Comprehensive サイバー脅威分析 Tools for Every Need

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サイバー脅威分析

  • Amplifier Security safeguards digital environments through AI-driven threat detection and real-time monitoring.
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    What is Amplifier Security?
    Amplifier Security leverages artificial intelligence to provide dynamic threat detection, real-time monitoring, and detailed reporting for businesses. Its AI-driven features allow for faster identification of vulnerabilities and automatic responses to potential breaches. This ensures comprehensive security management, enhancing organizational resilience against persistent cyber threats.
  • Offensive Graphs uses AI to automatically generate attack path graphs from network data, empowering security teams with clear visualization.
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    What is Offensive Graphs?
    Offensive Graphs leverages advanced machine learning algorithms to seamlessly ingest diverse network data sources such as firewall rules, Active Directory configurations, cloud assets, and vulnerability scanner outputs. It automatically constructs comprehensive attack graphs that reveal the most effective lateral movement and privilege escalation paths an adversary might exploit. Users can interactively explore these graphs in a user-friendly web interface, apply filters by risk level or asset criticality, and drill down into detailed risk factors. The platform also prioritizes remediation tasks based on aggregated threat scores and generates customizable reports to support compliance and incident response. By automating complex threat modeling, Offensive Graphs significantly reduces manual effort while enhancing the accuracy and coverage of security assessments.
  • An open-source RAG-based AI tool enabling LLM-driven Q&A over cybersecurity datasets for contextual threat insights.
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    What is RAG for Cybersecurity?
    RAG for Cybersecurity combines the power of large language models with vector-based retrieval to transform how security teams access and analyze cybersecurity information. Users begin by ingesting documents such as MITRE ATT&CK matrices, CVE entries, and security advisories. The framework then generates embeddings for each document and stores them in a vector database. When a user submits a query, RAG retrieves the most relevant document chunks, passes them to the LLM, and returns precise, context-rich responses. This approach ensures answers are grounded in authoritative sources, reducing hallucinations while improving accuracy. With customizable data pipelines and support for multiple embeddings and LLM providers, teams can tailor the system to their unique threat intelligence needs.
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