Just a few years ago, artificial intelligence in cybersecurity was mostly discussed in theory. Today, it’s a practical, everyday tool. It’s used wherever there’s a need to gather information faster, understand code more easily, or spot unusual behaviour in a system before it’s missed. In many cases, AI simply saves time and removes repetitive manual work.
This article brings together different approaches — from experimental solutions to tools already used in real-world projects. Some are suited for pentesting and red team tasks, others for event analysis and infrastructure defence. This isn’t a look at “future technologies”, but a curated selection of tools that already work in practice and genuinely help get the job done.
A Palo Alto Networks solution that focuses on behaviour rather than signatures alone. The platform analyses activity across the system and detects things that are hard to catch with traditional tools, such as fileless attacks, unusual event chains, and suspicious credential activity. It’s most commonly used in large environments where understanding the overall picture matters more than individual incidents.
AutoSploit combines Shodan and Metasploit, and with additional tuning can operate in a much smarter way. With AI, it can be configured to identify which targets look more interesting and automatically select appropriate attack scenarios. It’s more commonly used in research and lab environments than in real-world networks.
BloodHound is well known to anyone working with Active Directory. With the addition of AI-driven features, it has become even more powerful. AI helps uncover non-obvious privilege escalation paths and complex access chains that are difficult to spot through manual graph analysis.
Elastic Stack includes security modules that leverage machine learning. They analyse logs and highlight anomalies such as unusual logins at night, brute-force attempts, and atypical data activity. The key advantage is flexibility — everything can be tailored to your environment instead of relying on rigid, one-size-fits-all patterns.
This tool is useful when you sense that “something isn’t right” in the network, but there are no obvious signs. AI-Hunter is good at detecting quiet activity — beaconing, slow lateral movement, and unusual traffic patterns. It’s often used alongside traditional defences because it spots things that typically slip past firewalls.
An AI assistant for security professionals integrated into the Microsoft ecosystem. It helps analysts triage alerts faster, summarise logs, and generate queries or detection rules. Essentially, it removes part of the routine workload from analysts during incident response.
