AI incident classification: AI applied to security and incident management

Introduction

The management of security incidents is one of the most complex and critical activities for any IT organization. Every day, systems and applications generate an enormous amount of logs, alerts, and notifications. Being able to distinguish between a false positive and a real threat is an ongoing challenge. This is where AI incident classification comes into play, which is the application of artificial intelligence to automatically classify, correlate, and prioritize incidents.

Thanks to machine learning and NLP (Natural Language Processing) techniques, AI can read, understand, and categorize security events with speed and accuracy, enhancing the response capability of the SOC (Security Operations Center) team. This is one of the most advanced applications of sviluppo-AI in the field of cybersecurity.

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What is incident classification and how AI enhances it

The incident classification is the process that allows for the automatic assignment of a category, priority, and risk level to a security event. Traditionally, this work was performed by SOC analysts, who manually interpreted logs and decided whether an event was an unauthorized access attempt, malware, a configuration vulnerability, or a network anomaly.

With AI, this process is automated and made adaptive. The algorithms analyze the content of the logs, correlate events with historical data, and dynamically update the classification models. This way, it is possible to:

• Reduce triage and analysis times.
• Identify correlations between seemingly independent events.
• Reduce false positives and improve response prioritization.

A well-trained AI incident classification engine is capable of continuously learning from new incidents, updating its decision model based on the business context.

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The Importance of Incident Logging

The incident log is the operational heart of security management. It contains information on every event: date, type of incident, involved assets, corrective actions. Automating the compilation and updating of this log through AI means having:

• Complete and real-time updated traceability.
• Consistent history with corporate security KPIs.
• Alignment with regulations such as DORA and NIS2, which require documented evidence of security incidents.

A technical documentation AI system (see AI for technical documentation) can be integrated to automatically generate periodic reports or incident changelogs.

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Practical Example: AI Classification in a SOC

Imagine a SOC that handles thousands of daily events. With an incident classification system based on AI:

• Logs are collected by SIEM (e.g., Splunk, Sentinel, QRadar).
• An NLP module extracts key entities (IP, users, timestamp, severity).
• A ML model assesses similarity with previous incidents and assigns a category (e.g., phishing, brute force, data exfiltration).
• The results are sent to the incident log for automatic auditing.

Here is a simplified example in Python:

from openai import OpenAI
client = OpenAI(api_key="API_KEY")

incident_log = "Suspicious login from unknown IP 185.123.44.2 detected by firewall at 03:42."

prompt = f"Classifica il seguente evento di sicurezza: {incident_log}. Indica tipo di attacco, priorità e azione suggerita."

response = client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": prompt}]
)

print(response.choices[0].message.content)

This approach can be integrated into SIEM or SOAR pipelines, providing a higher level of automation in incident triage.

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Integration with DevSecOps and Incident Management

AI is not limited to the SOC: it can be integrated into DevSecOps workflows, where incident management is part of the software lifecycle. In this context:

• Security incidents related to code are automatically classified and linked to specific commits or branches.
• The incident log is consistently populated with CI/CD pipelines.
• Information is shared in real-time among development, security, and compliance teams.

This ensures continuous traceability between the source code and the vulnerabilities encountered in production, improving operational resilience in the context of DORA/NIS2.

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Benefits and Limitations of the AI-based Approach

The benefits of AI in incident classification are evident:

• Speed: reduction of triage times.
• Accuracy: classification based on context and history.
• Scalability: automatic management of thousands of events per day.
• Compliance: support for regulatory documentation.

But there are also limits:

• Need for high-quality labeled datasets for training.
• Algorithmic bias if the data does not represent all cases.
• Human supervision still necessary for critical cases.

As with every AI-development process, success depends on governance and constant monitoring.

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Consulting and Implementation

As an IT consultant, I support companies in the design and implementation of incident classification solutions based on AI. The services include:

• Integration with existing SIEM platforms.
• Creation and maintenance of the automated incident log.
• Training of custom models for business context.
• Validation of compliance according to DORA/NIS2.

Do you want to understand how to introduce automatic classification into your security process? Check out the complete guide to AI development or contact me for a personalized consultation.

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FAQ

What is incident classification?
It is the automated process of categorizing and prioritizing security events based on AI and machine learning.

What is meant by incident log?
It is a structured archive that documents all security incidents, with details on the actions taken and results.

How to implement incident management with AI? Through the integration of NLP models into SIEM/SOAR workflows and DevSecOps pipelines, enhancing the speed and quality of response.

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