Mentioned in 13 AI use cases across 2 industries
This is like putting a smart security guard in your cloud data center who never sleeps, learns what “normal” looks like, and automatically flags or blocks suspicious behavior before it turns into a breach.
This is like giving your company’s security cameras and fire alarms a brain that learns. Instead of waiting for a fixed list of ‘bad things’ to happen, machine learning watches all activity on your network, learns what “normal” looks like, and then flags and blocks suspicious behavior in real time—often before humans would even notice.
This is a research survey that acts like a “buyers guide plus textbook” for using AI to catch hackers. It reviews how different machine‑learning and deep‑learning techniques can watch network and system traffic, learn what normal looks like, and automatically flag or block suspicious behavior in real time.
Think of your company’s security center as an airport control tower. Traditional tools watch planes (devices, users, emails). This use of AI threat hunting in Defender XDR adds new radar that also watches the AI copilots and automations you’ve deployed—so if someone hijacks your AI assistant or uses it to sneak in malware, security can see and stop it.
Think of your company’s network as a city. AI gives both the police and the criminals super-powered binoculars and autopilot cars. Defenders use AI to spot unusual behavior and block attacks faster than humans can. Hackers use AI to scan for weak doors, write convincing scam messages, and automate break‑ins at scale.
Think of this as turning today’s security analysts into ‘AI-augmented guardians’: people who use smart tools that can spot cyberattacks much faster than humans, while also learning how to control and question those tools so they don’t make dangerous mistakes.
Think of AIOps for next‑generation firewalls as a smart co‑pilot for your network security team. It constantly watches all your firewalls, spots issues before they break things, suggests the safest settings, and automatically tunes configurations so your security stays strong without your team manually chasing every alert.
This is about using smart software that learns from patterns in network traffic and user behavior to spot hackers and suspicious activity much faster than human teams or rule-based tools can, and then automatically block or contain threats before they spread.
Think of this as a smart operations co‑pilot that constantly watches how your apps and networks feel to end users, spots problems before people complain, and suggests (or triggers) fixes automatically.
Think of a Security Operations Center as an airport control tower watching thousands of planes (devices, users, apps) at once. Traditional tools show you every single radar blip and alarm; humans get overwhelmed and miss real threats. AI- and ML-powered SIEM act like an assistant that learns normal flight patterns, filters out the noise, and flags only the suspicious flights that may be hijacked — and often does it in real time.
This is like giving your security team an AI co-pilot that watches everything in your environment in real time, spots attacker behavior (including AI-generated attacks) faster than humans can, and automatically helps block and contain those attacks before they spread.
Imagine your entire IT and network environment has a 24/7 “air traffic controller” that watches every signal from every system, spots early warning signs of trouble, and automatically re-routes traffic or fixes issues before users even notice. That’s what AIOps does for IT and security operations.
Think of this as a 24/7 security guard for your computers and networks. It continuously watches what’s happening, looks for signs of break‑ins or suspicious behavior, and alerts your team before a small issue turns into a major cyber incident.