The Solutions Review editors summarise some of the most important ways AI can impact cybersecurity jobs, employment, skill sets, and more.
Regardless of your position or industry, Artificial Intelligence (AI) can affect internal and external processes in your company. AI is especially true for cybersecurity experts as it has changed the way threat actors plan and execute attacks and introduced new ways to combat potential and active threats. What's less clear is the specific impact AI has had on cybersecurity and whether these experts are the source of concern.
As AI is integrated into cybersecurity operations at an unprecedented level, the format and functionality of corporate cyber teams continues to change rapidly. To track these changes, the Solutions Review Editor outlined the main ways AI has changed cybersecurity, what experts can do to maintain agile during their evolution, and what futures will be for the technologies they use.
Note: These insights were informed through web research using advanced scraping techniques and generation AI tools. Solution Review Editors use a unique multiprompt approach to extract targeted knowledge and optimize content for relevance and utilities.
How has AI changed the cybersecurity workforce?
In just a few years, the impact of AI on cybersecurity has dramatically restructured the role, responsibilities and the required skill set of industry. This transformation has been freed for many as AI technology streamlines user workloads and allows teams to focus on more specialized, high-value tasks and projects. For comparison, consider how the global market for AI in cybersecurity is estimated to reach a market value of USD 133.8 billion by 2030 compared to the US$14.9 billion reported in 2021. These technologies are exploding and don't go anywhere.
However, it is not uncommon for cybersecurity experts to be worried about the rapid adoption of these technologies. Because they have already proven that some tasks and roles can almost be abolished. Below are some of the duties and processes that have been most affected by AI:
AI-driven automation and analysis
AI is reconstructed by expanding scope and compressing cognitive overhead. Traditionally, analysis included log inspection times, alert correlations, and cross-references to Threat Intel Feeds. However, with AI, particularly machine learning (ML) and natural language processing (NLP), companies can automate these time-consuming processes to reduce alert fatigue and allow analysts to focus on the threat of the highest risk.
For example, we will explore how major cybersecurity platforms such as Microsoft Defender XDR and IBM QRADAR use ML models to correlate log entries and contextualize hundreds of alerts into real-time attack stories. These streamlined analyses can dramatically reduce workloads by identifying possible causes, unlocking cross-work insights, and streamlining the process of deploying that data to protect against future threats.
AI may be evolving what “analysis” looks like in cybersecurity, but it is not ready to completely replace the need for human intervention. With AI handling information detection and aggregate information workloads, human analysts commit time and expertise to interpretation, intention modeling, and escalation decisions.
Threat Hunting and Hostile Behavior Modeling
For years, traditional threat hunting has been hypothetical driven. Analysts suspect that certain tactics, EEG, qualification filling or lateral movements occur and are searching for logs or telemetry of artifacts that confirm or expose the suspicion. However, this process is often narrow and human-sided, and is a useful place for AI. Unsupervised learning and clustering capabilities allow AI to identify and track patterns without preconceptions.
AI essentially enabled “continuous hunting.” Some of the major cybersecurity tools already use AI and behavioral models to actively express deviations, such as beacons in new domains and anomalous SMB sharing accessed during odd hours. The AI can run 24/7, so threat hunting doesn't have to be ad hoc anymore. It also adds a new dimension of data engineering to threat hunting, as cybersecurity experts are encouraged (if not entirely expected) to have AI-specific skills in curating telemetry, labeling behavior, and tuning features.
According to Fortinet, there is no denying that AI is a double-edged sword for cybersecurity. It launched 36,000 malicious scans per second in 2024, and a 1,200% phishing attacks surged in late 2022, starting with the rise of Genai.
The emergence of AI-centric cybersecurity roles
The rise of AI in cybersecurity not only impacted existing workflows, but also created entirely new occupations, restructuring the profession around data-centric and model-centric capabilities. These AI-centric cybersecurity roles represent the convergence of traditional areas such as security, data science, ML operations (MLOPS), and even behavioral psychology. Other roles such as “Blue Team Analyst” and “SOC Engineer” can be supplemented or completely replaced by titles like AI threat analyst, ML security engineer, and hostile ML Red Team.
It is also possible that the future of cybersecurity jobs will be more similar to the safety role of AI than traditional Infosec. This focuses on the boundaries of the validation agent, applying RLHF to constrain behavior, and building sandbox testbeds for threat simulation. Its future may be possible, but active and ambitious experts should be vigilant. This trend is because traditional network defenders can become subsequent skill bars unless they are actively retrained.
The metatrend here is beginning to become clear. Cybersecurity has evolved into a data science problem, and the workforce is changing accordingly. Those who infer statistically, build or investigate AI systems and think adversarial can think of as defining the next generation of cybersecurity leadership. While traditional roles may likely last, they may become increasingly similar to operational support for AI-first tools. Anyway, as LinkedIn's Rise Report says, AI literacy will continue to be a skill that “experts prioritize and businesses are hiring more and more.”
Highly skilled for the future
AI is not a new technology, but it is attacking the cybersecurity job market quickly and violently. According to cybersecurity ventures, there were 3.5 million jobs in the cybersecurity industry until 2025, with a growth of 350% from the 1 million open positions reported in 2013.
To enhance that urgency, check the costs of IBM data breach reports. This indicates that half of organizations that have encountered security breaches also face a shortage of security staff. Even if there is one in five organizations using some form of generator AI, the skill gap remains a real challenge. Industry companies need experts who are proficient in hostile and algorithmic logic. That expertise is to ensure that you remain relevant regardless of the future. “We are pleased to announce that we are committed to providing a range of services and services to providing services that will help us to ensure that we are committed to providing services that will help us to ensure that we are committed to providing services that will help us to implement our services,” said Mike Arrowsmith, Ninjaone's Chief Trust Officer. “The best way to reduce the risk of AI is to increase employee training. Especially as AI technology evolves, people need to know what they need to be aware of.”
One of the experts is soft skills. A recent study by Skiilify demonstrated that 94% of engineers consider soft skills more important than ever, including curiosity, resilience, tolerance of ambiguity, dependence on perspective, relationship building and humility. Soft skills can also help cybersecurity experts understand how models break down, how attackers leverage statistical assumptions, and how to envelop AI systems with resilient human surveillance.
By 2028, Gartner predicts that “Genai adoption will break down the skill gap and remove the need for professional education from 50% of entry-level cybersecurity positions,” so it's important to cybersecurity experts to discover and improve unique skills than ever before.
Will AI replace cybersecurity experts?
“AI will not replace cybersecurity professionals, but it will transform the profession,” says Chris Dimitriadis, Chief Global Strategy Officer at Isaca. The cybersecurity marketplace has already changed with AI tools and threats, but conversions are far from over. Even if the profession itself is no longer gone, current cybersecurity practitioners could be left behind as jobs evolve into something unequipped.
In the long run, AI will likely turn cybersecurity experts into decision supervisors. Their responsibility is less concentrated on decision-making and instead emphasizes supervision, calibration and intervention in AI-driven decisions as needed. It's a subtle shift, but if the current workforce is not elevating itself during preparation, you may find their expertise is less valuable than they used to be.
According to Sam Hector, senior strategy leader at IBM Security, AI “will fundamentally shift the skills needed. Humans will focus more on improving strategies, analytics and programs, which will require the continuous skill development of existing staff on the evolving capabilities of AI.” The future of cybersecurity will be charted by practitioners who will expand their perspective, prioritize the growth of experts, engage with peers, and collectively learn how to improve AI-centric skills and literacy.
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