Tailored security guidance for your specific use case or industry. Each solution includes risk analysis, policy templates, code examples, and compliance mapping relevant to your domain.
AI agent security isn't one-size-fits-all. A RAG pipeline pulling documents from a vector database faces different threats than a customer support agent handling live conversations, which faces different threats than a coding agent with file system access. The right security approach depends on your agent's architecture, the data it handles, and your compliance requirements.
Each solution below includes a risk analysis specific to your use case or industry, a ready-to-use YAML policy template, integration code, and compliance mapping where applicable. Start with your use case for technical guidance, or your industry for compliance-focused recommendations.
Protect RAG pipelines from document poisoning, retrieval manipulation, and indirect prompt injection. Runtime security for LangChain, LlamaIndex, and custom retrieval-augmented generation systems.
Secure AI-powered customer support agents against prompt injection, PII leakage, and unauthorized actions. Enforce compliance for support bots handling sensitive customer data.
Secure AI coding agents against malicious code execution, MCP tool manipulation, and supply chain attacks. Runtime protection for Copilot, Cursor, and custom coding assistants.
Protect data analysis agents from SQL injection, unauthorized data access, and exfiltration. Runtime security for AI agents with database access and analytical tool use.
Secure autonomous AI agents executing multi-step workflows. Prevent cascading attacks, runaway execution, and unauthorized actions in agent loops, CrewAI, and AutoGPT-style systems.
Secure MCP (Model Context Protocol) tool servers and client integrations against supply chain attacks, tool manipulation, and cross-server injection. Runtime protection for MCP ecosystems.
Protect AI sales and outreach agents from PII mishandling, email automation abuse, and data compliance violations. Runtime security for CRM-connected AI agents.
Secure AI agents handling financial data, transactions, and advisory services. SOC 2, PCI DSS, and regulatory compliance for AI-powered financial applications.
HIPAA-compliant AI agent security for healthcare applications. Protect PHI, enforce clinical data access controls, and maintain audit trails for AI agents in healthcare environments.
Protect AI agents handling legal documents, case files, and privileged communications. Safeguard attorney-client privilege, prevent document confidentiality breaches, and ensure ethical compliance.
If your agents handle regulated data (healthcare, financial, legal), yes. Industry solutions include compliance-specific policies — for example, healthcare solutions enforce HIPAA-aligned PII redaction and audit logging, while financial solutions include SOX-compliant monitoring. Generic security covers the technical threats but may miss regulatory requirements.
Start with the use-case solution that matches your agent's primary function (RAG pipelines, customer support, coding agents, or autonomous agents). Each solution page includes a framework-specific code example. If you're using LangChain, OpenAI, or CrewAI, the integration guides under /integrations have step-by-step setup instructions.
Yes — and you should. Most production agents span multiple categories. A customer support agent that uses RAG retrieval should combine the customer support solution (conversation-level policies) with the RAG pipeline solution (document scanning). Rune's policy engine lets you layer multiple YAML policy files.
Most solutions can be implemented in under 10 minutes. The code changes are minimal — typically one pip install and a 3-line wrapper around your existing agent. The policy template can be used as-is or customized. The bulk of the work is deciding your risk tolerance and tuning alert thresholds after deployment.
Add runtime security in under 5 minutes. Free tier includes 10,000 events per month.
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