AI in Cybersecurity: Where It Delivers Real Value and Where It Remains Marketing Hype

The cybersecurity industry faces a severe talent shortage, making it increasingly difficult to hire experienced SOC analysts, incident responders, and threat hunters. At the same time, businesses seek greater efficiency and lower costs, while vendors promote their products as highly innovative. These pressures create unrealistic expectations that simply purchasing an AI-labeled solution will resolve major security problems.

What Counts as AI in Information Security Today

Vendors often blur distinctions between technologies by labeling everything as AI. Classical correlation rules in SIEM systems trigger on predefined conditions, such as a user logging in at night, gaining administrative rights, and exfiltrating data. These contain no artificial intelligence, only engineering. Machine learning systems build behavioral models from historical data and flag deviations, yet they require extensive calibration and still produce high volumes of false positives. Generative AI and large language models can draft queries, summarize incidents, and explain detection rules, but they lack business context.

Where Marketing Claims Outpace Reality

One common promise is that AI will automatically discover unknown attacks. Solutions such as UEBA, NDR, and XDR do detect anomalies, for example when an accountant suddenly accesses servers via VPN at 3 a.m. and runs PowerShell. However, the system identifies deviation, not confirmed malice, so analysts must still validate each alert. Another claim is fully automatic investigation of complex incidents. While tools can assemble timelines, they cannot assess which systems are business-critical or predict operational impact without human input.

Assertions that AI will replace SOC analysts also fall short. Modern platforms can group events, suppress some false positives, and generate report drafts, yet they cannot answer questions about client impact, backup availability, or financial consequences. Similarly, generative models can produce plausible security strategies, but they ignore budget limits, corporate culture, and specific regulatory constraints, resulting in generic templates rather than actionable plans.

Areas Where AI Delivers Measurable Value

In anti-fraud systems used by financial institutions such as Alfa-Bank, machine learning has moved beyond simple threshold rules. Modern models analyze click patterns, typing speed, device behavior, and transaction context simultaneously. They can block a transaction made by a pensioner in one city minutes after a high-value purchase from a newly installed app in another country. Behavioral analytics platforms also excel at spotting insider threats by comparing current activity against an individual’s historical profile.

Generative AI assists analysts by translating complex detection logic into plain language and automatically building queries against large datasets. Correlation engines in contemporary SIEM and XDR platforms process millions of daily events and surface relationships spanning days or weeks that no human could review manually.

How to Separate Real AI from Marketing Labels

Organizations should demand concrete metrics from vendors, including false-positive rates, time required to validate each alert, and the percentage of incidents actually detected. Understanding which underlying technology—rules, machine learning, or generative models—is being used remains essential before committing significant budgets to solutions that may deliver only conventional correlation under a new label.