AI Security for Enterprise LLM, RAG, MCP and Agentic Workflows
AI security is moving from prompt filtering toward operational control. Enterprise teams need visibility and enforcement across model inputs, retrieved context, tool calls, identities, permissions, runtime actions, and evidence.
What is AI security?
AI security is the practice of protecting AI-enabled systems from misuse, manipulation, data exposure, model and supply-chain risks, unsafe tool execution, and governance failure. For generative AI, it includes LLM application security, RAG security, MCP security, AI agent security, runtime monitoring, and compliance evidence.
Search intent this page answers
Broad AI security queries need a clear taxonomy that connects AI risks with practical enterprise controls.
- What is AI security?
- How do LLM security, RAG security, and AI agent security differ?
- What AI agents security controls are needed for enterprise adoption?
- What controls are needed for enterprise AI adoption?
- How do AI security programs map to NIST, ISO, OWASP, and MITRE?
Risk areas
AI security spans application, data, model, identity, tool, and governance risk.
| Risk | Why it matters | Rutile response |
|---|---|---|
| Prompt and context manipulation | Attackers influence model behavior through direct prompts, indirect content, or poisoned context. | Policy proxy and tool-call checks reduce operational impact. |
| Sensitive data exposure | Agents and LLM apps may expose PII, secrets, prompts, documents, or regulated records. | Scoped permissions, resource boundaries, and audit logs. |
| Uncontrolled autonomy | AI systems act without sufficient identity, approvals, or rollback paths. | Agent registry, JIT/JEA, approvals, monitoring, and kill switch. |
| Governance gaps | Teams cannot prove risk ownership, control coverage, or evidence against AI governance frameworks. | Framework mapping and reporting across agent actions. |
Control layers
A practical AI security program needs multiple layers rather than a single prompt filter.
| Control | Implementation pattern | Rutile capability |
|---|---|---|
| Inventory | Find AI apps, agents, MCP servers, data flows, and connected tools. | Discovery and Registry. |
| Access control | Limit who or what can invoke tools and resources, and for how long. | JIT/JEA Permission Broker. |
| Runtime policy | Evaluate requests, retrieved context, tool calls, and destinations before execution. | Policy Proxy. |
| Evidence | Record decisions, owners, permissions, outcomes, and exceptions. | Audit and Compliance Reporting. |
Primary references
These references anchor the page in recognized AI security guidance.
OWASP Top 10 for Large Language Model Applications
Defines critical LLM application risks including prompt injection, sensitive information disclosure, excessive agency, and vector or embedding weaknesses.
NIST AI RMF Generative AI Profile
Applies NIST AI RMF concepts to generative AI risks and mitigation practices.
Google's Secure AI Framework
Frames AI development, deployment, and operation through a security lens.
MITRE ATLAS
Documents adversary tactics and techniques against AI-enabled systems.
Related AI security topics
AI Security FAQ
Is AI security only about prompt injection?+
No. Prompt injection is important, but enterprise AI security also covers data access, tool execution, identity, supply chain, retrieval, runtime control, monitoring, and governance.
Where does Rutile fit in an AI security stack?+
Rutile focuses on the identity, access, runtime enforcement, and audit layer for AI agents and tool-using LLM systems.
Control layers
A practical AI security program needs multiple layers rather than a single prompt filter.