Artificial intelligence (AI) is rapidly becoming infused into countless aspects of our lives, and its impact on cyber security is particularly transformative for businesses.
In this ever-changing technological world, AI can act as a digital guardian that will protect and secure an organisation’s digital assets. Behind the scenes, one technology that is underpinning these modern, AI-driven cyber security measures is machine learning.
What is machine learning and why is it necessary? Put simply: machine learning enables IT systems to learn from data without explicit programming. Using machine learning and data analysis, AI can seamlessly automate the detection, prevention, and response to cyber threats.
Traditional security can be like finding a needle in a haystack, but AI excels at processing vast amounts of data in an instant. Machine learning algorithms allow AI to sift through data and spot unusual patterns that may signal a breach. If your system has a sudden surge of logins from an unfamiliar location, AI can flag it.
Understanding machine learning in cyber security
Machine learning differs from traditional rule-based security systems that rely on predefined instructions to identify threats. Instead, machine learning models are trained on large datasets of normal and malicious activity. By analysing this data, the models learn to recognise patterns and anomalies that indicate potential cyber threats. For example, a machine learning model can identify unusual login patterns, such as multiple failed attempts or logins from unfamiliar locations, which could signal that an account has been compromised.
A key advantage of machine learning in cyber security is its ability to adapt and improve over time. As new data becomes available, the models can learn and adjust their algorithms to better detect evolving threats, without requiring manual updates to the system. It just keeps getting better.
This adaptive learning capability is critical in the fight against cybercrime, where attackers constantly develop new and sophisticated techniques. Look no further than the alarming surge in cyber attacks we have seen of late, from crippling ransomware assaults on healthcare systems to data breaches that compromised millions of personal records.
Across the board, machine learning is enhancing numerous cyber security practices, from phishing (and quishing!), to network security and dark web monitoring, its impact cannot be underestimated.
Key applications of machine learning in cyber security
Phishing and social engineering defence
Machine learning has significantly enhanced email filtering to combat sophisticated phishing attacks. AI systems analyse various email features, including content, sender behaviour, and embedded links, to identify malicious intent.
Unlike traditional filters that rely on known phishing indicators, machine learning models detect subtle anomalies and patterns indicative of phishing, even in novel attacks. By continuously learning from new data, these systems adapt to evolving phishing techniques, providing a more robust defence against deceptive emails.
AI also plays a role in protecting against QR code phishing, nicknamed as “quishing”, and enhancing website verification. AI algorithms can analyse QR code destinations for suspicious URLs and verify website authenticity by checking for inconsistencies and potential spoofing.
As QR codes became enormously popular in the wake of the pandemic, scammers seized the opportunity to exploit them. In our experience, between 40-70% of employees will be deceived by a QR code-based security attack, making them a serious threat vector. With multiple reports indicating that this tactic is on the rise among scammers, it’s imperative that employers help staff know how to identify and handle a potential threat. Bespoke QR code security training is a tried and tested approach here.
Network security & intrusion detection
In network security and intrusion detection, machine learning plays a crucial role in identifying unusual network traffic that could indicate a breach. AI systems analyse network traffic patterns, user behaviour, and communication protocols to establish a baseline of normal activity.
By continuously monitoring network activity, machine learning models can detect deviations from this baseline, such as sudden spikes in data transfer, unauthorised access attempts, or communication with suspicious IP addresses.
Real-time monitoring gives security teams superpowers. By empowering them with immediate visibility into potential threats, they can respond swiftly and effectively.
Automated threat response capabilities, powered by machine learning, can further enhance network security by automatically blocking malicious traffic, isolating compromised devices, or triggering alerts to security staff. This combination of proactive detection and automated response strengthens an organisation’s ability to protect its network infrastructure and sensitive data from cyberattacks.
Traditional vulnerability assessments are often priced at an eye-wateringly high amount, often in excess of £1,000 a day, and are usually reported only once a year. We believe in providing monthly reports and keeping costs significantly lower than this, to help customers stay on top of vulnerability exposure without breaking the bank.
Dark web monitoring & data leak prevention
Monitoring the dark web and the prevention of data leaks is critical for proactive cyber security. Machine learning plays a key role in this by scanning the dark web for stolen credentials, compromised accounts, and sensitive information that may have been leaked or sold. Lost or stolen passwords that are published on the dark web can make it easy for criminals to access your critical business systems. It’s like handing the front door keys for your house to a burglar.
By identifying exposed data, organisations can take proactive steps to mitigate potential damage. This may include resetting passwords, enhancing authentication measures, and notifying affected customers, thereby reducing the risk of further exploitation.
The future of AI machine learning in cyber security
The future of AI in cyber security is one of continuous evolution, driven by automation and predictive threat intelligence.
Machine learning will play an important role in automating responses to cyber threats, enabling security systems to react instantly to incidents without human intervention. Predictive threat intelligence will allow security systems to anticipate and neutralise attacks before they occur.
Looking ahead, the integration of machine learning with emerging technologies like blockchain and quantum computing holds immense potential. Blockchain’s tamper-proof nature could enhance the security of data used to train machine learning models.
As technology continues to rapidly advance, and the abilities of AI as a “digital guardian” continue to evolve, the most resilient businesses will be the ones who prioritise maturing their security capabilities alongside it.
Need help improving your company’s cyber resilience? Then get in touch with Cyber Security Awareness today.
