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How do you secure LLM and AI agent traffic in production?
An AI gateway protects LLM and agent traffic by inspecting prompts, tool calls, and responses in real time, enforcing access controls on what data and actions an agent can reach, detecting and redacting sensitive data, and maintaining a full audit log of every model and agent interaction. Instead of relying on the model provider or the application layer to catch abuse after the fact, the gateway sits inline on every request, tool call, and response, applying security policy before a prompt reaches the model and before an agent's action is executed.
This matters more with agents than with simple LLM traffic, because an agent doesn't just generate text, it takes actions: calling tools, querying internal systems, and chaining multiple steps together autonomously. A malicious payload can be embedded in natural language, hidden inside a retrieved document, or planted in a tool's output, and a compromised agent can turn that into unauthorized data access or a real-world side effect. None of this is something a traditional web application firewall (WAF) or API gateway is built to parse. Securing production LLM and agent traffic requires a control point that understands semantic content and agent behavior, not just headers, methods, and rate thresholds.
TL;DR - Key Takeaways
- Traditional WAFs and API gateways inspect syntax (headers, payload size, known signatures). They cannot inspect the meaning of a prompt or the intent behind an agent's tool call, which is where most LLM and agent-specific attacks live.
- An AI gateway is the natural enforcement point for LLM and agent security because every prompt, tool call, and completion already passes through it.
- Agents raise the stakes: a compromised prompt no longer just produces bad text, it can trigger unauthorized tool calls, data access, or downstream actions, which is why excessive agency is one of the risks a gateway has to actively contain, not just log.
- The OWASP Top 10 for LLM Applications (2025) gives a structured threat model — prompt injection (LLM01), sensitive information disclosure (LLM02), excessive agency (LLM06), and unbounded consumption (LLM10) are the ones a gateway is best positioned to stop.
- Core gateway controls: prompt injection detection, PII detection and redaction, token- and tool-level access controls for agents, rate limiting, full audit logging, fail-closed behavior, and data residency enforcement.
- According to IBM's 2025 Cost of a Data Breach Report, 97% of organizations that suffered an AI-related security incident had no AI access controls in place, a governance gap gateways are designed to close.
- The EU AI Act (Articles 12, 19, and 26) requires automatic event logging and record-keeping for high-risk AI systems, capabilities an AI gateway can provide by default.
- TrustGate (the AI gateway) and TrustGuard (governance and threat detection) are complementary, not interchangeable: one enforces policy inline, the other assesses and monitors risk.
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Why Traditional WAFs and API Gateways Can't Protect LLM and Agent Traffic
Web application firewalls and API gateways were built for a world of structured requests: known endpoints, fixed schemas, and attacks that show up as malformed syntax, SQL fragments, script tags, oversized payloads. They're excellent at that job. They are the wrong tool for LLM and agent traffic because the attack surface has moved from syntax to semantics, and, with agents, from a single request to a chain of autonomous decisions.
A prompt injection attack doesn't look malicious at the protocol level. It's plain English (or any other language) instructing the model to ignore its system prompt, exfiltrate context, or call a tool it shouldn't. A WAF sees a well-formed JSON payload and lets it through. With an agent, the risk compounds: the injected instruction doesn't just produce a bad text response, it can get executed, a tool call, a database query, an outbound email, because the agent is designed to act on what it reads, including content planted by an attacker inside a document, a webpage, or a tool's own output. Sensitive data exposure follows the same pattern: an agent handling a support ticket might legitimately need to read a credit card number to resolve a case, but nothing about that request trips a signature-based rule. And because agentic systems chain multiple tool calls and model invocations together to complete a task, a single compromised step can cascade into a sequence of unauthorized actions, something rate limiting alone won't catch, because the request volume looks completely normal.
This is the gap an AI gateway is designed to close. If you're new to the concept, our guide on what an AI gateway is covers the architecture in more depth; this article focuses specifically on the security layer for both LLM and agent traffic.
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The AI Gateway as the Primary Security Control Point
Every prompt sent to a model, every tool call an agent makes, and every completion returned already passes through the gateway if one is deployed, which makes it the natural place to enforce security policy, rather than bolting checks onto the application layer or trusting the model provider's own filters. Centralizing enforcement at the gateway means policy is applied consistently across every application, agent, team, and model, instead of being reimplemented (and inevitably drifting) inside each service that calls an LLM.
A gateway that's doing its job for security should be able to:
- Inspect every prompt, tool call, and completion for injection attempts, policy violations, and sensitive content, not just log them after the fact.
- Enforce access controls at the token, user, agent, and application level, so a compromised credential or a manipulated agent doesn't grant blanket access to every model, tool, and dataset.
- Redact PII and other sensitive data before it reaches the model or before a completion reaches the caller.
- Rate-limit by user, application, and endpoint to prevent both abuse and runaway cost from unbounded consumption.
- Log every interaction in a tamper-resistant audit trail, satisfying both incident-response and compliance needs.
- Fail closed, when a policy check can't complete (timeout, upstream error, ambiguous classification), the safe default is to block the request, not let it through.
This is also where an AI gateway differs from a guardrails library bolted onto a single application: guardrails run inside the application's own process and have to be integrated per-service, while a gateway enforces policy centrally, in front of every application that talks to a model. We cover that distinction directly in AI gateway vs. guardrails.
Mapping OWASP LLM Top 10 Threats to Gateway Controls
The OWASP Top 10 for LLM Applications is the closest thing the industry has to a standard threat model for LLM security, and it's a useful lens for evaluating whether a gateway is actually covering the risks that matter. Not every threat on the list is something a gateway can fix on its own, some require changes to model training or application logic, but several sit squarely in the gateway's inspection path.
| Threat (OWASP ID) | Risk | Gateway Control |
|---|---|---|
| Prompt Injection (LLM01) | Crafted input overrides system instructions, causing the model to leak data or take unauthorized actions | Real-time prompt inspection and injection detection before the request reaches the model |
| Sensitive Information Disclosure (LLM02) | PII, credentials, or proprietary data appear in prompts or completions | PII detection and redaction applied to both inbound prompts and outbound responses |
| Excessive Agency (LLM06) | An agent is granted more tool access, permissions, or autonomy than the task requires, letting a manipulated agent take unintended actions | Token- and tool-level access controls limiting which tools, endpoints, and data sources a given agent or request can reach |
| System Prompt Leakage (LLM07) | An attacker extracts the system prompt to reverse-engineer guardrails | Output inspection that blocks completions resembling system-prompt content |
| Unbounded Consumption (LLM10) | Uncontrolled request volume drives cost overruns or denial-of-service conditions | Rate limiting and quota enforcement per user, application, and model endpoint |
Two threats worth flagging as gateway-adjacent rather than gateway-solved: Supply Chain risks (LLM03) and Data and Model Poisoning (LLM04) are largely about what goes into training and fine-tuning, which sits upstream of runtime traffic. A gateway can still help, for instance, by enforcing which models and versions an application is allowed to call, but it isn't a substitute for supply-chain and data-governance controls further back in the pipeline.
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Core Security Capabilities, in Practice
1. Prompt injection defense: detection has to run on every request, inline, with latency low enough not to degrade the user experience. That typically combines pattern-based detection for known injection techniques with semantic classification for novel ones, and the decision, allow, flag, or block, happens before the prompt reaches the model.
2. PII detection and redaction: this runs in both directions. Inbound, the gateway can strip or mask sensitive fields before they ever reach a third-party model provider, important when the provider's data-handling terms aren't fully trusted, or when regulatory requirements prohibit sending certain data categories externally at all. Outbound, the same inspection catches cases where a model reconstructs or infers PII it wasn't directly given.
3. Token & tool-level access controls: access should be scoped by API key or service identity down to which models, which tools, and which data sources a given caller, or a given agent, can reach, not an all-or-nothing credential shared across an entire application. For agents specifically, this means defining per-agent permissions: which tools it's allowed to call, which endpoints it can hit, and which actions require a human in the loop before executing. This is precisely the gap IBM's 2025 data breach research points to: organizations that suffered an AI-related incident overwhelmingly lacked this layer of control.
4. Rate limiting: beyond preventing abuse, rate limiting is a cost-control mechanism. Runaway agentic loops, an agent stuck re-calling the same tool, or chaining steps indefinitely because a stopping condition never triggers, can generate thousands of unnecessary model and tool calls in minutes; per-user, per-agent, and per-application quotas cap the blast radius.
5. Full audit logging: every prompt, completion, policy decision, and redaction event should be logged in a way that supports both security investigations and compliance reporting, see the EU AI Act section below for why this specific requirement is becoming non-optional for many deployments.
6. Fail-closed behavior: when a security check can't be completed, the classification service times out, a policy lookup fails, the gateway should block the request rather than pass it through. Fail-open configurations quietly turn every outage into a security gap.
7. Data residency enforcement: for organizations operating under regional data protection requirements, the gateway can enforce which model endpoints and regions a given request is allowed to route to, preventing data from leaving an approved jurisdiction. We go deeper on this in AI gateways and data sovereignty.
Example: A Security Policy in TrustGate
Below is a simplified example of what a security policy configuration looks like at the gateway level, combining prompt injection detection, PII redaction, agent tool-access controls, and a rate limit into a single enforced policy:
The specifics will vary by platform, but the pattern is consistent: policy is declarative, applied at the gateway rather than in application code, and defaults to blocking when a check fails.
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Compliance Is Catching Up, and Logging Is the Common Thread
Two developments make audit logging and access controls less of a best practice and more of a requirement.
First, the cost of getting AI security wrong is now measurable. IBM's 2025 Cost of a Data Breach Report found that 13% of organizations reported a breach involving an AI model or application, and of those, 97% lacked proper AI access controls. The same report found that 60% of AI-related incidents led to compromised data and 31% caused operational disruption, and that organizations using AI and automation extensively in their security operations saved an average of $1.9 million per breach and cut the breach lifecycle by 80 days. The pattern is consistent: ungoverned AI systems get breached more often, and cost more when they do.
Second, regulation is formalizing what "good governance" means. The EU AI Act requires high-risk AI systems to support automatic recording of events over the system's lifetime (Article 12), obligates providers to retain those logs (Article 19), and requires deployers to keep logs for at least six months and cooperate with competent authorities on request (Article 26). An AI gateway that logs every prompt, completion, and policy decision by default is well positioned to satisfy this requirement without requiring every downstream application to implement logging separately.
TrustGate and TrustGuard: Two Layers, Not One Product
It's worth being precise about where enforcement and assessment sit, because the two are often conflated.
- TrustGate is the AI gateway, it sits inline on every request and every agent tool call, enforcing the policies described above in real time: blocking prompt injection, redacting PII, applying access and tool-permission controls, and producing the audit log.
- TrustGuard operates at the governance and threat-detection layer, assessing agent risk, running adversarial testing against agentic workflows, and providing the broader visibility that informs what TrustGate's policies should actually be.
In practice, the two are complementary: TrustGuard tells you which tools and actions an agent should never be allowed, TrustGate enforces that on every request.
FAQs about AI Gateway Security
1. How does an AI gateway prevent prompt injection?
It inspects every prompt, and every tool call an agent makes, inline, before the request reaches the model or the tool executes, using a combination of pattern-based detection for known injection techniques and semantic classification for novel ones, including injections planted in retrieved documents or tool outputs. Requests classified as malicious are blocked (or flagged, depending on policy) rather than passed through, and because this happens at the gateway, the same protection applies across every application and agent that routes through it, not just the ones that implemented their own checks.
Related article: How to Set Up Prompt Injection Detection for Your LLM Stack
2. Can an AI gateway detect PII?
Yes. PII detection runs on both inbound prompts and outbound completions, identifying categories like emails, phone numbers, credit card numbers, and government IDs, and either redacting or blocking the content based on policy. This protects against sensitive data being sent to a third-party model provider and against a model surfacing PII it inferred rather than being directly given.
3. What is fail-closed in an AI gateway?
Fail-closed means that when a security check can't complete, a classification service times out, a policy lookup errors, the gateway blocks the request by default instead of letting it through. The alternative, fail-open, prioritizes availability over security and turns any outage in the checking pipeline into an unprotected window. For production LLM traffic handling sensitive data, fail-closed is the safer default.
4. How does an AI gateway differ from a WAF?
A WAF inspects traffic at the syntax level, headers, payload structure, known attack signatures, which works well for conventional web exploits like SQL injection or XSS. An AI gateway inspects traffic at the semantic level, understanding the meaning of a prompt or completion well enough to detect injection attempts, sensitive content, or policy violations expressed in natural language. The two aren't mutually exclusive: many production deployments keep a WAF in front of the application layer and add an AI gateway specifically for the LLM traffic a WAF can't meaningfully parse.
Related Articles
- What Is an AI Gateway? Complete Guide 2026
- AI Gateway Security: How to Protect LLM Traffic in Production
- AI Gateway Architecture: How It Works Under the Hood
- How an AI Gateway Solves LLM Observability
- How an AI Gateway Reduces LLM Costs
- How an AI Gateway Solves AI Governance for Enterprise
- How to Choose an AI Gateway: Enterprise Buyer's Guide (2026)
- AI Gateway for Agentic AI: Securing Multi-Agent Workflows
- How to Deploy a Self-Hosted AI Gateway (Step-by-Step)
About the Author
Alessandro Pignati is Lead AI Security Researcher at NeuralTrust, where he leads research on AI and agentic security, advancing techniques to evaluate and secure large language models and autonomous AI systems. He specializes in adversarial machine learning, AI red teaming, LLM security, and AI safety, contributing to the development of secure and trustworthy AI.
NeuralTrust is an AI agent security platform, recognized in the Gartner 2025 Market Guide for AI Gateways and Guardian Agents, and the KuppingerCole 2025 Leadership Compass for Generative AI Defense. Headquartered in Barcelona with ISO 27001 certification.
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