Technology plays an increasingly important role to individuals and businesses globally, yet simultaneously, the threat of cybercrime also poses an increasing challenge.

Cyberattacks are becoming increasingly refined, meaning that conventional cybersecurity measures struggle to keep up. However, the emergence of artificial intelligence (AI) and machine learning (ML) has opened up new opportunities for developing and implementing AI-based cybersecurity technologies and systems that can help combat cybercrime.

How is AI used in Cybersecurity?

 

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AI can quickly detect and adapt to new and unknown threats, then it will learn and improve its protection. AI-powered security systems offer real-time alerts to potential threats and continuously monitor networks, devices and applications, removing human delay and response time.

More specifically, machine learning can play a significant role in discovering data insights and bolstering cybersecurity. By utilising machine learning, security processes can be enhanced, and security analysts can quickly identify and prevent new attacks.

ML can automate the process of finding, contextualising and prioritising relevant data that might cause a threat. The algorithms scrutinise enormous amounts of data, recognise patterns and then detect anomalies that could indicate a potential cyberattack. They can detect suspicious network activity and find dark web forum posts that could indicate a data breach.

However, there is still scepticism. According to The CISO Report, commissioned by Splunk, 70% of CISOs fear generative AI will give cyber criminals an advantage. But almost paradoxically, they are also excited about the potential for AI to bolster cyber defence:

  • 35% of CISOs are already using AI for security applications
  • 61% will likely use it in the next 12 months
  • 86% believe generative AI will alleviate security skills gaps and talent shortage
Benefits of Machine Learning in Cybersecurity

 

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As many traditional security systems prove slow and ineffective against a growing variety of complex cyberattacks, AI is proving to be a dependable alternative. AI’s ability to ‘learn’ from previous behaviour enables rapid, actionable insights when confronted with new or unfamiliar information or behaviours. It can make logical inferences based on potentially inadequate data subsets and provide several solutions to a known problem, allowing security teams to choose the best course of action.

  • Malware and ransomware detection and prevention

Models can be trained to help anti-virus solutions fight all types of malware, such as adware, backdoor, ransomware, spyware and trojans. Sophisticated algorithms are being used to recognise even the slightest anomaly in the behaviour of ransomware attacks, then run pattern recognition before a system is infiltrated.

  • Pattern recognition

AI can learn normal patterns in data, whether it’s network traffic, system logs, user behaviour or application usage. By recognising these patterns, it can subsequently identify deviations that might indicate anomalies or potential threats.

  • Real-time analysis

Machine learning software can continuously monitor and analyse large volumes of data in real-time, allowing for immediate detection of anomalies as they occur, minimising potential damages.

  • Reducing false positives

By analysing data comprehensively and understanding normal behaviour, AI can reduce false positives, meaning it’s better at distinguishing actual anomalies from normal irregularities, ensuring that security teams focus on legitimate threats.

  • Multimodal analysis

AI can analyse different types of data simultaneously, such as network traffic, system logs, user behaviour and even external threat intelligence, providing a holistic view of potential anomalies.

  • Bot recognition

Bots make up a substantial portion of internet traffic and can present substantial risks, such as account takeovers and data fraud. AI and machine learning can help organisations gain a deeper understanding of website traffic and differentiate between good bots, bad bots and human users.

  • Zero-day threat detection

AI algorithms can identify previously unseen threats by analysing patterns, behaviours and anomalies, even if there’s no historical data available. This capability is crucial in detecting zero-day attacks.

  • Automated response

AI-powered systems can automate responses to certain threats based on predefined rules or learned behaviours. For instance, they can isolate compromised systems, block suspicious IP addresses or terminate malicious processes autonomously, enabling faster reaction times and reducing the impact of cyber incidents.

  • Fraud detection

In financial sectors, machine learning algorithms are used to detect fraudulent transactions or activities by analysing patterns in user behaviour, spending habits, atypical transaction timings or transactional data. Organisations can significantly reduce financial losses, protect sensitive data and maintain trust with their customers by providing a secure environment for transactions and interactions.

  • Predictive analytics

By analysing historical data, AI can predict potential fraudulent activities. This enables proactive measures to be implemented, such as flagging suspicious transactions for further review before they are completed.

  • Behavioural biometrics

AI can employ behavioural biometrics to authenticate users based on their unique behaviour patterns, such as typing speed, mouse movements or interaction patterns, making it harder for fraudsters to replicate.

  • Vulnerability scanning

AI-powered tools can automatically scan networks, applications or systems to identify vulnerabilities. AI can analyse vulnerabilities based on various factors such as severity, potential impact, exploitability and relevance to the specific environment, helping to prioritise which vulnerabilities should be addressed first.

  • Patch Management

AI and machine learning can assist in the patch management process by identifying which patches are necessary, testing their compatibility and prioritising deployment to minimise system disruptions.

  • Facial and voice recognition

AI-driven facial and voice recognition technologies verify users’ identities by analysing unique facial features or voice patterns, continuously improving accuracy through machine

Examples of AI Cybersecurity Detection Systems

 

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  • Amazon GuardDuty, an AI-based threat detection service, uses machine learning to analyse AWS logs and identify potential security threats in real-time.
  • IBM Watson for Cybersecurity, another powerful AI-based threat detection system, analyses security data from multiple sources, such as logs and security alerts. This system can identify threats that traditional security systems may have missed.
  • CylancePROTECT is an AI-based endpoint security solution that uses machine learning to detect and prevent cyber threats. Its predictive model can identify and block malicious files and processes before they can execute on an endpoint.
  • Netskope and SentinelOne use AI to scan network traffic and identify malware, and can predict where organisations are most likely to be breached. AI can also improve authentication processes, ensuring that users’ identities are verified throughout their session.
  • OneLogin’s SmartFactor Authentication verifies login attempts in real time and prompts users to verify their identity if there are suspicious actions.
Beware of the Pitfalls of AI in Cybersecurity

 

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Using AI and ML in cybersecurity can be a double-edged sword. AI-based systems are not immune to attacks themselves, so you need to consider the risks and limitations of AI as effective strategies to prevent cybercrime. Designing, testing and appropriately maintaining AI-based cybersecurity systems is crucial to prevent misuse.

Cybercriminals can also harness the power of AI to launch more advanced and sophisticated attacks: the same features that make them so helpful can also make them attractive targets for cybercriminals.

  • Deepfakes

One of the most worrying exploitations of AI technology is the creation of deepfakes, which can manipulate audio and visual content to produce false, but seemingly authentic media. This makes them a perfect tool for disinformation campaigns and can be used by cybercriminals to obtain funds from companies or try to ruin their reputation.

Cybercriminals also use AI to improve their AI algorithms and techniques in areas such as password guessing, human impersonation on social media, and hacking vulnerable hosts.

  • Hackers

AI software is increasingly available to everyone, and criminals can learn how these programmes work to adjust their attacks to avoid detection. Using AI to disguise malware is also possible, leading to potential breaches.

To mitigate these risks, it is crucial to implement a zero-trust security model, which assumes that all users and devices are potentially compromised and require continuous authentication (such as multi-factor authentication) and authorisation to limit potential damage.

  • Limited data availability

Machine learning is not as effective with smaller amounts of data. The process by which AI acquires knowledge and adjusts to new situations involves analysing vast quantities of data to identify anomalies and patterns. The ability of AI software to detect abnormal behaviour is limited when there is insufficient data from which to learn.

Human and AI Collaboration

AI and machine learning doesn’t replace human analysts, but enhances their capabilities. It can sift through enormous amounts of data, flag potential anomalies and prioritise alerts, allowing human analysts to focus on investigation and decision-making.

By assigning time-consuming tasks related to low-level security risks to software, skilled personnel can devote their attention to security aspects that require human intervention.

To achieve optimal results, it is crucial to adopt a comprehensive strategy that combines the expertise of human analysts with the capabilities of AI software.

Conclusion

The use of AI and machine learning in preventing cyberattacks is crucial in the fight against cybercrime, enabling organisations to respond proactively and protect their systems and data.

The benefits:
  • Automate cybersecurity processes and detect threats in the early stages
  • Enable adaptable and proactive defence systems 
  • Identify network vulnerabilities
  • Internalise learnings from previous attacks to prevent future attacks based on similar profiles
  • Help security analysts to quickly identify, prioritise and remediate attacks
  • Minimise human errors
  • Power sophisticated authentication mechanisms, such as facial recognition, fingerprint recognition, motion tracking, retinal scanners and voice recognition
  • Help prevent security threats against endpoints 
  • Provide insights into advanced threats  
  • Scan massive amounts of data to identify malware
  • Understands nuances of normal behaviour to enable the detection of the smallest deviances

It is important to develop a comprehensive approach, combining AI’s power with human expertise, to combat cyberthreats effectively.

To find out more,  please get in touch with the Securus’ cybersecurity experts on 03451 283457.

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