Solutions
A RAG pipeline faces different attacks than a customer-support agent, which faces different attacks than a coding agent with file-system access. Start with the use case that matches your shape, or jump to industry for compliance-first guidance.
Protect RAG pipelines from document poisoning, retrieval manipulation, and indirect prompt injection. Runtime security for LangChain, LlamaIndex, and custom retrieval-augmented generation systems.
Top risks: Document Poisoning · Retrieval Manipulation
Secure AI-powered customer support agents against prompt injection, PII leakage, and unauthorized actions. Enforce compliance for support bots handling sensitive customer data.
Top risks: PII Leakage Through Conversation · Unauthorized Action Execution
Secure AI coding agents against malicious code execution, MCP tool manipulation, and supply chain attacks. Runtime protection for Copilot, Cursor, and custom coding assistants.
Top risks: Malicious Code Execution · MCP Tool Server Compromise
Protect data analysis agents from SQL injection, unauthorized data access, and exfiltration. Runtime security for AI agents with database access and analytical tool use.
Top risks: SQL Injection Through Natural Language · Unauthorized Data Access Escalation
Secure autonomous AI agents executing multi-step workflows. Prevent cascading attacks, runaway execution, and unauthorized actions in agent loops, CrewAI, and AutoGPT-style systems.
Top risks: Cascading Injection Across Agent Steps · Runaway Execution and Resource Exhaustion
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.
Top risks: MCP Server Supply Chain Attacks · Tool Response Injection
Protect AI sales and outreach agents from PII mishandling, email automation abuse, and data compliance violations. Runtime security for CRM-connected AI agents.
Top risks: Unauthorized Email Sending · CRM Data Exfiltration
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.
Start with the policy template that matches your shape.
Free tier. 10,000 events/month. No credit card.