Enterprise AI Security with MCP Gateway & Runtime Guardrails

Introduction

Logs and analytics are crucial parts of an AI Gateway. On the surface that sounds routine. In reality the design choices under those features shape reliability, security and cost in an AI Gateway and an MCP Gateway. Our team co hosted a webinar with leaders from Palo Alto Networks to share what works today. We compared old network centric models with a world where a single prompt can trigger data loss without touching a firewall rule. We also walked through practical guardrails and a live demo that tied models, tools and policies into one flow.

This article distills that session into a clear plan. It avoids theory and focuses on decisions that teams are making right now. You will see why context and identity form the new perimeter. You will learn how an AI gateway and an MCP gateway work together. You will also see how runtime guardrails from Prisma AIRS connect into that control plane. The goal is simple. Give your business safe velocity without slowing your builders.

From packets to prompts - a new security era

Classic security relied on static boundaries. Requests hit a firewall. Policies matched on ports and IPs. That model still matters for many systems. But it does not explain what is happening with AI. Language models accept natural language. They call tools. They chain decisions. Attackers do not need to break a network rule. They can convince a model to break intent. That is why context and identity now act as the real perimeter. Who is asking? What are they allowed to do? What context is in scope. What trace ties the steps together. Answer those and you can enforce policy with confidence.

How should one think of LLM security?

OWASP published the AI Top Ten to give teams a common frame. It is a great start for threat modeling. Prompt injection sits near the top because the attack surface is everywhere. Untrusted pages. Shared documents. Paste bins. A model can be tricked into ignoring system rules or into leaking sensitive data. The same list also calls out improper output handling toxic content, weak auth and supply chain issues. You do not need all items on day one. You do need a plan that touches inputs outputs identity and audit from the first pilot anchored in an AI Gateway and an MCP Gateway.

What real teams fear the most

We polled the audience live. The largest share named data leakage and exfiltration as the top concern. Prompt injection came next. Lack of observability was close behind. Over privileged access ranked lower yet it still matters. That mix mirrors what we see in the field. The lesson is simple. Secure inputs and outputs first. Then make every token and every tool call traceable across the gateway. Without traces you have no attribution. Without attribution you cannot enforce policy or fix cost blowups.

Strong identity and access

Many teams start with a single model key for early experiments. That is fine for a proof of concept. It is not acceptable for production. You need strong authentication and role based access. Users and services should have clear scopes. Access should be granular per model and per tool via an MCP Gateway. Rotate credentials. Prefer short lived tokens. Tag requests with identity so traces show who did what. Treat identity as part of the payload that flows through the gateway. When you can answer who and why the rest of your security program gets easier.

Guarding the input layer

The input layer is where prompts, instructions, context and external data meet. This is where prompt injection shows up. This is also where agents receive tool lists. You need checks that look at the full request not just a single string. Detect jailbreak patterns and instruction overrides. Filter untrusted context or mark it as untrusted and limit its power. Enforce allow and deny lists. Push obviously risky flows into a review channel for humans. The goal is not to block users. The goal is to block dangerous intent while keeping useful work moving.

Guarding the output layer

The response side matters just as much. Models can return PII or secret values. They can cite malicious links. They can produce text that breaks policy or brand tone. Output guardrails catch those cases. Mask sensitive values. Block unsafe categories. Require grounded answers for some workflows. Send suspect responses to a human queue. Your AI gateway is the right place for these checks because it sees identity context and the raw response. It can then log the full trace for audit.

Observability with purpose

If you cannot trace a flow you cannot secure it. You also cannot control cost. Good observability covers input tokens output tokens model choices tool calls and timing for each step. That data must use one trace ID from start to finish. Tie the trace to user identity and application identity. Then make a single view for platform teams and for security analysts. You will find shadow usage. You will find loops in agents. Observability is not an afterthought. It is the backbone of policy and finance.

Cost control and loop breakers

Language models are easy to call. Agents can call them again and again. They can also trigger other services. A small bug can become a large bill. Put budgets in the gateway. Set request caps per user and per app. Set time limits for agent loops. Apply rate limits for tools that have their own billing. Alert when a trace crosses a threshold. These are simple features yet they protect your runway.

Supply chain safety for models and tools

Models live in many registries. Tools come from many repos. Treat them like any other dependency. Scan every model before use. Record the source and the version. Red team your prompts and your agents. Keep an allow list of trusted tools. For community MCP servers read the code and lock the version. Use the gateway to store approvals and block anything unapproved.

Making MCP safe by design

Why MCP changes the stakes

MCP gives agents a common way to discover and call tools. That power unlocks real work. It also expands risk. A single agent can reach into source control, chat calendars and data stores. Without guardrails a simple instruction can cause harm. The answer is an MCP gateway that sits between agents and servers. It makes discovery safe. It centralizes auth. It controls which tools are visible to whom. It logs every call. It also adds rates and quotas so a bug does not become a fire.

Three layer authorization

An effective MCP gateway performs three checks for each request. First it verifies the person or service that is calling. Use your identity provider. Second it checks if that identity may reach the named MCP server. Teams can grant access to servers just like they grant access to apps. Third, it performs token translation into the credential that the target server expects. That way an agent never needs to hold raw secrets for Slack or GitHub or any other system. One token in. Many safe tokens out. All actions tied to one audit trail.

Tool level permissions and virtual servers

An MCP server groups many tools. Some read data. Some write data. Some delete data. Not every agent should see all of them. The gateway should let you grant access at the tool level. It should also let you build a virtual MCP server that combines tools from different servers for one use case. For example an agent that lists pull requests and nudges a teammate on chat needs a small set of read methods and one message method. Package just those. Present a single clean surface. Reduce blast radius and cognitive load at the same time.

Runtime guardrails with Prisma AIRS

Palo Alto Networks built Prisma AIRS to inspect prompts and responses at runtime. It detects prompt injection. It performs DLP checks and masking. It flags toxic output. It blocks dangerous URLs and links to malware which is vital for agent flows that browse or fetch pages. You can tune profiles by app. You can also add custom topics for your brand and your policies. The AI gateway forwards input and output to Prisma AIRS. The result and the reason come back. The gateway allows or blocks and logs the decision. Prisma AIRS also records the event in its own console which helps teams that separate security review from product operations.

Watch our live demo

In our demo, an agent lists open pull requests for a teammate and nudges them on Slack. The AI Gateway validates inputs and masks secrets before the model reasons. The MCP Gateway exposes a virtual MCP server with only two tools (GitHub read-only, Slack message-scoped). At runtime, Prisma evaluates prompts and responses, filters suspicious URLs in citations, and enforces DLP rules.

Watch the demo: https://youtu.be/hWNV2v3C8SA

Why an API gateway is not enough

A normal API gateway handles routing, basic limits and some auth. It does not cover what modern AI stacks need. The AI gateway owns input and output guardrails central auth model access unified traces and budgets with loop breakers to stop runaway agents. The MCP gateway handles curated discovery tool level permissions token translation for each tool and per tool rate limits and quotas with full audit of tool calls. You need both working together. Traditional API gateways do not take on these jobs.

What teams get on day one

Centralized access to models and MCP servers. Clean code snippets for Python, TypeScript and AI Gateway SDK snippets so teams start fast. One place to manage quotas and allow lists. Guardrails that you can turn on for the highest risk apps first. Unified traces and cost views that remove guesswork. Optional marketplace servers that are vetted by the platform team. A path for end customers when you need to expose an MCP surface outside your org. Less time spent wiring glue. More time spent building value.

Practical rollout steps

Start with identity in the gateway. Register model providers and MCP servers. Use groups that mirror your org. Create a virtual MCP server for one narrow agent. Turn on input and output guardrails for that flow. Watch traces for a week. Add budgets and rates based on real usage. Hold a short red team session. Fix what you learn. Repeat for the next agent. In parallel build a small catalog of approved MCP servers and versions. Keep a short list at first. Growth becomes smooth when the first paths work well.

A word on culture

Security programs work when builders feel enabled. Show teams how the gateway removes toil. Share traces that help them debug. Share cost data that helps them ship within budget. Invite security to design reviews so policies fit the product. Keep the tone practical. The goal is safe outcomes not paperwork. When people see that the flow is safe and fast they choose the paved road.

Closing thoughts

The last twenty years moved security from ports and hardware to identity and context. AI speeds that shift. A prompt can cross many systems in seconds. A bug in an agent can spend real money fast. The answer is a clear control plane with an AI Gateway and an MCP Gateway. Place an AI gateway in front of model traffic. Place an MCP gateway in front of tools. Tie them together with one trace across both gateways. Add runtime guardrails that inspect both input and output. Apply least privilege at the server and at the tool. Use budgets to stop loops. Record everything.

This approach does not slow teams. It gives them a single path that is fast, safe and observable. It also passes reviews with confidence. Your models and your tools live behind one standard contract. Your data stays in your cloud. Your policies live in one place.

If you want to see the flow in action we can share the full session and a live walkthrough. If you want hands-on time we offer a trial for the MCP gateway in both SaaS and self hosted forms. The best time to build your GenAI stack is now.