edged Sword in Cybersecurity

Posted on by Isla Sibanda

Artificial intelligence (AI) and machine learning (ML) are nothing short of game-changers in cybersecurity. They make it easy to quickly find and predict threats, automate responses, and improve data analysis. They also help reduce false alarms, secure individual devices, and strengthen overall security. 


However, despite enhancing efficiency and productivity, they have also made it easier for cybercriminals to carry out more advanced attacks. 


Even with all the efforts by businesses and individuals to protect themselves against potential attacks, the estimated cost of cybercrime is expected to rise steadily and hit $10.5 trillion by 2025. This is in large part thanks to the increased use of AI by attackers. 


How Criminals are Using AI in Cybercrimes

Cybersecurity is in a constant state of being in an arms race — while the defensive capabilities of AI and ML are significant, so too are the possible malicious use cases. For example, cybercriminals are leveraging these technologies for sophisticated attacks that can evade traditional modes of detection. AI is also a “logistics” game changer for cybercriminals — allowing them to automate the early stages of cyberattacks.


This not only accelerates the pace of attacks but also allows criminals to operate with discretion, mapping out network vulnerabilities and siphoning sensitive data without triggering alarms.


They are also using ML algorithms to craft convincing, context-aware phishing messages. A recent whitepaper from Egress suggested that 71% of AI-generated email attacks go unnoticed. This makes it challenging for individuals to discern between genuine and malicious communications, amplifying the already significant effectiveness of social engineering attacks.


Thanks to AI and ML, it’s no longer easy to tell what’s real and what’s not. With the rise of deepfakes, phishing emails in perfect English, and other novelties, we may have to relearn what exactly constitutes a threat and what steps we should take to prevent identity theft and ensure online security, both personally and professionally. 

How AI and Machine Learning Are Used in Cyber Defense

AI and ML have become pivotal in enhancing cyber defense strategies. These technologies equip cybersecurity systems with advanced capabilities to identify, analyze, and respond to threats more efficiently than traditional methods. Here are some of the most common uses of these two revolutionary technologies in cybersecurity:

Anomaly Detection

Traditional security measures often fall short when it comes to detecting new or evolving threats. But AI and ML excel in identifying patterns and anomalies in vast data sets, a capability pivotal in cyber defense. AI systems continuously learn and adapt to new data, which allows them to recognize unusual behavior or irregular network traffic that may indicate a cyberattack. This ability is particularly effective against zero day attacks. Once a threat is detected, AI can also assist in formulating a rapid response tailored toward the hitherto unseen threat, minimizing potential damage.

Phishing Detection

According to the FBI, phishing is the most prevalent form of cybercrime, accounting for just a little over 300,000 cases in 2022 alone. This data shows that more must be done to defend against this threat. Businesses and individuals can use AI and ML to analyze email content, including headers and metadata, to identify patterns typical of phishing attempts. 

Automated Response to Incidents

Automating incident response could be one of the biggest boons that AI and ML can provide in terms of cybersecurity. Like a well-optimized set of automated defenses, when properly applied, these technologies can rapidly respond as soon as a threat is detected.


This automation is crucial in mitigating damage, as the speed of response often determines the severity of a breach's impact. 


AI systems can isolate affected systems, close network vulnerabilities, and even deploy patches or updates to defend against the identified threat. This automated response is particularly valuable against fast-spreading threats like ransomware, where every second counts.

Malware Detection and Analysis

AI and ML are highly effective in detecting and analyzing malware. Traditional antivirus software relies on signature-based detection, and while the efforts to maintain up-to-date databases are commendable, this old method struggles to keep up with the rapid evolution of malware, as it is always basically playing catch-up. 


In contrast, AI-based systems can detect malware based on behavior and other attributes, making them more effective against new or unknown types of malware that haven’t been detected or even deployed previously. 


Additionally, AI can assist in analyzing malware to understand its behavior, origins, and potential impact, which is vital for developing effective countermeasures.

Fraud Detection and Prevention in Financial Transactions

These two new frontiers in technology are crucial for detecting and preventing fraud in the financial sector. 


Artificial intelligence and machine learning can analyze transaction patterns to identify unusual activities that may indicate fraud and do so on such a detailed level where small inconsistencies that would evade human oversight can easily be detected. 


This includes detecting anomalies in transaction amounts, frequency, or geographical location. Machine Learning models are trained on vast sets of historical transaction data, enabling them to recognize various patterns indicative of fraudulent activities, such as credit card fraud or identity theft.

Risks Associated with Using AI and ML in Cyber Defense

While AI and ML already do and will continue to play a significant role in cybersecurity, they present the field with numerous new challenges. This isn’t limited solely to the malicious use of these technologies — any new paradigm shift leads to disruptions and a radically different security landscape.

Dependency and Over-Reliance

These systems can streamline and enhance security processes, but they cannot replace human judgment and intuition. Over-reliance can lead to complacency, where security teams may overlook or underestimate threats that AI fails to identify. 

Data Privacy 

There is a significant risk of data exposure when using third-party AI and ML models for cybersecurity. Enterprises often need to feed sensitive data into these models for them to learn and adapt. 


This process can inadvertently breach data privacy regulations like PCI DSS or HIPAA. For instance, in a scenario where a shared AI platform is used by competing corporations, such as JP Morgan and Citi Bank, there is a risk that the AI could unintentionally expose sensitive data from one entity to another. 

Complexity and Lack of Transparency (Black Box Issue)

AI and ML models, especially deep learning systems, are often criticized for their "black box" nature — their decision-making processes can be opaque and difficult to understand, even for experts. 


This lack of transparency can be a significant issue in cyber defense, where understanding the rationale behind a security alert is crucial for effective response. 


If a cybersecurity professional cannot discern why an AI system flagged a particular activity as suspicious, it undermines the trust and reliability of the system, turning what would have been an actionable insight into another step in the process that requires oversight. 


AI and ML have a profound influence on both the offensive and defensive sides of cybersecurity. While malicious actors might often be one step ahead, this should not deter us from diligently protecting our vital assets.


Cybersecurity experts must continuously refine their security strategies, ensuring networks are updated and safeguarded using the most robust security measures available—and in today’s day and age, AI and ML and essential elements. 

Isla Sibanda

Freelance Writer,

Machine Learning & Artificial Intelligence

orchestration & automation artificial intelligence & machine learning identity theft phishing intrusion detection Endpoint Detection and Response intrusion prevention/detection incident response

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