LLM Security Tools Compared: Gateways, DLP, Guardrails, and Proxies

A comparison of LLM security approaches — AI gateways, enterprise DLP, guardrail libraries, and content-aware proxies explained.

The landscape is confusing

If you've spent any time evaluating "AI security" or "LLM security" tools, you've noticed: the category is a mess. Four different types of products all claim the same keywords. An API gateway, a data loss prevention platform, an open-source validation library, and a security proxy all show up when you search for "LLM security" — and they solve very different problems.

CTOs and security leads often end up comparing apples to oranges. A routing-focused gateway gets measured against a DLP vendor's detection depth. A per-service library gets compared to infrastructure-level coverage. The feature checklists overlap just enough to confuse, but the architectural differences are what matter.

Each category has a different primary purpose, and security is either the core design goal or something bolted on. Understanding that distinction saves weeks of evaluation time.

A note on transparency: we build Grepture, which falls in the content-aware proxy category. We'll be honest about it throughout — including its limitations.

AI Gateways

Examples: Portkey, Helicone, LiteLLM, Kong AI Gateway

What they're built for

AI gateways are API routing and operations tools. They sit between your application and multiple AI providers, handling load balancing, model fallback, cost tracking, and observability. If you're calling OpenAI, Anthropic, and Google AI from the same application and need unified logging, cost breakdowns, and automatic failover — gateways are purpose-built for this.

The security angle

Some gateways have added security features — basic content filtering, rate limiting, or audit logging. Useful add-ons, but add-ons. Security is not the core design goal, and the detection capabilities reflect that.

Strengths

• Great for multi-provider management — one integration point for all your AI providers
• Cost optimization — track spend per model, per team, per feature
• Operational visibility — latency metrics, error rates, request volumes
• Model fallback — if OpenAI is slow, route to Anthropic automatically
• Mature ecosystem with strong developer experience

Limitations for security

• Detection is typically shallow — keyword matching or basic pattern detection, not deep PII detection with local AI models
• No mask-and-restore — if data gets redacted, it's gone permanently
• Limited or no prompt injection detection
• Often no streaming-aware content scanning — SSE traffic passes through without inspection
• Security features tend to lag behind the routing and observability features in maturity

When to choose a gateway

You need routing and observability first and you're okay with light security guardrails on top. If multi-provider management is your primary problem and security is secondary, start here.

Enterprise DLP

Examples: Nightfall AI, Strac, Google Cloud DLP

What they're built for

Enterprise DLP platforms are data loss prevention tools covering many communication channels — email, Slack, cloud storage, SaaS apps, and increasingly, AI APIs. They've been doing sensitive data detection for years and expanded to cover LLM traffic as AI adoption grew.

The security angle

Security is the core purpose here. DLP vendors have deep detection capabilities, often ML-powered, trained on broad datasets. Their detection engines are mature and battle-tested.

Strengths

• Mature detection engines — years of refinement across many data types and formats
• Broad coverage — protect email, Slack, cloud storage, and AI from one platform
• Enterprise-grade compliance — SOC 2, HIPAA certifications, audit reports, the works
• ML-powered detection — goes beyond regex for unstructured data
• Established vendor relationships and procurement processes

Limitations for AI specifically

• Not purpose-built for LLM traffic. AI API scanning is an extension of their existing platform, not the primary use case.
• Streaming (SSE) support is often missing. Many DLP tools buffer the entire response before scanning, adding latency to streaming completions. Users notice.
• No mask-and-restore. DLP tools typically offer permanent redaction only. Once data is stripped, it doesn't come back. This breaks AI use cases where you need real values in the output. Mask and restore is a different paradigm.
• Pricing is per-scan and expensive. Thousands of LLM API calls per day add up fast. AI traffic volume is often much higher than email or Slack scanning.
• Integration can be heavy. If AI is your only use case, the setup overhead may not be worth it.

When to choose enterprise DLP

You need DLP across your entire stack — email, Slack, cloud storage, and AI. AI is one channel among many, and you want a single vendor covering all of them. The per-scan cost is justified by the breadth of coverage.

Guardrail Libraries

Examples: LLM Guard, Guardrails AI, Lakera Guard

What they're built for

Guardrail libraries are in-code validation tools. Import a library, wrap your LLM calls with validation checks, define rules for acceptable inputs and outputs. They run in your application process alongside your LLM integration code.

The security angle

Flexible, often open-source validators for PII, injection, toxicity, and custom checks. Coverage depends on which validators you configure and how thoroughly you integrate them.

Strengths

• Developer-friendly — import a package, write a few lines of code, done
• Highly customizable — write custom validators for your specific domain
• Can run on your own infrastructure — no external dependencies if you use open-source options
• Fine-grained control — different rules per endpoint, per model, per use case
• Low cost — open-source options are free, paid tiers are typically reasonable
• Active open-source communities with fast-moving development

Limitations

• Per-service integration. Every service that talks to an LLM needs the library imported and configured. Ten services means ten integration points.
• No centralized policy management. Changing a rule means updating every service and redeploying.
• No unified audit trail. Each service logs independently. Correlating events across your AI surface area requires custom work.
• Consistency is hard at scale. Ensuring every service applies the same rules with the same thresholds takes discipline.
• No network-layer safety net. A new service that calls an LLM without the library? Unprotected.
• Streaming support varies. Some libraries handle SSE well, others don't.

When to choose a guardrail library

You have one service (or a few) talking to LLMs and want granular per-endpoint control. Centralized policy management isn't a hard requirement. Or you want custom per-endpoint logic on top of another detection layer.

Content-Aware Security Proxies

Example: Grepture

This is the category we operate in. We'll be upfront about what it is and where it falls short.

What it's built for

A content-aware security proxy sits at the network layer between your services and AI providers. Every API call flows through the proxy — scanned, acted on, and logged from a single point.

What makes this approach different

The idea: DLP-level detection depth with the simplicity of a network proxy. Instead of integrating a library into every service or configuring a DLP platform across channels you don't use, you point your AI traffic through a proxy purpose-built for LLM security.

Key characteristics of this category:

• Regex rules + AI detection. 50+ built-in regex patterns on the free tier (80+ on Pro) for structured PII and secrets. Local AI models, prompt injection scoring, toxicity detection, and DLP cover what regex can't. All AI models run locally on Grepture infrastructure. Rules are auditable and deterministic; AI adds depth. Complementary, not competing.
• Mask-and-restore. Replace sensitive values with tokens, store originals in a vault with a TTL, let the LLM process a clean prompt, then swap real values back in the response. No information lost, no PII exposed. How it works.
• Streaming-native. SSE detokenization happens in real time — no buffering entire responses, no latency spike. Streamed chunks get scanned as they arrive.
• Zero-data mode. On Business plans and above, Grepture processes every request in memory only. No request bodies, no response bodies, no headers touch storage. Only operational metadata gets logged.
• Single integration point. Install the SDK, wrap your AI client, and every service in your stack is covered. No per-service library imports, no per-channel DLP configuration.

Strengths

• Zero code changes — drop-in SDK wraps your existing OpenAI/Anthropic client
• Centralized policies — one rule set covers all services and all AI providers
• Unified audit trail — dashboard shows every request, every detection, every action across your entire AI surface area
• Streaming support — SSE scanning without buffering
• Mask-and-restore — reversible redaction that keeps your AI features working
• Prompt injection scoring — AI-powered detection with configurable thresholds
• EU-hosted with 99.98% uptime
• Open source proxy core and SDK

Limitations

Clearly:

• Adds a network hop. Every request routes through the proxy before reaching the AI provider. Sub-millisecond rule processing and 99.98% uptime mitigate this — but it's still an extra hop.
• Proxy dependency. If the proxy goes down, your AI calls are affected. Configurable fallback modes help — passthrough (traffic flows directly to provider) or error (requests fail). You choose based on your priorities.
• Newer category. Content-aware security proxies are a younger category than enterprise DLP. Fewer years of battle-testing, fewer enterprise logos, smaller team.
• Not a multi-channel DLP. We cover AI traffic specifically. If you also need email, Slack, and cloud storage DLP, you'll need a separate tool for those channels.

When to choose a content-aware proxy

Security is your primary concern for AI traffic and you want centralized protection without per-service integration work. You value mask-and-restore over permanent redaction. You want streaming support that doesn't add latency. Check the quickstart guide to see how fast the integration is.

Comparison table

How to choose: a decision tree

Simpler way to think about it:

• Your primary need is multi-provider routing and cost tracking? Start with an AI gateway. Add security tooling later if your requirements grow.

• You need DLP across email, Slack, cloud storage, and AI is just one more channel? Enterprise DLP makes sense. You get one vendor covering your entire communication surface area.

• You have a single service calling LLMs and want fine-grained, per-endpoint control? A guardrail library gives you maximum flexibility with minimal overhead for simple architectures.

• Security is your primary concern for AI traffic, and you want deep, centralized protection with minimal integration work? A content-aware proxy like Grepture fits this need. Drop-in SDK, centralized policies, mask-and-restore, streaming support.

These categories aren't mutually exclusive

You don't have to pick one. These tools operate at different layers and can complement each other.

Run Grepture as your security proxy alongside an AI gateway for routing — proxy handles detection and redaction, gateway handles load balancing and fallback. Or use a guardrail library for custom per-endpoint logic on top of proxy-level detection as a baseline. The guardrail catches domain-specific edge cases; the proxy ensures nothing slips through at the network layer.

The question isn't "which category is best?" — it's "which problem am I solving first, and what's the right tool for that specific problem?"

Where to go from here

A few resources that might help:

• Prevent data leaks in LLM API calls — a hands-on tutorial for setting up detection and redaction
• PII detection best practices — understanding detection strategies in depth
• EU AI Act compliance guide — what the regulatory landscape looks like and what's required
• Grepture quickstart — see how the proxy approach works in practice (takes about five minutes)

Grepture is free for up to 1,000 requests per month with 25 AI detection requests — no credit card required. The proxy core and SDK are open source.