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.

網路安全的RAG結合了大型語言模型與向量檢索的力量，改變了安全團隊訪問與分析網路安全資訊的方式。用戶首先導入如MITRE ATT&CK矩陣、CVE條目與安全公告等文件。框架接著對每個文件生成嵌入向量並存入向量資料庫。當用戶提交查詢時，RAG會檢索最相關的文件片段，傳遞至LLM，並返回精確且富有情境的回應。此方法確保答案基於權威資料來源，降低幻覺現象，同時提升準確性。利用可定制的資料流程與多種嵌入模型及LLM供應者的支援，團隊可調整系統以符合其獨特的威脅情報需求。

Amplifier Security 利用人工智能來為企業提供動態威脅檢測、實時監控和詳細報告。其 AI 驅動的功能使得能夠更快速地識別脆弱性並自動應對潛在的違規行為。這確保了全面的安全管理，增強了組織對持續網路威脅的韌性。

DeployH.ai透過利用先進的AI技術來革命化網絡安全，自動檢測漏洞、分析安全威脅和保護數據。該平台將大量的安全數據轉化為可行的洞察，使用戶能夠實時應對潛在威脅。通過自動分析各種安全報告並改善威脅檢測能力，DeployH.ai幫助組織增強其網絡安全措施，同時減輕工作負擔，提升效率。