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Understanding the Role of AI in Cybersecurity for Your Digital Security

November 7, 2023
January 14, 2024
4 min
Understanding the Role of AI in Cybersecurity for Your Digital Security

Welcome to an era where artificial intelligence (AI) has become embedded in cybersecurity, completely changing the landscape of the industry for both attackers and defenders. In this article, we will explain the definition, advantages, and disadvantages of AI in cybersecurity and how to apply AI in cybersecurity. Let's explore how this innovative technology can provide incredible benefits to cybersecurity, as well as identify the things that need to be considered so that we can utilise it optimally.

Definition of AI and Cybersecurity

AI is the ability of computers to learn from existing data and situations and then use this knowledge to make decisions or perform tasks without direct human intervention. In the context of cybersecurity, AI is used to detect cyber threats, identify suspicious behaviour, and address attacks quickly and accurately.

Advantages of AI in Cybersecurity

Enhanced Threat Detection and Response

AI and Machine Learning (ML) can analyse large amounts of data at speeds far beyond human capabilities. This allows them to identify suspicious activities, anomalies, and potential threats in a real-time. By learning from each attack, AI systems can continuously improve their detection capabilities, allowing them to anticipate and identify even the most sophisticated threats.

Proactive Security Measures

Conventional cybersecurity methods react to threats as they occur. AI, on the other hand, can predict potential threats and take preventive measures. Through ML algorithms and predictive analytics, AI can identify patterns and trends in data that may signal an impending cyberattack.

Automating Routine Tasks

AI can automate routine tasks, thus freeing up valuable time for cybersecurity professionals to focus on complex issues. Tasks such as monitoring logs, analysing network traffic, and updating security systems can be simplified through AI.

Disadvantages of AI in Cybersecurity

Dependence on Data

AI performance is highly dependent on the quality and quantity of data provided. If the data is biased, inaccurate, or incomplete, AI may make wrong decisions, leading to false positive or negative results in threat detection.

Potential AI-Powered Attacks

Cybercriminals can also take advantage of AI. Sophisticated AI-powered attacks can learn and adapt to security measures, making them harder to detect and counter. AI can even be used to automate cyberattacks, increasing their scale and frequency.

High Implementation and Maintenance Costs

Developing, implementing, and maintaining AI systems is expensive and requires skilled labour. In addition, these systems must be constantly updated and trained with new data to remain effective, adding to the overall cost.

A Real Case that Happened in Singapore

SingHealth AI cybersecurity cyberattacks

In 2018, Singapore's largest healthcare group, SingHealth, suffered a massive cyberattack. The personal data of 1.5 million patients, including Singapore's Prime Minister Lee Hsien Loong, was stolen. Post-breach analysis revealed that the attack was sophisticated, likely state-sponsored, and potentially detectable and defensible  with AI-based systems.

Singapore has since taken steps to harness the power of AI in cybersecurity. The government partnered with various technology companies to develop AI-based systems to predict and detect sophisticated cyberattacks. These AI systems have significantly reduced the response time to threats and improved the overall cybersecurity posture.

Applying AI to Cybersecurity

Artificial Intelligence (AI) is fast becoming a critical component in the cybersecurity landscape, providing superior threat detection, remediation, capabilities, and offering promising solutions to some of the most pressing cybersecurity challenges. Here's a look at how AI can be applied to cybersecurity, complete with examples.

1. Threat Detection and Analysis

AI excels at recognising patterns and anomalies in massive data. By applying AI and Machine Learning (ML) algorithms, cybersecurity systems can analyse network behaviour, identify unusual patterns, and detect threats in real-time.

Example: Many cybersecurity companies use AI-powered Intrusion Detection Systems (IDS). These systems monitor network traffic, learning what is normal. When anomalies are detected - for example, very large data transfers during off-peak hours - AI alerts cybersecurity professionals of a potential breach.

2. Predictive Analysis

AI and ML can also be used for predictive analytics. These systems can identify patterns and trends in data, which may indicate future cyberattacks.

Example: Companies like Darktrace use AI to create an ever-growing understanding of every device, user, and network in an organisation. They call it an 'Enterprise Immune System'. It learns the 'self' from the 'non-self', finding irregularities that indicate an emerging cyber threat.

3. Phishing Detection

AI can be used to combat phishing attacks. By analysing the text in emails, AI can find signs of phishing attempts, such as certain language patterns, email headers, or anomalies in sender details.

Example: Google uses an AI system to filter out spam and phishing emails in Gmail. It uses Natural Language Processing (NLP) to examine the content and context of emails to block deceptive emails.

4. Automated Incident Response

AI can significantly reduce response time after a security incident. By automating repetitive and routine tasks, AI allows cybersecurity teams to focus more on strategic and complex issues.

Example: IBM's Watson for cybersecurity uses AI to automate incident response at the security operations centre. Once an incident is identified, Watson can automate initial response steps, such as isolating the affected system and notifying the appropriate personnel.

5. Password Protection and Authentication

AI can improve password protection and user authentication processes. Biometric logins, such as facial or fingerprint recognition, are becoming increasingly common and are inherently more secure than traditional text-based passwords.

Example: Apple's FaceID on the iPhone is an AI-powered system that uses neural networks to verify identity through facial recognition.


While AI offers enormous potential in advancing cybersecurity efforts, it is not without its downsides. As AI continues to evolve, so does its application in cybersecurity. It is imperative for organisations to understand both sides of the coin so that they can leverage its benefits while minimising its potential risks. The story of Singapore's cyber breach and subsequent adoption of AI serves as an important lesson for organisations around the world.

Secure Your Future

You might be thinking, "I'm not a tech expert, how can I involve myself in protecting myself from cyber threats?" Don't let yourself be left behind in an ever-evolving world. Join the Centre For Cybersecurity and acquire the cybersecurity skills required to enter this exciting industry.

Register now and attend our Information Session for free to find out more about the industry and how CFC can help you kickstart your cybersecurity journey.

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