Although discussions and doubts about AI’s security in the realm of data protection and authorship of its generated material continue – and will likely persist for some time – this technology is undeniably transforming the field of cybersecurity. In this article, we will explore how AI in cybersecurity contributes to a safer cyber environment.
What Is Cybersecurity?
Cybersecurity is the field dedicated to ensuring the safety of systems connected to the internet. It protects hardware, software, and data from malicious attacks aimed at accessing or otherwise compromising private and sensitive information belonging to companies or individual users.
Cyber threats come in various forms, including but not limited to:
- Malware. Malicious software designed to harm computers and networks.
- Ransomware. This threat locks system files, demanding payment for their release.
- Social Engineering. Exploits human interaction to trick individuals into breaching secure data.
- Phishing. Involves fake messages or emails containing links that often lead to stolen login credentials or banking information.
Unlike AI in cybersecurity, traditional cybersecurity relies on signature-based threat detection tools. These tools analyse network traffic against a database of known threats and send alerts when inconsistencies are found, along with recommendations for action.
In traditional processes, much of the analysis is done manually. This approach carries risks, including human error, as it is typically managed by one or a few individuals. Moreover, manual analysis of security alerts and event logs is time-consuming, delaying decision-making and posing significant risks, especially during real threats.
How AI in Cybersecurity Improves Traditional Methods
AI tools can significantly enhance data protection. Technologies like machine learning, deep learning, neural networks, and large language models analyse data, learn from it, and detect complex threats.
AI in cybersecurity algorithms can process large amounts of data, enabling comprehensive network scans at an early stage to assess potential threats or overall cybersecurity levels. This ability to handle vast datasets quickly is a major strength, as human specialists cannot achieve the same results in such a short time.
Additionally, AI systems constantly monitor behavioural patterns and can respond immediately to anomalies. They issue real-time alerts and enable swift decision-making based on machine learning insights, allowing companies and specialists to work more efficiently and effectively.
Continuous learning makes AI algorithms more adaptable in detecting various threats. Recent advancements have also enabled AI systems to catch cyber attackers in certain scenarios.
Another advantage of AI in cybersecurity is its ability to prevent cyberattacks that have outsmarted traditional antivirus programs. These new technologies focus on behavioural analysis, accurately identifying attacks through their unusual actions.
Lastly, machine learning systems automate repetitive tasks. For instance, responding to recurring threats, such as blocking malicious traffic or generating reports on harmful activity, saves time and streamlines risk mitigation.
Final Word
At least for now, AI in cybersecurity cannot operate entirely independently of human oversight, as it requires evaluation of risks and may not detect new behavioural patterns associated with emerging cyberattacks. However, AI significantly helps cybersecurity specialists by providing stronger data protection compared to traditional methods.
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Sources: Balbix, Sophos, TechTarget
