DBAPPSecurity Threat Intelligence Center

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Anheng Threat Intelligence Center has detailed its threat intelligence framework, anchored by the Hengnao AI engine, to support automated threat discovery, multi-source data processing, and proactive security defense operations.

According to the center’s system specifications, the intelligence framework integrates billions of indicators of compromise (IOCs) and uses artificial intelligence to process global security data. The platform incorporates multi-source threat collection, advanced persistent threat (APT) tracking, and large language model analytics to manage enterprise attack surfaces and trace malicious actors.

Core Threat Intelligence Metrics and Detection Capacities

The Hengnao AI intelligence architecture processes massive datasets to identify malicious infrastructure across multiple vectors. System documentation outlines specific scale parameters for its core detection capabilities:

• Intrusion Detection IP Intelligence: More than 11B+ real-time monitoring points.
• Compromise Detection Intelligence: Over 312M+ precise identification entries.
• File Hash Samples: More than 10B+ malicious code detection records.
• Malicious URLs: Over 5000M+ risk identification items.
• Vulnerability Data: More than 35K+ risk warning entries.
• Threat Organizations: Over 600 Tactics, Techniques, and Procedures (TTP) attack matrices.
• Internet Assets: More than 190B+ exposure point checks.
• IP and Domain Data: Over 100B+ tracking and attribution data points.

AI-Driven Intelligence Production and Governance Workflow

Anheng structures its threat intelligence production through an automated, multi-stage pipeline designed to handle complex data environments. The workflow consists of five distinct phases:

1. Data Collection: Uses AI-powered smart crawlers to harvest surface and deep-web threat data automatically. Large language models provide multi-language translation capabilities to process global threat feeds.
2. Data Governance: Employs text summarization models to extract critical insights from large volumes of data alongside topic recognition technology to pinpoint prominent threat subjects.
3. Threat Analysis: Executes automated malware analysis to detect potential threats and applies large language model behavioral analysis to interpret attacker methodologies.
4. Intelligence Production: Utilizes AI threat scoring models for automated qualification and risk assessment while identifying threat actor families and organizational affiliations.
5. Association Aggregation: Builds global threat relationship networks via AI knowledge graphs and incorporates Retrieval-Augmented Generation (RAG) technology to retrieve threat context efficiently.

Integration and Open Capabilities

To support security operations centers, the platform provides standardized integration paths. The system offers RESTful APIs supporting IP, domain, URL, and file hash queries for integration with Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms.

Additionally, the center supports Model Context Protocol (MCP) services. This allows AI clients—including Claude Desktop, Cursor, and Kiro—to execute standardized threat intelligence queries directly through compatible protocols.

Security Tools and Analysis Features

Operators can access cloud sandbox environments to submit files and URLs for dynamic behavioral analysis. The platform also includes security scanning tools designed to evaluate AI agent skill packages via file uploads and URL submissions, checking for malicious code and vulnerabilities before deployment.