How Generative AI Can Enhance Cybersecurity

How to Use Generative AI in Cybersecurity

Cybersecurity has historically been a dense, data-heavy field. Complex enterprise networks have always demanded slightly more working hours than highly-skilled cybersecurity teams can offer and specialized tooling hasn’t always made the burden lighter.

GenAI’s framework can be applied to far more than language. It thrives in any field with large quantities of data, making it a natural fit for cybersecurity. Despite ongoing concerns around its risks, it’s possible to implement GenAI in a secure, controlled manner – giving cybersecurity teams:

• A rapid
• Approachable
• And highly scalable toolset to support their workflows

Automated Network Traffic Analysis

Network traffic analysis takes pride of place in most cybersecurity programs, as it’s one of the clearest indicators of suspicious or malicious activity.

Functionally, NTA relies on a tight mesh of people and tooling:

• A firewall is deployed and monitored at the network edges
• A log collection and analysis tool monitors network device activities

Since visibility is core to cybersecurity, every single data point matters, from a firewall rule preventing a connection to the volume of traffic passing through each router. All of it must be:

• Monitored
• Interpreted
• Integrated into the team’s broader situational awareness

GenAI security introduces a new approach to network traffic classification by training models directly on unstructured data. Once deployed, an NTA GenAI model:

• Receives real-time network data from sensors and devices
• Classifies this data by traffic protocol or network event (e.g., login attempts)

This alone saves significant time, reducing the need for manual analyst review of each network event. But GenAI doesn’t stop at classification.

Using transformer architecture, the model can:

• Compare current network data to patterns learned during training
• Cross-reference traffic with additional security data, including firewall activity and SIEM (Security Information and Event Management) alerts

This contextual layer of analysis helps reduce false positives, track incidents based on potential attack paths, and classify the true risk level of a security event more accurately. The final stage is how the AI communicates the risk. Instead of flooding teams with isolated alerts (as many segmented tools do), a GenAI-powered NTA solution:

• Provides a context-rich overview of the entire projected attack chain
• Gives cybersecurity teams better clarity for faster, smarter response

Phishing Attack Prevention with AI

Language-focused GenAIs have taken up much of the spotlight in recent years and their application in cybersecurity is just as impactful. Phishing remains one of the weakest and most unpatchable areas of an organization’s attack surface. Time-sensitive, high-pressure messages can easily trick an employee into giving up:

• Passwords
• Sensitive documents
• Financial details

Keyword-based filtering is no longer enough.

Worse still, attackers now use publicly available LLMs to remove the telltale language errors that used to give phishing email attempts away. GenAI offers a new defense mechanism by analyzing incoming messages for intent. If the message aligns with common phishing signals, such as:

• Suspicious document types
• Time-sensitive urgency
• Lack of contextual background

It’s flagged as high-risk and forwarded to the cybersecurity team for review. GenAI can also detect anomalies from compromised accounts, identifying deviations from a sender’s usual tone or communication style, something traditional filters miss.

And because GenAI’s neural networks evolve over time, it continually refines its accuracy, which reduces false positives and improves cyber threat detection with every cycle.

AI Attack Simulation

Since cybersecurity teams and tools are required to operate and catch attacks even in worst-case scenarios, best practice often demands regular and realistic penetration tests.

This once relied on a red team manually conducting simulated attacks against an application or wider network to assess its defenses’ real capabilities. GenAI now makes attack simulations, and predictive security analytics as a whole, more accessible.

• Just as LLMs can generate contextually relevant text, Generative Adversarial Networks (GANs) can generate synthetic traffic data that mimics real-world attack patterns
• Security teams can simulate cyberattacks like malware variants, phishing attempts, and denial-of-service attacks, then monitor success rates of existing tooling
• GAN-based training also enables more responsive, AI-driven malware analysis

Because synthetic network traffic and protocol sequences must be as high-fidelity as possible, GANs trained on real network and attack data allow for:

• Safe, realistic attacks
• Simulations tailored to an organization’s unique security setup
• Effective stress-testing without the risks of real compromise