How AI Is Used in Cybersecurity: Top Examples

Is your security team stretched thin and still missing threats?

Every day, hackers get smarter, and the data at risk grows. A recent study found that the average cost of a data breach is more than 4 million dollars. Another survey shows that many security teams see thousands of alerts every week and only a fraction get handled. Those numbers make it clear why AI in cybersecurity is not optional anymore. 

This post explains how AI is used in cybersecurity, gives clear examples of AI in cybersecurity, and shows simple ways teams can use AI today.

What is AI in Cybersecurity

First, a short, clear definition. AI in cybersecurity means using machine learning, pattern recognition, and automation to spot, stop, and recover from cyber threats. It watches data and behavior, learns what normal looks like, and flags anomalies. It also helps with triage and response, so humans can focus on the hard problems.

Think of AI as the assistant that never sleeps. It reads logs, watches network traffic, and looks for small signals that usually mean trouble. It does not replace people. Instead, it makes humans much more effective.

Now that we have the basics, let’s look at real examples people can understand.

How is AI used in cybersecurity for threat detection

One of the first and clearest uses is detection. Traditional rules miss a lot and give many false positives. AI learns from real data and spots patterns that rules miss.

Examples

• Behavioral profiling. AI learns a user’s normal laptop use. If that user suddenly downloads thousands of files at 2 am, AI flags it.
  
• Anomaly detection in network traffic. AI watches flow patterns and finds odd connections that may be command and control.
  
• Email analysis. AI checks email text, links, and headers to catch phishing before users click.
  
• Log aggregation. AI reads thousands of logs and finds chains of events that add up to an intrusion.

These are not theoretical. Teams using anomaly detection reduce mean time to detect by days. That matters in real dollars.

Detection is just the start. AI also helps stop attacks in real time.

Top Examples of AI in cybersecurity for prevention and response

When a threat is detected, AI can help stop it fast. It can also help responders with the next steps.

Prevention and response examples

• Automated blocking at the edge. When AI sees bad behavior, it can trigger network rules to block the suspect host.
  
• Endpoint isolation. If a laptop shows ransomware behavior, AI can isolate it from the network automatically.
  
• Playbook automation. AI runs the steps of an incident playbook, gathers logs, and prepares a report.
  
• Risk scoring. AI gives each alert a risk score so analysts know which things to act on first.
  

Another simple example is the modern web application firewall. When combined with AI, it does more than block known attacks. It learns how normal requests look and can block odd requests that look like an exploit. That reduces the noise and prevents new attack patterns.

AI tools are also used inside applications and services to stop more advanced techniques.

How AI is used in cybersecurity for fraud and identity protection

Identity theft and fraud are the top losses for many businesses. AI helps here in clear ways.

Identity and fraud examples

• Transaction monitoring. AI models watch purchase patterns and flag unusual buys that may be fraud.
  
• Adaptive authentication. AI decides when to ask for extra verification based on risk, not on rigid rules.
  
• Account takeover detection. AI spots small changes in behavior that hint an account was compromised.
  
• Synthetic identity detection. AI finds fake profiles made from mixed real and fake data.
  

These examples reduce friction for real users while making fraud much harder. That balance is why many banks and services use AI today.

But using AI brings new types of risk that we must understand and manage.

Risks and weak spots: AI can be attacked, too

AI is powerful, but it is not perfect. Attackers try to trick or poison models. There are clear risks, and we must be practical about them.

Key AI risks

• Model poisoning. If attackers can change the data AI learns from, they can make it miss attacks.
  
• Evasion. Some clever attackers craft payloads that confuse detection models.
  
• Supply chain attacks. AI tools and models have dependencies that can be abused.
  
• Prompt manipulation. With systems that use natural language prompts, attackers try prompt injection to alter behavior. If you use LLMs in security workflows, you must test for prompt injection attack vectors.
  
• Generative model abuse. Attackers may use LLMs to write better phishing emails or to find code vulnerabilities. That makes generative AI security a must-have consideration now.

Knowing risks helps us design defenses that are realistic and effective.

Examples of AI in cybersecurity for monitoring cloud and containers

Cloud systems need continuous monitoring. AI scales where humans cannot, and it fits well with cloud native patterns.

Cloud and container examples

• Runtime protection. AI watches container behavior and spots unusual system calls or file changes.
  
• Configuration drift detection. AI notices when a cloud role or permission changes in a risky way.
  
• Data exfiltration monitoring. AI watches outbound flows and can stop slow leaks that humans miss.
  
• Resource anomaly detection. AI flags unusual compute or network spikes that may mean misuse.

Cloud teams that add these AI layers find issues early and avoid big outages or data leaks.

Now, let’s talk about how teams adopt AI without breaking things.

Practical steps for teams that want to implement AI in security

Adopting AI can feel daunting. Start small and focus on wins.

Step-by-step plan

1. Map your problem. Pick one clear use case, like phishing detection or endpoint isolation.
   
2. Gather good data. AI needs labeled examples and clean logs to learn well.
   
3. Run pilot projects. Test models in monitoring mode before you let them act automatically.
   
4. Add human review. Keep analysts in the loop and let them correct the model.
   
5. Measure results. Track reduced alerts, time saved, and incidents avoided.
   
6. Scale gradually. Once a pilot works, expand to similar use cases.

Keep models auditable and keep logs of decisions. That helps with compliance and with improving the AI over time.

People often ask about vendors and services. Here is a simple guide.

Buying vs building: AI security tools and services

You can build models in-house, or you can buy tools. Both have pros and cons.

Build pros and cons

• Pros: tailored to your data and context.
  
• Cons: needs skilled people and time, risk of wrong training data.

Buy pros and cons

• Pros: faster deployment, vendor expertise, and maintained pipelines.
  
• Cons: may not fit your exact needs, cost can be high.

A balanced approach is common. Use vendor tools for broad coverage and build specialty models for unique needs. When you evaluate vendors, ask for transparency on how models were trained and how false positives are handled.

Before we finish, let’s look at a few case studies that show these ideas in action

Short case studies: Clear examples of AI in cybersecurity

Case study 1 ‒ Retail company: Mastercard & Riskified / TickPick

TickPick, a ticket marketplace, used Riskified’s AI-powered system called Adaptive Checkout. The tool looks at orders in real time and decides if something looks risky or needs extra verification. In the first three months after using it, TickPick reclaimed about US$3 million in revenue from orders that would have been falsely declined as fraud.

Case study 2 ‒ Financial services / Retail fraud: Yapı Kredi bank

Yapı Kredi in Turkey used FICO’s Falcon system to fight credit card fraud. Over seven years, they cut fraud losses by about 98.7% using AI-driven monitoring, predictive models, and alerts.

Case study 3 ‒ Bank/credit union example: A US credit union working with Alkami, BioCatch & Appgate

A US credit union with about 100,000 members used layered fraud detection tools. They used behavioral biometrics (from BioCatch) plus transaction monitoring (via Appgate). After implementation, they lowered account takeover losses by about US$211,000 in six months.

Case study 4 ‒ Retail operational / loss prevention: Kroger & Everseen

The supermarket chain Kroger used AI-enabled cameras via Everseen and edge computing servers (Lenovo). The system watched self-checkout and store checkouts to spot when customers didn’t scan items or moved barcodes fraudulently. That cut shrinkage and internal theft significantly. 

How to pick AI security partners

When you choose a partner, look for these things

• Clear explanations of their models and training data.
  
• Support for integrating with your logs and cloud provider.
  
• Human in the loop features so analysts can correct the AI.
  
• Strong incident response playbooks and automation.
  
• Ability to run in your environment or in a way that keeps your data private.
  

If you want a quick test, ask for a free trial and try to reproduce a simple detection using your real logs. That shows you how the tool performs on your data.

Closing

You now have a practical view of how AI is used in cybersecurity and clear examples of AI in cybersecurity you can act on. Start small, measure results, and keep improving. With sensible design and human oversight, AI will make your security better and faster. To cleverly use AI in cybersecurity, you should reach out to AI security services.