Introduction

Large Language Models (LLMs) such as OpenAI’s GPT, Anthropic’s Claude, and Meta’s LLaMA have unlocked unprecedented capabilities in generative reasoning, summarization, and autonomous agent behavior. However, exposing these models through APIs introduces new governance, performance, and security challenges. This page explores how LLM APIs are different—and what it means for modern API management.

Why LLM APIs Are Different

Unlike traditional APIs, LLM APIs:

• Don’t have strict contracts – they accept natural language and return probabilistic results

• Can hallucinate – results may include false or biased information

• Are metered by tokens – pricing and rate-limiting are based on tokens, not requests

• Can be manipulated – vulnerable to prompt injection, jailbreaking, and context leakage

These attributes require new layers of governance that sit above and beyond normal API security.

Governance & Security Challenges

LLM APIs introduce several unique risks

Risk Description

Prompt Injection Malicious instructions hidden inside user input or system prompts

Data Leakage Sensitive data stored in context windows or model memory

Unbounded Output Models generating inappropriate, toxic, or unfiltered content

Unauthorized Use Model overuse, scraping, or agent impersonation

Drift & Non-determinism Output varies based on versioning and tuning

How Modern API Management Is Evolving for LLMs

Prompt Sanitization Layers

1. Insert policy-based validation at the gateway to scan for injections or banned phrases.

Token Metering & Usage Enforcement

Track usage at the token level, apply rate limits by user or org, and alert on anomalies.

2. Output Moderation

Use post-processing pipelines (like Trust Layer) to analyze responses for hallucination, bias, or compliance violations.

3. Context Isolation

Implement mechanisms like Model Context Protocol (MCP) to keep user sessions private and separated in shared multi-agent environments.

4. Role-Based Prompt Control

Gate prompts based on roles and entitlements. For example, an internal app can access medical reasoning prompts, but external apps cannot.

Key Tools & Frameworks

• Salesforce Trust Layer – Filter, redact, and audit LLM prompts/responses

• MuleSoft Flex Gateway + LLM Policy Engine – Enforce AI-specific gateway policies

• OpenAI Moderation API – Detect harmful or unsafe content

• LangChain / Agentforce – Build LLM pipelines and control agent behavior

• MCP (Model Context Protocol) – Control context routing and identity

Use Case Spotlight: Healthcare LLM API Governance

A leading health system uses Flex Gateway and Trust Layer to:

• Sanitize patient-generated prompts

• Token-gate usage of an LLM-powered symptom checker

• Filter hallucinated responses that violate medical compliance

• Log and audit all interactions for HIPAA review

Final Thoughts

LLM APIs are opening up powerful new experiences—but without intentional governance, they risk becoming uncontrollable and unsafe. By bringing together AI-native policies, token-level metering, and ethical guardrails, you can build intelligent and trustworthy systems powered by LLMs.

Explore More

• How A2A Works with LLMs

• Building an LLM Gateway with Flex

• MCP: Model Context Protocol Explained