Artificial Intelligence (AI) is rapidly transforming the Cybersecurity sector, reshaping job roles, skill requirements, and organizational structures. As AI technologies advance, cybersecurity professionals are experiencing significant changes in their daily tasks and responsibilities.
Before AI, most Cybersecurity tasks were handled by the human workforce. Since the introduction and adoption of AI tools in the Cybersecurity world, most of the routing jobs like monitoring, flagging and analyzing are performed by AI tools in a more efficient and faster way. While this reduces the need for the human eye, it requires humans to train and set up AI tools as well as oversight of the AI tools.
Because of AI, people working in cybersecurity could be displaced. AI is good as an assistant along with the person working, but certain jobs could potentially replace jobs of people since it increases efficiency, automation, and saves money. Tasks that involve repetition are the jobs AI would be most likely to replace.
However, recent advancements in artificial intelligence have significantly accelerated its adoption across the cybersecurity landscape. These innovations go beyond basic automation and reflect a shift toward more intelligent, adaptive, and context-aware systems. As AI technologies mature, they are becoming more capable of making decisions in complex, high-stakes environments like security operations, where speed, accuracy, and adaptability are critical.
A notable area of progress is the development of deep learning models, which allow AI systems to analyze unstructured data such as images, videos, and raw text. In cybersecurity, this enables improved detection of phishing attacks through email pattern recognition or identifying malicious files through visual and behavioral analysis. For example, AI tools can now recognize fake login pages or manipulated URLs designed to trick users—tasks that traditional firewalls and antivirus software might miss.
Another transformative trend is the rise of AI-powered Security Orchestration, Automation, and Response (SOAR) platforms. These platforms integrate multiple security tools and automate complex workflows, such as incident detection, escalation, and containment. They reduce the burden on human analysts by handling low-level tasks and escalating only the most serious threats, effectively blending human expertise with machine efficiency.
AI is also fueling the growth of behavioral biometrics, which track user behavior—such as typing speed, mouse movements, and navigation habits—to detect anomalies that may indicate account compromise. This continuous authentication approach moves beyond static credentials and passwords, offering a more dynamic and secure model of user verification.
In addition to defensive uses, AI is being applied in cyber threat intelligence. By scanning online forums, dark web markets, and leaked databases, AI systems can identify potential threats and emerging attacker tactics long before they are widely deployed. Natural Language Processing (NLP) plays a vital role here, helping systems parse human language across different languages and formats to extract actionable insights.
The rapid advancement of generative AI—particularly large language models (LLMs)—is another significant development. While these tools can assist defenders by generating security documentation, writing detection rules, or summarizing incident reports, they can also be exploited by adversaries to craft more convincing phishing emails, generate malicious code, or spread disinformation. This dual-use nature makes generative AI both a valuable resource and a potential threat.
Drawing from Article 4: "Advances in intelligent information technology: re-branding or progress toward conscious machines?", there is ongoing debate about whether current AI systems represent true cognitive progress or merely enhanced pattern recognition. While AI has not reached the level of human-like consciousness or general intelligence, its increasing ability to mimic decision-making processes creates the illusion of autonomous thinking. In cybersecurity, this means AI tools can perform some analyst functions—such as prioritizing incidents or recommending remediation steps—but still require oversight to ensure contextual accuracy and ethical use.
In terms of workforce impact, these technological advancements are not eliminating cybersecurity jobs outright but shifting the skills required. Professionals are now expected to understand how AI tools work, how to interpret their outputs, and how to manage their limitations. This shift is echoed in other fields as well, such as radiology (Zanardo et al., 2024), where AI is viewed as a collaborator rather than a replacement. Likewise, AI in cybersecurity is best viewed as augmenting human expertise, allowing professionals to focus on higher-order analysis, strategic planning, and AI governance.
Artificial Intelligence is a strategic area for upscaling in cybersecurity. Artificial Intelligence (AI) is clearly listed as an important area where professionals should upskill to remain competitive and valuable in the job market. It is grouped with other digital skills like data analysis, data visualization, and digital marketing, indicating its significance in modern cybersecurity functions. If professionals don’t upskill to keep up with AI, then they could start falling behind. Data analysis, threat detection, and incident response are tasks that AI is used to automate.
The rapid development of information technology (IT) has created new pathways for addressing global sustainability challenges while also raising concerns about digital security in educational environments. Artificial intelligence (AI) is increasingly recognized as a tool for advancing sustainability by enabling more efficient resource management, predictive modeling, and decision-making processes (The Intersection Between Artificial Intelligence and Sustainability: Challenges and Opportunities, n.d.). Applications such as AI-driven climate modeling, optimization of renewable energy systems, and sustainable supply chain management illustrate the transformative potential of intelligent technologies in reducing waste, lowering carbon emissions, and promoting long-term ecological balance. These innovations also support circular economy initiatives by improving recycling processes and material recovery, further advancing global sustainability objectives. In parallel, the growing reliance on digital platforms in education highlights the importance of cybersecurity in safeguarding both data and intellectual property. School libraries, which serve as central hubs for digital learning resources, face increasing threats from cyberattacks and data breaches. As noted in Cybersecurity and School Libraries (n.d.), recent developments such as zero-trust security models, AI-assisted threat detection, encryption protocols, and multi-factor authentication are critical in maintaining secure access to digital platforms. These measures not only protect student privacy and institutional data but also strengthen trust in digital educational tools. When considered together, the advancements in AI for sustainability and cybersecurity in education demonstrate the dual role of IT in driving innovation and ensuring safety. By leveraging AI to optimize sustainable infrastructure while implementing robust cybersecurity frameworks, schools and organizations can foster responsible digital practices, enhance efficiency, and create resilient systems that deliver both environmental and educational benefits.