Secure AI Agents, LLM Apps, RAG Pipelines and MCP Tool Workflows
Enterprise AI agents now send email, write code, query databases, retrieve RAG context, operate SaaS workflows, and call MCP, A2A, and cloud APIs. Rutile registers every agent as an identity, evaluates every tool action against policy, and creates temporary access, audit evidence, and runtime controls.
Built for CISO, IAM, security architecture, AI governance, and platform engineering teams.
What is Rutile?
Rutile is an AI agent security product, AI agent governance product, and identity, access, and runtime control plane for enterprise AI agents. It helps organizations discover agents, govern owners and permissions, enforce zero-trust policies before LLM, RAG, MCP, A2A, SaaS, and API tool execution, and preserve an auditable delegation chain.
Every agent starts from zero trust.
Click each control to see how Rutile turns an agent action into governed execution.
Every agent needs an identity.
A governed agent has a unique ID, owner, purpose, scope, and expiration.
AI agents act like employees, but they are rarely managed like employees.
Companies apply SSO, MFA, RBAC, PAM, offboarding, and audit logs to people and server accounts. AI agents often operate with long-lived tokens, unclear ownership, prompt-injection exposure, RAG trust gaps, broad tool access, and weak runtime controls.
Shadow Agent
Unapproved agents and automation bots spread without IT or security oversight.
Over-Privileged Agent
Agents retain broad API keys instead of task-specific least privilege.
Tool Misuse
Prompt injection or manipulated context can push agents toward unauthorized tool calls.
No Audit Chain
Teams cannot prove who delegated work, which model reasoned, or which tool touched data.
No Runtime Kill Switch
Risky sessions continue because there is no control point to revoke or isolate the agent.
Rutile extends IAM, PAM, SIEM, and LLM gateways for the agentic era.
Traditional controls remain important, but AI agents need identity-aware runtime enforcement across prompts, tools, APIs, and delegated authority.
| Control | Primary target | Gap | Rutile role |
|---|---|---|---|
| IAM/SSO | Human users | Limited context for autonomous tool calls and delegation chains. | Registers AI agents as identities with owners, purposes, scopes, and lifecycle state. |
| PAM | Privileged accounts | Not designed for dynamic LLM tool-call level access. | Issues task, time, and data-scoped JIT/JEA permissions. |
| SIEM | Log analysis | Observes after execution but rarely enforces before execution. | Connects behavior, policy decisions, revocation, and audit evidence. |
| LLM Firewall | Prompts and responses | Weak coverage for identity, authority, tools, resources, and audit chains. | Controls LLM, MCP, A2A, SaaS, and API execution paths. |
AI security threats mapped to agent controls.
Rutile positions AI agent security as a control problem across identity, authority, context, tools, runtime behavior, and audit evidence. This makes the page easier for search engines and AI answer engines to match with concrete AI security questions.
| Risk | Search intent | Control pattern | Rutile capability |
|---|---|---|---|
| Prompt Injection | prompt injection protection, LLM security | Treat model, user, tool, and retrieved content as separate trust zones; verify high-risk tool calls before execution. | Policy proxy, delegation chain, runtime deny/revoke decisions. |
| Data Leakage | AI data security, sensitive data exposure | Constrain data scope, detect risky destinations, and preserve which agent touched which resource. | JIT/JEA permissions, resource scope, audit log schema. |
| Excessive Agency | AI agent security product, least privilege for agents | Limit autonomy with task-bound permissions, approval gates, expiration, and kill switch workflows. | Agent registry, permission broker, runtime kill switch. |
| Tool Abuse | MCP security, tool call security | Enforce allowlists, policy checks, identity binding, and tool-call telemetry before SaaS/API execution. | LLM/MCP/A2A tool proxy and tool-call enforcement. |
| RAG Poisoning | RAG security, vector database security | Record source trust, retrieval context, prompt hash, and data access boundaries for agent decisions. | Registry metadata, policy context, evidence trail. |
| Audit Failure | AI governance, AI compliance audit | Maintain traceable evidence across human owner, agent, model, tool, resource, policy, and outcome. | Audit, compliance, and reporting module. |
Referenced security and governance frameworks.
AI security pages are stronger when claims are connected to recognized primary sources. Rutile uses these frameworks as vocabulary for risk mapping, not as unsupported certification claims.
OWASP Top 10 for LLM Applications
Prompt injection, sensitive information disclosure, excessive agency, vector and embedding weaknesses, and related GenAI application risks.
OWASP Top 10 for Agentic Applications
Agent-specific risks across autonomy, tools, memory, multi-agent workflows, identity, and delegation.
OWASP MCP Top 10
Security considerations for MCP servers, tool permissions, secrets, authorization, telemetry, and supply chain exposure.
NIST AI Risk Management Framework
Govern, map, measure, and manage AI risks with trustworthiness considerations for AI systems.
ISO/IEC 42001
AI management system requirements for organizations that develop, provide, or use AI systems.
MITRE ATLAS
Knowledge base of adversary tactics and techniques against AI-enabled systems.
Discover. Govern. Protect.
Rutile turns agent sprawl into an operating model security teams can approve.
Discover
Find LLM apps, internal agents, MCP servers, A2A agents, OpenClaw, NemoClaw, and AX bots, then register them centrally.
Govern
Track each agent owner, purpose, model, runtime, connected tools, data access, risk tier, creation date, expiration, and last activity.
Protect
Evaluate every call, issue temporary access when allowed, and revoke, quarantine, or terminate when behavior becomes risky.
Every agent needs an identity.
A governed agent has a unique ID, owner, purpose, scope, and expiration.
Every permission should be temporary.
Replace static secrets with task, request, and time-scoped access.
Every action must be verified.
LLM, MCP, A2A, SaaS, and API calls pass policy checks before execution.
Every result must be auditable.
Rutile records the human, agent, model, tool, resource, policy decision, and outcome.
Agentic IAM vocabulary
Agentic IAM
Identity and access management adapted for autonomous AI agents.
Agent Registry
A governed inventory of agent IDs, owners, tools, permissions, risk, and lifecycle state.
JIT/JEA Access
Temporary and just-enough permission issued for a specific task, time window, and data scope.
Delegation Chain
The trace from human owner to agent, model, tool call, resource, policy decision, and result.
Runtime Kill Switch
A control that can revoke permission, quarantine an agent, or terminate a session during execution.
AI Agent Governance FAQ
Is Rutile an LLM firewall?+
Rutile covers more than prompt and response filtering. It governs agent identity, delegation chains, tool calls, temporary permissions, runtime behavior, and audit logs.
Does Rutile replace existing IAM?+
No. Rutile extends systems such as Okta, Entra, CyberArk, SailPoint, SIEM, and DLP with an AI-agent-specific control layer.
Which agents can Rutile manage?+
Rutile is designed for LLM chat, internal AI agents, MCP servers, A2A agents, OpenClaw, NemoClaw, and business automation agents.
Every agent starts from zero trust.
Rutile is an AI agent security product, AI agent governance product, and identity, access, and runtime control plane for enterprise AI agents. It helps organizations discover agents, govern owners and permissions, enforce zero-trust policies before LLM, RAG, MCP, A2A, SaaS, and API tool execution, and preserve an auditable delegation chain.