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The New Stack
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How We Built a LangGraph Agent To Prioritize GitOps Vulns
- In Kubernetes environments, managing vulnerabilities can be overwhelming; HAIstings, an AI-powered prioritizer using LangGraph and LangChain, was developed by Stacklok.
- HAIstings helps prioritize vulnerabilities based on severity, infrastructure context, user insights, and evolving understanding through conversation.
- Main components include k8sreport, repo_ingest, vector_db, and memory to gather data, provide context, store files, and maintain conversation history.
- HAIstings uses LangGraph for conversation flow, retrieving data, creating reports, gathering context, and refining assessments based on new information.
- A retrieval-augmented generation (RAG) approach efficiently retrieves relevant files from GitOps repositories for each vulnerable component.
- CodeGate enhances security by redacting secrets and PII, controlling model access, and maintaining a traceable history of interactions with AI models.
- Configuring HAIstings with CodeGate involves updating the LangChain configuration to work seamlessly with the security controls provided.
- The combined system provides context-aware vulnerability prioritization while ensuring strict security measures are in place.
- HAIstings can generate security reports highlighting critical vulnerabilities, providing tailored recommendations for prompt attention.
- Performance considerations emphasize the trade-off between latency and security benefits when utilizing LLMs for vulnerability prioritization.
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