{"id":45780,"date":"2024-03-19T12:02:57","date_gmt":"2024-03-19T12:02:57","guid":{"rendered":"http:\/\/www.developer-tech.com\/\/?p=45780"},"modified":"2024-03-19T12:02:58","modified_gmt":"2024-03-19T12:02:58","slug":"nvidia-genai-rapid-software-vulnerability-detection","status":"publish","type":"post","link":"https:\/\/www.developer-tech.com\/news\/nvidia-genai-rapid-software-vulnerability-detection\/","title":{"rendered":"NVIDIA employs GenAI for rapid software vulnerability detection"},"content":{"rendered":"\n

NVIDIA<\/a> has demonstrated how its generative AI technologies can help to quickly identify and mitigate common vulnerabilities and exposures (CVEs) and other software security risks.<\/p>\n\n\n\n

The NVIDIA NIM<\/a> and NeMo Retriever<\/a> microservices \u2013 along with the Morpheus<\/a> accelerated AI framework \u2013 enable security analysts to detect and mitigate risks in a matter of seconds, a task that previously took hours or even days using traditional methods.<\/p>\n\n\n\n

Traditional cybersecurity methods often involve laborious manual efforts to pinpoint solutions for identified vulnerabilities. However, NVIDIA’s generative AI technologies automate this process, providing quick and actionable CVE risk analysis through large language models (LLMs) and retrieval-augmented generation (RAG). This empowers analysts to make informed decisions swiftly, resembling the role of CEO-like decision-makers in the enterprises of the future.<\/p>\n\n\n\n

The significance of generative AI in cybersecurity is highlighted by recent trends. Last year witnessed a record-high number of reported software security flaws in the CVE public database, underlining the critical need for innovative solutions in this space.<\/p>\n\n\n\n

Gartner predicts that generative AI will play a pivotal role in reducing false-positive rates for application security testing and threat detection by 30 percent by 2027. NVIDIA’s AI Enterprise software platform incorporates these generative AI microservices and Morpheus, delivering unparalleled accuracy comparable to human experts.<\/p>\n\n\n\n

The process of generative AI in cybersecurity involves the use of LLMs and event-driven RAG triggered by the creation of new software packages or the detection of CVEs.<\/p>\n\n\n\n

In a demonstration by NVIDIA, an LLM generates a checklist of tasks to assess software vulnerabilities\u2014followed by AI-powered searches across internal and external data sources to identify necessary safety actions:<\/p>\n\n\n\n

\n