Security AI Training Data & Annotation

Security

Our data annotation services cover the full spectrum of security LLM applications, enabling threat detection, risk assessment, and personalized cybersecurity solutions.

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LLM Data Types for the Security Industry

Malware Code Annotation

Marks specific code fragments as malicious by identifying malicious functions, API calls, or obfuscated areas. This helps LLM learn to automatically classify new malware samples and identify threats in source code.

Incident Report Annotation

Extracts key entities (IP addresses, hashes, indicators of compromise (IOCs), systems involved) from cyberattack reports, protocols, and security logs. Allows LLM to generate accurate threat reports and automate incident response.

Phishing & Spam Annotation

Marks linguistic and structural characteristics of emails or messages that indicate phishing, social engineering, or fraud. Teaches LLM to recognize and filter dangerous content in real time.

Vulnerability Annotation

Details vulnerability descriptions, attack vector labeling, and exploit impact in text databases. Teaches LLM to prioritize vulnerability remediation.

Security Policy Annotation

Tags and categorizes requirements, rules, and regulations in compliance documents. Allows LLM to generate appropriate recommendations or verify system configuration for compliance.

Malicious Command Annotation

Tags commands used in command lines or scripts as suspicious or malicious. This helps LLM in intrusion detection systems identify anomalous or dangerous user actions.

LLM Data Services for Security

Domain Data Collection and Cleaning

Generation, collection, and standardization of large amounts of specialized data for model training.

Specialized Data Annotation

Engaging experts to label data to transform input into structured training material.

Model Fine-Tuning

Adapting generic LLMs to client-specific data so that the model better understands industry terminology and context.

Accuracy and Hallucination Audit

Systematically checking the model’s generated responses for factual inaccuracy and fabricated information (hallucinations) to ensure reliability.

Prompt Engineering

Development and optimization of prompts to maximize the quality and predictability of the model’s output.

LLM Monitoring and Support

Continuous monitoring of model performance in a production environment, tracking data drift and using feedback for regular updates.

LLM Use Cases in Security

Threat Detection and Analysis

LLMs process large volumes of security logs, telemetry, and system events to identify potential threats and vulnerabilities across complex digital environments. They help automate the recognition of malicious patterns in complex systems. In combination with embedded AI deployed on endpoints or network devices, preliminary threat detection can occur locally and in real time, reducing response latency. As a result, LLMs can:

Analyze malware behavior and signatures
Detect suspicious network activity
Prioritize critical threats

Prevent phishing and fraud

By analyzing emails, messages, voice transcripts, and web content, LLMs identify phishing attempts and social engineering attacks using linguistic cues and contextual inconsistencies. Embedded AI on user devices or gateways can filter obvious threats instantly, while LLMs perform deeper semantic analysis to uncover sophisticated scams. As a result, LLMs can:

Classify phishing emails and malicious links
Detect fake websites and scams
Automatically alert users

Vulnerability management

LLMs summarize and interpret vulnerability reports, helping security teams respond faster. Annotated data allows models to associate CVEs with affected systems and remediation strategies. As a result, LLMs can:

Prioritize patches and software updates
Track vulnerabilities by asset
Generate remediation recommendations

Detect insider threats

LLMs analyze user behavior and access logs to detect anomalous activities that may indicate insider threats. Annotated data helps models distinguish normal from suspicious activities. When combined with embedded AI for on-device monitoring, sensitive behavioral data can be partially processed locally, improving privacy while maintaining detection accuracy. As a result, LLMs can:

Monitor suspicious logins and file access
Detect privilege abuse
Warn about potential information leaks

Incident response and reporting

LLMs process incident reports, summarize violations, causes, and corrective actions. Annotated examples help models generate actionable recommendations for rapid response. In cyber-physical systems environments, physical AI extends these capabilities by analyzing data from sensors, cameras, industrial controllers, and other physical assets. As a result, LLMs can:

Synthesize security incidents
Analyze causes and effects
Remediation recommendations

Aggregate threat intelligence

LLMs collect and analyze data from various threat sources to provide a complete overview of the security situation. Annotated data provides accurate highlighting of key details such as actors, tools, and tactics. Physical AI systems can incorporate intelligence related to attacks on physical infrastructure, such as industrial sabotage, sensor manipulation, or unauthorized access to facilities. As a result, LLMs can:

Correlate threat indicators from various sources
Profile attackers and attack methods
Automated threat reports

FAQ

Why is data annotation important for LLMs used in threat detection systems (TDS)?

Well-annotated data, such as observed malicious commands or IOCs, is the basis for training a model to distinguish between legitimate and malicious traffic. This allows LLMs to accurately identify new, previously unknown attack vectors and improve system performance.

What are the main challenges in cybersecurity data annotation?

Challenges include the rapid evolution of threats, the need for deep knowledge from expert annotators, and the issue of ensuring the confidentiality of sensitive data during annotation.

How does annotation help LLMs combat phishing?

Annotators mark key linguistic and structural elements of phishing messages, including spoofed URLs, urgency requirements, and grammatical errors. This trains LLMs to identify these fraud markers even when the message text is constantly changing.

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Reviews
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"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...

"Great service, fair price"

Ability to accommodate different and not consistent workflows.
Ability to scale up as well as scale down.
All the data was in the custom format that...

"Awesome Labeling for ML"

I have worked with Keymakr for about 2 years on several segmentation tasks.
They always provide excellent edge alignment, consistency, and speed...