AI Automation in Cyber Security

How is AI Being Implemented in Cyber Security?

AI security is already immensely well-established: AI is well-positioned to build endpoint, network, and user behavior datapoints into their broader patterns. But, Generative AI’s integration with security processes is more mature than ever, leading to four key fields of AI integration:

• Network analysis
• Malware detection
• Phishing detection
• Analyst workflow prioritization

1. Network-Level Data Analysis

Machine learning in security is able to ingest and analyze network-level data by collecting massive volumes of traffic from various network components, such as:

• Routers
• Switches
• Pare-feux
• Endpoint devices

This data can include packet headers, flow records, logs, and even full packet captures, depending on the depth of inspection required.

Once ingested, the data is preprocessed: timestamps are normalized, irrelevant noise is filtered out, and relevant features are extracted – such as protocol type, packet size, IP addresses, port numbers, and communication frequency.

This structured dataset is then fed into corresponding machine learning models.

Supervised Models

Supervised models rely on historical datasets that classify types of network traffic as benign or malicious.

For instance, a common DDoS attack relies on an abnormally high volume of SYN packets, which all target the same server from many IP addresses at one time. Supervised AI can identify not just that an attack is underway, but label an attack according to which preset pattern it matches with.

Unsupervised Models

Unsupervised learning models work a little differently: these aren’t trained to spot specific attacks, but instead build a model of day-to-day behavior from the target network itself.

Rather than pre-set attack patterns, this form of AI identifies any network anomaly by spotting deviations from each network device’s pre-established patterns. As connections are established between devices and servers, the AI model continually assesses one’s level of risk.

This allows it to spot malicious network behavior even within encrypted connections and highly obfuscated attacks, since it’s trained directly off the enterprise’s own network.

By learning over time, AI systems:

• Improve their detection capabilities
• Adapt to changing security threats
• Reduce false positives

(making them crucial in managing large-scale, high-speed network environments.)

2. Endpoint-Based Malware Detection

Malware detection previously relied on signatures: this would involve a database of previously known malware strains, each having their own signatures that an Antivirus software would monitor for.

AI enhances this by continuously collecting telemetry data from endpoint devices like:

• Laptops
• Mobile phones
• Routers

This data spans file metadata, process behavior, registry changes, memory usage, system calls, and user activity patterns. Once collected, the granular activity data is built into a broader behavior profile for each process or application.

Key features – like hash values of executables, parent-child process hierarchies, privilege escalation attempts, or anomalous API calls – are extracted to represent behavior in a machine-readable format.

This structured behavioral dataset is then passed into AI models trained on large corpora of known malware samples and benign software.

Supervised Models

Supervised learning algorithms can classify whether a process exhibits patterns consistent with

• logiciel rançonneur
• Keyloggers
• Droppers
• Fileless malware

For example, a PowerShell script that spawns a series of encoded commands and attempts lateral movement may match patterns typical of fileless attacks – despite no known signature being triggered.

Unsupervised Models

Unsupervised models, on the other hand, identify outliers in behavior – flagging novel or zero-day threats that deviate sharply from the learned baseline. Combined with threat intelligence feeds and feedback loops from incident response teams, AI-driven endpoint protection systems adapt quickly to emerging attack techniques.

3. Phishing Detection

Natural Language Processing (NLP) aids in phishing detection by analyzing the textual content of emails, messages, and websites to identify the cues and patterns used in social engineering attacks.

NLP allows for the raw text to be extracted from a message.

From there, it identifies all linguistic features involved – word frequency, sentence structure, tone, and named entities, such as:

• Brands
• Senders
• Recipients
• Login prompts
• Financial terms

These are developed into a picture of the message’s legitimacy: set phrases like “validate your credentials” or slightly-mistyped sender credentials drastically increase this score, while established security team members sending a regular update keeps the score minimal.

To achieve this, an AI-powered phishing tool will use both supervised and unsupervised models together.

Labeled datasets of AI phishing messages allow for the rapid identification of telltale characteristics, like impersonation, while transformer-based NLP models can then dig deeper. These behavioral context clues allow for subtle manipulations in text, such as fake vendor notifications, to be spotted.

They also analyze embedded links and their anchor text, flagging inconsistencies between displayed text and destination URLs. Combined with metadata analysis (sender reputation, IP geolocation, and header anomalies), NLP-driven phishing detection engines improve both speed and accuracy.

4. Analyst-Level Security Incident Prioritization

Alongside on-the-ground threat detection, AI technology is playing a critical role in assisting security analysts with their triage and incident responses. By ingesting data from SIEM systems, correlating alerts, and learning from past analyst decisions, models are able to distinguish between genuine cyber threats and false positives.

Rather than simply discarding low-confidence alerts, AI assesses the likelihood that each event represents a serious security incident. It assigns a “threat probability” score, often visualized through color-coded indicators or risk meters.

This helps analysts quickly zero in on the most urgent issues. Here’s an example of how that looks:

• An alert that’s linked to lateral movement attempts across an organization’s critical infrastructure might receive a high score, prompting immediate review
• A benign port scan is automatically deprioritized

This intelligence enables a smarter workflow: AI filters the noise, clusters related events, and surfaces high-risk anomalies, significantly reducing the volume of alerts analysts must manually investigate.

These enriched insights can be pushed even further toward automated incident response.

This sees a threat trigger automated responses, such as isolating an endpoint, disabling compromised accounts, or blocking suspicious IPs, before human intervention is even required.

Deploy AI Securely with Check Point GenAI Security

While AI security automation presents several key opportunities for security professionals, cybersecurity isn’t the only field in which AI is being deployed across organizations.

Check Point’s 2025 AI Security Report discovered that even cybercriminals are relying on GenAI to develop malware and execute phishing attacks. Keeping track of which AI tools are being used both internally and against your organization demands a suitably forward-thinking security tool.

Conventional data loss prevention (DLP) tools operate off predefined keywords, making them incapable of understanding the context around unstructured data typical of conversational AI prompts. Check Point’s GenAI Security automatically discovers the AI tools that are used across your organization, alongside their intended purpose and associated risk factors.