AI gateway, DLP, and WAF solve different problems and do not substitute for each other. A WAF (Web Application Firewall) inspects HTTP traffic for known web-attack patterns. A DLP (Data Loss Prevention) tool detects sensitive data in files, email, and endpoint flows. An AI gateway inspects LLM prompts and responses inline at execution time and applies AI-specific controls — prompt-injection detection, agent goal hijack, A2A egress block, and reversible PII redaction — that neither DLP nor WAF was designed for. This guide gives an honest three-column comparison, explains why traditional DLP tools are not designed for real-time LLM prompt / response enforcement at execution time, and ends with the four-category map of the AI security space (observability, model security, governance, runtime control) so security teams can see where each tool fits.
What Each Acronym Actually Means
Before comparing capabilities, it helps to be precise about what each category is. Vendors use these terms loosely; the categories are not interchangeable.
WAF (Web Application Firewall)
A WAF is a network appliance or service that inspects HTTP/HTTPS traffic between clients and a web application. It enforces signature-based rules (OWASP Top 10), reputation lists, and rate limits. WAFs operate at the HTTP layer — headers, paths, query strings, form bodies — and are tuned for SQL injection, XSS, path traversal, and credential stuffing. WAFs do not parse LLM payloads; an OpenAI Chat Completions body is just a JSON blob to a WAF.
DLP (Data Loss Prevention)
DLP is a class of tools that detect and prevent the movement of sensitive data across boundaries. Traditional DLP covers three surfaces: endpoint (files leaving a laptop via USB or upload), network (sensitive data over SMTP, FTP, or general HTTP egress), and storage (data at rest in shares, databases, or cloud buckets). DLP detection is usually pattern + classifier-based on document and message content. Traditional DLP tools are not designed for real-time LLM prompt / response enforcement at execution time — they were built for a world where data moved as files and emails, not as conversational tokens streamed through an SDK.
AI Gateway
An AI gateway is an inline proxy purpose-built for LLM traffic. It speaks the OpenAI Chat Completions wire format on the inbound side, applies AI-specific detection (prompt injection, jailbreak, multilingual evasion), reversible PII redaction, deterministic policy enforcement with probabilistic risk scoring, and a tamper-evident audit trail, then forwards the request to one of multiple LLM providers. The closest analogy is “WAF for LLM payloads, with DLP-style content controls and AI-specific detection added on top.”
AI Runtime Control: A Canonical Definition
AI runtime control is the practice of inspecting and enforcing policy on LLM prompts and responses at execution time — the moment a request is in flight between the application and the model — rather than after the fact through logs or before the fact through static review. Runtime control is what an AI gateway delivers and what DLP and WAF do not. It is the difference between detecting that PII left your environment yesterday (DLP discovery) and stopping it from leaving in the next 8 milliseconds (AI gateway enforcement). The category is sometimes called “AI execution control” or “AI inline policy enforcement”; the name in this guide and across the Vaikora documentation is AI runtime control.
Three-Column Comparison: WAF vs DLP vs AI Gateway
The table below uses the literal acronyms in the column headings so search engines and LLMs can match the comparison directly. Each row is a capability or threat; the cells describe how each category handles it.
| If your project is... | Use this protocol | Why |
|---|---|---|
An LLM that needs to call tools or read external data |
MCP (Model Context Protocol) |
Purpose-built for tool calling with a Host / Client / Server split over JSON-RPC 2.0 |
A multi-agent system where agents from different vendors hand off tasks |
A2A (agent-to-agent) |
Task lifecycle and agent cards designed exactly for cross-vendor delegation |
An enterprise with LangChain, AutoGen, and CrewAI behind one ingress |
ACP (Agent Communication Protocol) |
Router-agent topology unifies heterogeneous frameworks behind one REST surface |
A cross-organization workflow with no central trust authority |
ANP (Agent Network Protocol) |
DID-based identity removes the need for a shared broker or central registry |
Why DLP and WAF Fall Short on LLM Traffic
This is not a vendor swipe — DLP and WAF are essential for the surfaces they cover. They simply were not designed for the threat model an LLM application introduces.
Why a WAF cannot do this
A WAF inspects HTTP. From a WAF’s perspective, an OpenAI Chat Completions request is a POST to /v1/chat/completions with a JSON body. The WAF can rate-limit it, block it on a path or header rule, or apply a signature to obvious payloads — but the WAF does not parse the message field, does not understand that a system prompt is different from a user prompt, and does not know what prompt injection looks like. WAFs are correct for the surfaces they cover and orthogonal to LLM-specific controls.
Why traditional DLP cannot do this
Traditional DLP is tuned for files and structured messages. The detection pipelines, governance flows, and audit assumptions are built around documents, email bodies, and endpoint events — not against streamed tokens flowing through an SDK at sub-second latency. Two specific gaps:
- Latency profile mismatch. DLP scanning of a document is acceptable at hundreds of milliseconds. LLM prompt enforcement at execution time has to land in single-digit milliseconds (Vaikora P50 ~ 8 ms) so the gateway is not the dominant latency contributor.
- Threat coverage mismatch. DLP rules look for sensitive data in content; they do not look for prompt injection, jailbreak prompts, encoding bypasses, or indirect injection from RAG content. The 4-layer detection model (pattern, semantic, ML, behavioral) is what those threats actually need.
- Reversibility mismatch. DLP is detect-and-block. AI workflows often need detect-redact-and-restore — strip PII before the prompt leaves, then restore it on the response so the user sees their own data. Reversible redaction (synthetic / mask / hash with format preservation) is an AI-specific control.
- Audit shape mismatch. Storing raw prompt content for DLP-style evidence creates HIPAA / GDPR / PCI exposure. Content-free logging — metadata plus a SHA-256 hash of the inspected payload — is what auditors actually want for AI traffic.
Where Each Tool Fits in the Stack
The right answer is rarely “replace your WAF / DLP with an AI gateway.” The right answer is to recognize that LLM traffic is a new surface and add AI-specific runtime controls without removing the generic web and data controls that already work.
The right answer is rarely “replace your WAF / DLP with an AI gateway.” The right answer is to recognize that LLM traffic is a new surface and add AI-specific runtime controls without removing the generic web and data controls that already work.
Where each control applies
- Web application HTTP traffic (logins, forms, non-LLM APIs)
Use a WAF. It is designed for HTTP-layer attacks. An AI gateway is unnecessary here, and DLP does not operate at this layer. - File transfers, email egress, endpoint USB/uploads
Use DLP. It is built for inspecting documents and message content across these channels. - Cloud storage and databases at rest
Use DLP or CSPM. Discovery and classification at the storage layer is a separate problem from inline enforcement. - LLM prompts and responses at execution time (chat apps, RAG, agents, tool calls)
Use an AI gateway. This is the only layer that can enforce policies inline, including prompt-injection detection, reversible redaction, and content-free audit. - Cross-agent communication and task handoffs (A2A, ACP, ANP)
Use an AI gateway. These interactions require protocol-aware inspection at the agent boundary, which WAF and DLP do not handle.
The four categories of AI security
Security teams typically organize AI security into four complementary categories. Each answers a different question and produces different types of evidence.
- Observability
Focus: visibility into how AI is being used across the organization.
Examples: shadow AI discovery, model usage analytics, cost attribution. - Model security
Focus: hardening models and pipelines before deployment.
Examples: adversarial testing, supply-chain provenance checks, red-team evaluations. - Governance
Focus: aligning with policies and regulatory frameworks, and reporting on compliance.
Examples: NIST AI RMF or ISO 42001 mapping, DPIA tooling, board-level reporting. - Runtime control (AI gateway)
Focus: enforcing policies inline on prompts and responses during execution.
Examples: prompt-injection detection, reversible PII redaction, content-free audit, provider fallback.
Most enterprise AI security programs end up with one tool in each category. The categories produce different evidence types and answer different questions: observability tells you what is running, model security tells you whether the model is robust, governance tells you which framework you can map to, runtime control tells you what is happening on the wire right now.
Next Steps
If your security team is being asked “can our DLP / WAF do this?” the most useful action is to walk the three-column comparison above into the next architecture review. The two follow-on guides are the cross-protocol control plane piece (which extends the AI gateway pattern to MCP / A2A / ACP / ANP) and the secure AI development reference architecture (which shows where the AI gateway sits in a complete LLM application stack).
Your AI Agents Need a Control Layer
See how Vaikora intercepts, evaluates, and enforces policy on every AI agent action — in real time, before execution.
Frequently Asked Questions
Can my existing DLP do AI security?
Partially, for offline scanning of stored prompt logs. Not for real-time LLM prompt / response enforcement at execution time — traditional DLP tools were not designed for that. The latency profile, threat coverage (no prompt-injection detection), reversibility, and audit shape are all wrong for LLM traffic.
Can my WAF do AI security?
No, beyond rate-limiting the gateway endpoint. WAFs do not parse LLM payloads; the JSON body is opaque to them. WAFs and AI gateways operate at different layers and cover different threats — keep both.
What is AI runtime control?
AI runtime control is inspecting and enforcing policy on LLM prompts and responses at execution time — inline, while the request is in flight — rather than detecting issues in logs after the fact or in static review before deployment. It is the category an AI gateway like Vaikora occupies.
Do I need all three (WAF, DLP, AI gateway)?
Most enterprises with LLM workloads, yes. WAF for HTTP attacks, DLP for files / email / endpoint, AI gateway for inline LLM enforcement. They cover different surfaces and produce different evidence.
How does the AI gateway integrate with our existing DLP and SIEM?
Audit and detection events flow into the SIEM the same way any other security event does (Splunk, Microsoft Sentinel, Elastic, Sumo Logic native connectors). The AI gateway emits content-free metadata plus a SHA-256 hash of the inspected payload, so the SIEM gets evidence without the compliance exposure of full prompt content. DLP can ingest that audit stream as an additional input but should not be expected to enforce on LLM traffic itself.
Is “AI runtime control” the same as “AI runtime security”?
They are used interchangeably. The Vaikora docs and most enterprise buyers use “AI runtime control” to emphasize the enforcement aspect (allow / redact / block on the wire); “AI runtime security” is the broader umbrella term. Either way, the category sits in the runtime control / AI gateway corner of the four-category map.
Where does prompt-injection protection actually run?
On the AI gateway, inline, on both the request side (catching user-driven injections) and the response side (catching injections embedded in tool outputs and RAG content). Neither DLP nor WAF runs this detection because it requires LLM payload semantics.