In the history of cybersecurity, artificial intelligence has become the most powerful force multiplier. By the end of 2025, the cost of cybercrime is expected to reach more than $10.5 trillion a year, and there is a global shortage of almost four million cybersecurity professionals. As a result, businesses can no longer rely only on the speed and scale of people. AI can now find, respond to, and even predict threats faster and more accurately than whole teams of analysts could ever do.
Threat detection is the most obvious win. More than half of zero-day malware goes undetected by traditional signature-based tools. However, modern behavioral systems that use machine learning regularly find more than 95% of malware. These platforms set a changing baseline of what is “normal” for each user, device, app, and cloud workload, and they flag even small changes right away. For example, Microsoft’s AI-powered extended detection and response platform stopped 97% of business email compromise attempts in 2024. Also, autonomous response tools like Darktrace have stopped ransomware outbreaks in seconds, which is much faster than any human security operations center could do.
Incident response, which used to be a very manual process, is also changing. Security orchestration, automation, and response (SOAR) platforms that use generative AI can now sort through thousands of alerts every minute, add threat intelligence to them, isolate infected endpoints, and even write messages to stakeholders without any help from people. In big companies, this has cut the average time to respond from hours to minutes and let analysts work on more important investigations instead of doing the same thing over and over.
Vulnerability management, which has been a major problem for security programs for a long time, is finally getting easier to handle. AI-driven prioritization engines look at how likely an exploit is to happen, how important an asset is, and how an attacker acts to focus remediation efforts on the 3–7 percent of vulnerabilities that really matter. Some tools now do even more: they suggest or automatically generate code fixes and make pull requests right in development pipelines. This cuts the time it takes to find and fix problems from months to days.
But every new technology comes with new risks, and AI is no different. Attackers are already using the same tools that defenders do. Generative AI has made it much easier to make convincing phishing emails, deep-fake voice calls, and automated social-engineering campaigns. In 2024, one criminal group used custom large language models to send out thousands of personalized spear-phishing messages every hour. In some sectors, these messages had open rates of over 70%.
Adversarial machine learning is now a serious threat. Attackers can poison training data, make inputs that trick detectors (evasion attacks), or steal proprietary models by making inference queries. There are now real-world examples of malware that changes how it works so that it stays below the confidence levels of the best endpoint detection systems.
AI-enabled autonomous malware is probably the most worrying new thing. Security researchers have shown worms that can plan multi-stage attacks, choose exploits, and move through networks without or with little help from people. These are mostly still in research labs, but the building blocks are available to everyone, and the gap between proof-of-concept and widespread criminal use is closing quickly.
The dual-use problem also affects defense. If an attacker compromises or changes the detection model itself, whole security stacks can be blinded. This is because AI is too reliant on it. False positives are still a real problem, though. Even a system that is 99.9% accurate can send out thousands of alerts every day in a big company, which brings back the “alert fatigue” problem with new technology.
Even with these risks, the path is clear. Companies that don’t use AI in their cybersecurity programs will fall behind those that do—and the attackers who already have. The future belongs to hybrid defenses that mix human knowledge with machine scale, constant checking of AI outputs, and strict testing of every model against real-world data before it goes into production.
AI doesn’t replace cybersecurity professionals; it makes the best ones better and makes those who don’t adapt useless. The technology is no longer a choice; it is the new battlefield.
