Artificial intelligence (AI) is making its way into cybersecurity systems around the world, and this trend is only beginning. The potential for AI to revolutionise network security is vast. The technology offers new methods to safeguard systems and reduce the manual workload for IT teams. Moreover, with cybercriminals increasingly adopting AI to create more sophisticated attacks, organisations are starting to consider deploying AI to stay ahead.
However, the question remains: How effective is AI in this space?
Streamlining Cybersecurity Systems
AI-based network security systems differ significantly to well-established methods of identifying malicious activity on a network. Signature-based detection systems only generate alerts when they identify an exact match of a known indicator of an attack. If there is any variation from the known indicator, then the system will be unable to pick it up. The alternative is an anomaly-based system, which generates alerts when activity is outside an accepted range of ‘normal behavior. While this takes a more comprehensive view of network activity compared to signature-based systems, it is not without shortcomings. Perhaps the one most often discussed is its tendency to generate false positives when there is unusual activity that is not part of a cyberattack.
Both systems can require extensive manual intervention. IT teams must constantly update databases for signature-based detection systems to ensure that new attack techniques will be recognised as malicious activity. The alternative is that they constantly sift through the alerts generated by an anomaly-based system looking for genuine threats.
AI represents a way to streamline cybersecurity systems, by enabling faster and more precise detection of cyber threats. By processing vast quantities of data, AI systems can identify unusual patterns and behaviours in real time. This imparts key benefits to organisations that leverage AI as part of their cybersecurity defences.
The Value of AI
Reducing Workload: AI-powered tools can significantly reduce the workload for IT teams. They help cut down the number of false alarms generated by security systems. This allows cybersecurity personnel to stay alert without becoming overwhelmed. This reduction in manual work allows security teams to focus on more complex, strategic tasks.
Increased Protection: AI also offers enhanced protection against cyberattacks. Unlike traditional signature-based detection methods, which struggle to identify zero-day threats, AI excels at recognising emerging threats based on behaviour and patterns. This, coupled with near real-time response capabilities, limits the window of opportunity for attackers to cause damage if they manage to infiltrate a system.
Greater scalability and adaptability. Another advantage of AI is that it gives organisations more flexibility. Security teams can quickly respond to increased threat levels or unusual network behavior without having to expand their personnel.
Human Oversight
Although AI offers numerous benefits, it’s crucial to acknowledge the need for human oversight in cybersecurity. We should not think of AI as replacing cybersecurity experts, but rather as a vital tool to support them in running day-to-day operations.
AI systems can process and analyse data rapidly, however they still rely on humans to validate findings, fine-tune the models, and make final decisions, especially when dealing with complex cyber threats. The stakes are high when it comes to the security of an organisation’s confidential data and technology infrastructure. That’s why human involvement is vital in ensuring that AI operates correctly and that correct procedures are being followed.
Mitigating the Risks of AI
While AI can enhance cybersecurity, it also brings several challenges that need to be managed, which highlight the need for human involvement and decision making.
Accuracy of datasets: One significant concern is the accuracy of the data AI systems are trained on. AI’s effectiveness is largely determined by the quality of the data it uses to learn. If training data is incomplete or biased, the system may produce inaccurate results, such as false positives, or a false sense of security, in case of false negatives due to non-detection of e.g. malicious agents. To prevent this, organisations need to rigorously assess the data they feed into their AI models.
Privacy: Another potential issue is privacy. AI systems rely on real-world data to monitor network activity and identify anomalies. This data must be protected through anonymisation or other privacy-preserving techniques to avoid misuse – and should be deleted when it is no longer necessary.
Resource consumption: Running AI models, especially on a large scale, can be demanding in terms of both energy and water, which are required to maintain the systems. This contributes to a higher environmental footprint. By optimising the frequency at which AI models are retrained, organisations can reduce resource consumption. Additionally, the usage of resources will be lower once the model is trained.
Conclusion
While AI offers substantial benefits to cybersecurity, it also presents challenges that must be addressed to ensure its safe and effective implementation. The technology can significantly reduce workload, enhance network security through faster and more accurate detection, and adapt to evolving threats. However, without high-quality data, privacy safeguards, and careful resource management, these advantages may be undermined.
The deployment of AI models should be carefully managed by cybersecurity professionals in order to fully take advantage of its capabilities while minimising risks. AI is a valuable tool – not a substitute for human experience and expertise.
- Cybersecurity
