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NIX helped the healthcare company streamline cybersecurity task management with an AI-powered solution.
Healthcare
Data Science, Web Development, Cybersecurity
Google Cloud, Vertex AI, LangChain
Our client specializes in developing, manufacturing, and distributing healthcare systems, both devices and software, for people with diabetes. Due to the sensitive nature of patient data and the stringent cybersecurity requirements imposed by regulatory bodies, the company prioritizes data protection and maintains rigorous security protocols like HIPAA, FDA, GDPR, etc.
To safeguard information transfer, each product release undergoes multiple cybersecurity checks, from bend testing to code review. Security architects have to analyze the results and prepare a specific software security report as part of the bureaucracy. This ongoing responsibility prevented them from performing more advanced tasks that required their professional knowledge and reduced their productivity.
To remedy the situation, the client decided to develop a system that would automate the process of creating vulnerability and threat reports and reduce the work of security specialists to just reviewing. The company turned to NIX with this request, choosing us for our proven cybersecurity background and vast experience in healthcare development.
Develop an LLM-based system to autonomously generate cybersecurity reports for medical software and firmware using an internal documentation database and generative AI technologies.
Ensure this system intelligently analyzes identified threats and vulnerabilities and subsequently proposes comprehensive mitigation strategies based on the company’s previous experience.
Our team developed an LLM-based system with a function-driven architecture that automates repetitive documentation tasks and streamlines cybersecurity administrative workflows, helping meet the client’s high data protection requirements. Deployed in the Google Cloud Platform (GCP), this solution consists of a complex multi-agent application integrated and orchestrated in both Vertex AI Agent Builder and LangGraph.
To provide the platform with proper threat modeling capabilities, we integrated it with the IriusRisk API as a key agentic tool. Our team also integrated it with the necessary enterprise-level systems, including the Oracle database as the final storage for reports. This two-way data synchronization united all interconnected operations and ensured the correct extraction and processing of information for generating reports.
To optimize information aggregation from the cybersecurity reporting repository, our team used a combination of advanced technologies, including prompt engineering techniques, the retrieval-augmented generation (RAG) approach, and Vertex AI model capabilities. This enabled precise data extraction, efficient analysis, and accurate report generation.
Prompt Engineering: Domain-specific prompts allowed the AI to understand complex cybersecurity language and regulatory needs, ensuring well-structured reports and actionable mitigation strategies with minimal corrections.
RAG Approach: By combining Google Search grounding with Vertex AI Search, the system accessed both external and internal data sources, delivering reliable, verifiable, and contextually relevant outputs.
Vertex AI Models: These models facilitated rapid data processing, accurate information retrieval, and smooth integration with the company’s systems, ensuring synchronized and reliable workflows.
LLM Module: To provide the system with advanced LLM capabilities, we used the Google Gemini model’s family as a convenient and effective tool for these tasks.
The complex project architecture and high system workload required a Google Kubernetes Engine integration and a custom reports UI, which was developed in Vue.js. This allowed us to implement a sophisticated system with advanced functionality.
To increase the accuracy of the system’s predictions and the quality of the generated reports, we are constantly improving our AI model. Feeding it curated data from Oracle’s database and internal storages and using automated iterative prompt optimization, we instruct the AI to generate predictions on known data and compare results with expected outcomes. This process can be repeated any number of times to further enhance the accuracy of AI outputs.
With this LLM-powered AI Agent system, the client boosted the company’s performance and achieved all of their goals:
1,000+
AI-powered Reports
70%
Less Time Spent per Report
90%
Accuracy of Mitigation Strategies
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