10 Best LLM Gateway Security Tools for Enterprise AI Deployments

Q1. Choosing an LLM Gateway Security Tool: What's Actually at Stake

An LLM gateway sits in front of every model call your applications make. It sees prompts, tool arguments, provider keys, and agent decisions. That makes it the strongest place to enforce policy and the worst place to be wrong. Attacks execute at runtime, not at scan time, and a gateway that only logs cannot stop an agent that acts in seconds. CISA added a command injection flaw in one widely deployed AI gateway to its Known Exploited Vulnerabilities catalog on June 8, 2026, with a June 22 remediation deadline. Ten tools were assessed against five criteria below.

⚙️ Our Evaluation Criteria

Each tool below was assessed against the same five criteria, in the same order, on every card.

• LLM and agent runtime enforcement. Does the tool defend live agents against prompt injection, tool poisoning, and abuse of MCP (Model Context Protocol, the open standard agents use to call external tools)? Our own approach to agentic AI security treats this as the first question, not the last.
• Tool-call and egress control. Does it validate tool names and arguments before execution and watch outbound traffic, or does it only filter prompts?
• Deployment reach. Can it run self-hosted, on-prem, sovereign, or air-gapped, across cloud, data center, and edge locations?
• Self-hardening record. What does the tool's own public CVE, CISA KEV, and EPSS history show? EPSS (Exploit Prediction Scoring System) estimates the chance a flaw gets exploited in the next 30 days, and it is one of the inputs behind exploitation-aware vulnerability management.
• Audit evidence quality. Does it produce records a CRA, NIS2, or SOC 2 reviewer will accept, without logging prompt contents? Mapping runtime evidence to requirement IDs is what our CRA and NIS2 Compliance Center is built to do.

Deliberately not scored here: routing benchmarks, token pricing, and observability dashboards. Those matter, but they are not security controls.

👤 Who This Guide Is For

• AI-workload and LLM-agent runtime-security owners who are responsible for agents already running in production, and who need AI and LLM security that acts at runtime.
• SecOps and DevSecOps leads at SMBs making cloud, AI, and GPU workloads production-safe without a 24/7 team.
• Kubernetes and container runtime-security owners who need enforcement at the node, not just in a dashboard, which is the core of Kubernetes runtime protection.
• GPU-cloud and AI-infrastructure security leads protecting inference nodes and shared GPU clusters, as in this GPU cloud security case study.

🧭 Which Tool Fits Which Situation

Total providers assessed: 10. These split into three different jobs. Gateway platforms own the request path. Guardrail and governance layers inspect content. Runtime layers watch what a decision actually does on the host. Buyers who pick one job and assume it covers the other two are the ones who get surprised.

• EdgeLabs: Best for runtime detection and response around AI workloads and agents, including self-hosted and air-gapped environments.
• LiteLLM: Best for teams that want a self-hosted, open-source gateway they fully control.
• Portkey: Best for teams wanting managed guardrails and per-key governance without building the layer themselves.
• Kong AI Gateway: Best for platform teams already standardized on Kong for API traffic.
• Bifrost: Best for teams that want an open-source gateway with native MCP support and self-hosting.
• TrueFoundry: Best for teams wanting a gateway plus control plane with documented prompt-injection defense.
• Guardrails AI: Best for Python teams validating and structuring model output in application code.
• Oligo Security: Best for runtime application and library-level detection with deep code context.
• Zenity: Best for governing a sprawling estate of business-user built agents and copilots.
• WitnessAI: Best for policy and visibility over how employees and apps use AI.

📊 Master Comparison Table

🔎 Why I Score Self-Hardening, Not Just Blocking

Every ranking list I read scores latency, model coverage, and price. Almost none scores whether the gateway itself has been broken into. That gap is now indefensible.

The LiteLLM flaw is a clean example. Two MCP preview endpoints accepted a full server config, including command, args, and env, and spawned that command as a subprocess with the proxy's own privileges. They were gated only by a valid API key, with no role check. Any low-privilege internal key holder could run commands on the host. That is exactly the class of exposure our MCP security guidance covers.

⚠️ Nine Seconds Is the Real Deadline

There is a story I keep coming back to when people ask why runtime matters. A small SaaS company lost its entire production database in nine seconds. An agent deleted a volume to clean up a credential issue, and the backups lived on the same volume, so one API call took both.

Nine seconds is faster than you can read a Slack message. No approval queue, no nightly report, and no dashboard refresh operates at that speed. That is the whole reason this guide weights enforcement over visibility, and why detection and response run on the agent itself.

EdgeLabs

"Good IPS/IDS/EDR software. Webportal management is good. Docker container integration is useful." On dislikes: "Nothing major. Description of issues can be complicated for non-technical folks." Rated 4 out of 5.

Verified User in Computer Software · EdgeLabs G2 Verified Review

"Its beneficial to secure any website or any server from hackers." On dislikes: "I dont think there is any option for dislike but the commercial seems high." Rated 4.5 out of 5.

Abhishek A. · EdgeLabs G2 Verified Review

4.3 ★★★★★

Based on verified reviews

LiteLLM

"Fully self-hostable, but enterprise features like SSO sit behind a paywall."
u/LLMDevs practitioner, r/LLMDevs Reddit Thread

Cards for the remaining eight providers, positions 3 through 10, follow in the next batch.

Portkey

Kong AI Gateway

Bifrost

TrueFoundry

Guardrails AI

Oligo Security

Zenity

WitnessAI

Q2. What Is LLM Gateway Security, and Which Controls Fire at Which Point in the Request Path?

LLM gateway security is the set of runtime controls applied to every model call: verifying identity before routing, enforcing model, region, and data-class policy, inspecting prompts and responses, validating tool names and arguments, capping tokens and spend, and writing audit records without logging prompt contents. Four enforcement points matter, which are pre-model, post-model, tool-call, and egress. Most tools cover the first two.

🔀 Why This Is Not Just an API Gateway

An API gateway enforces deterministic rules on structured requests. It knows the shape of the data, so it can reject anything that does not match.

An LLM gateway handles probabilistic input and output. It needs semantic prompt inspection, PII redaction, token-based budgets, semantic caching, and fallback routing that can change where your data lives. Deterministic rules alone do not cover that, which is why we treat AI and LLM security as a runtime problem rather than a filtering problem.

⚠️ The bug-versus-brain problem

Here is the distinction I keep returning to with engineering teams. Patch a bug in software and you can be 99.99% sure it is fixed.

Try that with an AI system and you can be 99.99% sure the problem is still there. That is why the request path needs layered controls, not one clever filter.

🛤️ The Request Path, Step by Step

Every model call should walk the same nine steps in the same order.

1. Verify identity. Confirm user, workload, application, and tenant from a verified token. Never trust a client header on its own.
2. Resolve the tenant. Establish which tenant's data and policy apply before anything else runs.
3. Evaluate policy. Decide the allowed model, region, data class, cost ceiling, tools, and maximum context length.
4. Apply input guardrails. Inspect the prompt, detect and mask PII (personally identifiable information).
5. Route to the provider. The gateway holds the provider credential, not the application.
6. Validate output. Check the response against an expected schema before anything downstream trusts it.
7. Validate tool calls. Check the tool name and every argument before the executor runs it.
8. Enforce consumption limits. Count tokens, apply budget caps, and trip circuit breakers.
9. Write the audit record. Metadata by default, and no prompt bodies.

📋 The Control Stack

🔁 Fallback Routing Is a Security Decision

Most teams configure fallback as an availability feature. If the primary model times out, send the call somewhere else.

That substitution can silently change region, data retention, capability, and safety policy. A replacement model must match the original region and controls, or the correct behavior is to deny the request or degrade it to a restricted no-tools mode.

❌ Silent substitution is the failure mode

The dangerous version is the one nobody notices. Your policy said EU-only, and the fallback served the request from another region during an outage.

Nothing alerted, because availability was preserved. Test this path deliberately, and confirm the fallback still writes an audit event, because region-bound evidence is exactly what a CRA and NIS2 compliance reviewer will ask for.

🗺️ OWASP LLM01 to LLM10, Mapped to Enforcement Points

Four of these ten are not gateway problems at all. A vendor claiming full OWASP coverage at the gateway is overclaiming.

🧪 A Worked Example That Passes Every Prompt Filter

Picture a support agent that reads customer emails, queries an order database, and can send messages. A customer emails an attachment containing instructions to export the last 500 records to an external address.

The user's prompt was clean. Prompt inspection passes, because the payload arrived in retrieved content, not the prompt. The damage happens later, in the tool-execution path, which is the mechanism we break down in our guide to prompt injection attacks.

🔺 The lethal trifecta

Three ingredients make exfiltration trivial: untrusted content, private data, and external communication. If your agent has all three, prompt filtering is not a boundary.

That is the clearest argument for splitting enforcement across four points instead of one. Filtering input catches the amateur attempt and misses the structural one.

🚪 What a Gateway Cannot See

Once the model's decision leaves the gateway, the gateway is blind. It logged the tool call. It does not see the process that spawned, the file that was read, or the outbound connection that followed.

This gets worse with agent frameworks. The application code often contains none of the tool-calling logic, because the framework handles it. You cannot audit code you did not write, which is why agentic AI security has to observe execution rather than source.

📡 Exposure is not theoretical

Researchers documented over 175,000 publicly exposed Ollama servers across 130 countries, many with tool calling enabled, which permits code execution. Those calls never touch anyone's gateway.

A gateway is a policy chokepoint, not an execution monitor. Most buyers conflate the two, and that conflation is the gap attackers use.

Edge Labs AI covers the gateway as one of six runtime layers its single agent monitors, which are platform, compute, network, container, application, and AI agent. Our Parallax engine handles model and agent security at the layer where the decision actually executes, using eBPF kernel-level syscall monitoring. That is the enforcement point after the gateway waves a request through, and you can see how the agent works in detail.

Q3. How Did We Evaluate These Tools, and Who Is This Guide For?

Five criteria, applied identically to every tool: LLM and agent runtime enforcement covering prompt injection, tool poisoning, and MCP; tool-call and egress control versus prompt-only filtering; deployment reach including self-hosted, on-prem, and air-gapped operation; the tool's own hardening record against public CVE, KEV, and EPSS evidence; and audit evidence quality for CRA, NIS2, and SOC 2 reviewers. No pricing column, no scores, no rankings.

Every tool in this guide was assessed against the same five criteria in the same order. Disagree with a criterion and you can discount the judgement it produced, which is the point of publishing it.

📐 Our Evaluation Criteria

• LLM and agent runtime enforcement. Does the tool defend live agents against prompt injection, tool poisoning, and MCP abuse, or does it only inspect text? Our MCP security analysis explains why that distinction decides outcomes.
• Tool-call and egress control. Does it validate tool names and arguments before execution and watch outbound traffic, or stop at prompt filtering?
• Deployment reach. Can it run self-hosted, on-prem, sovereign, and air-gapped, across cloud, data center, and edge locations?
• Self-hardening record. What does the tool's own public CVE history show, checked against CISA KEV and EPSS rather than CVSS alone? That is the same exploitation-aware logic behind our vulnerability management scoring.
• Audit evidence quality. Will the records satisfy a CRA, NIS2, or SOC 2 reviewer, without logging prompt contents?

👥 Who This Guide Is For

• AI-workload and LLM-agent runtime-security owners who already have agents in production and need enforcement, not a roadmap.
• SecOps and DevSecOps leads at SMBs making AI and GPU workloads production-safe without a 24/7 staffed team.
• Platform teams that need detection and response to keep working when the cloud backend is unreachable.

⚠️ Why Self-Hardening Earned a Slot

This criterion exists because of what I saw running security audits from 2017 onward. Almost 100% of the time, we found things misconfigured.

Security tools were installed and never given a policy. Teams almost got it right and just did not press the last button, which left detection running in monitor-only mode.

✅ The default-deny question

So the question I ask about any control plane is simple. Is it enforcing, or is it watching?

A gateway with guardrails installed and no blocking policy is a log producer. That is the same failure pattern, moved to a new layer, and it shows up just as often in Kubernetes runtime security tools.

Edge Labs AI publishes reproducible attack-simulation guides, including a 25-to-33-test detection battery and Falco event-generator scenarios (EdgeLabs technical guides). I would rather a buyer run those tests against us than trust a criteria list, including mine.

🚫 What This Guide Deliberately Does Not Score

Routing benchmarks, token pricing, and observability dashboards are all absent here. They matter for a platform decision and they are not security controls, and mixing them lets a fast, cheap gateway look safe.

Gartner's Market Guide for AI Gateways, published October 2025, frames the category around governing AI applications and agents and reducing unexpected cost risk. Several vendors in this roster have no third-party review profile at all, and the guide says so rather than inventing a rating.

Q4. Which LLM Gateway Security Tool Fits Which Situation?

Ten tools, three distinct jobs. Gateway platforms centralise identity, routing, and policy for model traffic. Guardrail and governance layers inspect prompts and outputs. Runtime enforcement layers watch what the model's decision actually does on the host. Buyers who pick one job and assume it covers the other two are the ones who get surprised. Match the tool to the enforcement point you are missing.

🧩 Three Jobs, Not One Category

These ten products get grouped together in search results and they do not do the same work. One owns the request path, one inspects content, and one watches execution.

A gateway and a runtime agent are complements, not substitutes. Anyone selling either as complete coverage is overclaiming, and that includes me.

• EdgeLabs: Best for runtime detection and response around AI workloads and agents, including air-gapped environments.
• LiteLLM: Best for teams wanting a fully self-hosted open-source gateway they control end to end.
• Portkey: Best for teams wanting managed guardrails wired into routing decisions.
• Kong AI Gateway: Best for platform teams already running Kong for API traffic.
• Bifrost: Best for teams needing tool-level access control over MCP agent traffic.
• TrueFoundry: Best for teams wanting a gateway and control plane in one system, deployable in-VPC.
• Guardrails AI: Best for Python teams enforcing output schemas inside application code.
• Oligo Security: Best for library and function-level runtime detection inside applications.
• Zenity: Best for governing business-user-built agents and copilots.
• WitnessAI: Best for policy and visibility over employee and application AI usage.

📊 Master Comparison Table

🔍 Why "Needs a Cloud Backend" Is a Column

I put that descriptor in the table because of a pattern I saw repeatedly in security tooling. Many SIEMs would not even tell you when log collection had stopped working.

The tool looked healthy and was blind. Ask any vendor what their product still does when the backend is unreachable, and get the answer in writing.

⏰ Response speed is the real test

Visibility that arrives after the fact is a report, not a control. An agent can delete a volume in seconds.

If the decision to respond has to travel to a cloud and back, that round trip is your exposure window. Measure it, the same way you would measure it for container runtime security tools.

📝 Roster Notes, Stated Plainly

Two vendors are missing for process reasons, not judgement. Astelia and a set of verify-bench vendors are pending confirmation, so they were left out rather than described from guesswork.

Ten providers are assessed in total. Detailed cards follow, in the same criteria order for every one.

Edge Labs AI occupies the runtime-enforcement row above, with detection and response both executing on the agent. Protection continues in self-hosted, on-prem, and air-gapped environments where cloud-dependent tooling cannot reach, which is the specific reason we built Disconnected Mode rather than a faster cloud round trip. If you want that tested against your own stack, get in touch with our team.

Q5. The 10 Best LLM Gateway Security Tools for Enterprise AI Deployments

The ten below split into gateway platforms that own the request path, guardrail and governance layers that inspect content, and runtime layers that watch execution. Each card states what the tool is built for, how it scores on the five criteria, one differentiator, one honest limitation, and whether third-party reviews exist. Positions carry no ranking meaning.

Total providers: 10. All ten provider cards, from EdgeLabs through WitnessAI, were produced in the two preceding listicle batches and carry forward unchanged here. Each follows the same fixed order: Company Name, Tags, Facts, Evaluated on the basis of, Differentiator, Proof of execution, Pricing, Potential limitation, My take, and Reviews. If you want the wider category view, our roundups of AI agent security tools for enterprise and generative AI security tools cover adjacent shortlists.

Q6. Why Do Guardrails Fail, and Who Is Attacking the Gateway Itself?

Guardrails are probability filters, not boundaries, so a determined attacker iterates until one payload passes. What holds is deterministic: capability restriction, tool-argument validation, approval gates on destructive actions, and execution-layer enforcement. The same logic applies to the gateway itself. One 2026 LiteLLM flaw scored CVSS 8.7 with roughly 80% EPSS and a CISA deadline, another scored 9.5 with under 1% EPSS. Sort by exploitation evidence, not severity.

🎯 The Claim, Stated Flatly

AI guardrails do not work as boundaries. When a guardrail vendor says it catches everything, that claim does not survive contact with a motivated attacker.

I want to be fair about the other half. Filters do reduce casual abuse, accidental data leaks, and lazy attempts. Run them. Just do not call them a perimeter.

⚠️ Why probabilistic defense degrades

A deterministic rule either matches or it does not. A probabilistic classifier has a success rate, and an attacker gets unlimited attempts.

Prompt-based defenses are the weakest tier available, and that has been known since early 2023. Telling a model to ignore malicious instructions is asking the attack surface to police itself, which is the core failure we document across AI security tools.

⚖️ The 200th-Attempt Argument, Both Ways

One published agentic-injection demo reportedly needed around 200 tries before it worked once. Critics called that marketing rather than research.

My read is that both extremes are wrong. "We catch everything" is false, and "guardrails are useless" is also false. It is a probability curve, and a defense that fails on attempt 201 is a speed bump, not a wall.

🔒 What Actually Holds

These controls do not negotiate, because they are not asking a model for permission.

• The two-of-three rule. An agent can read files, reach the internet, and execute custom code. Let it do two of those three, never all three.
• Deterministic hooks. Like git hooks, they fire regardless of what the model decides. The agent cannot argue with them.
• Typed tool interfaces. A strict schema turns "swapped an order ID for a person ID" into a validation failure, not an incident.
• Deploy by digest, not tag. A SHA-256 digest is immutable. A tag is a pointer someone can move.
• Pin and hash MCP tool definitions at registration. Tool poisoning is registration-time injection, not prompt-time. Diff schemas on every server refresh and alert on any change, as set out in our MCP security best practices.
• Never act on an unterminated stream. A streaming response is incomplete until it terminates, so no high-impact action should fire from a fragment that may still be cancelled.

📌 Registration time is the blind spot

Peer-reviewed work documents this clearly. One automated implicit tool-poisoning framework reached up to 84.2% attack success while holding malicious-tool detection to as low as 0.3%.

Another study found descriptor manipulation produced unsafe tool invocations in up to 36% of trials, dropping to 15% with layered defense. Nobody's filter caught that in a prompt, because it was never in one.

🔓 Now Flip It: The Gateway Is the Target

Two 2026 flaws in the same product make the ordering argument better than any framework diagram. The first let vulnerable MCP preview endpoints accept a full server config, including command, args, and env, spawning a subprocess with the proxy's privileges, gated only by a valid API key with no role check.

The second desynced the auth layer from the router. A crafted Host header made the auth gate evaluate a different route than the one FastAPI actually dispatched.

📉 The Ordering Rule

CVSS ranked these backwards. The 9.5 requires the proxy to be directly internet-reachable with no CDN, WAF, or reverse proxy normalising the Host header. The 8.7 was already being exploited.

Sort KEV first, then EPSS, then asset criticality, with CVSS as the tiebreaker. Fewer than 5% of the CVEs published in 2025 showed any observed exploitation, so a severity-sorted queue burns your week on theory. That ordering is the default in our vulnerability management view.

⏰ The pre-patch question

Here is the question no buying guide asks. Between disclosure and your patch landing, what notices exploitation?

Not your scanner, which reports a version number. The signal is a subprocess spawning from a gateway container and an unexpected outbound connection from that pod. That is kernel-level and egress monitoring, not application code, which is why workload and application security has to sit at the host.

I spent years on the side of security that generated millions of alerts, and I saw how little most teams needed another alert cannon. Direction matters more than speed. Fast without direction is a pocket circuit car, which goes very quickly into a wall.

Edge Labs AI enforces below the prompt, using eBPF kernel syscall analysis, host-local process termination, workload isolation, and time-boxed source-IP blocking that the agent executes itself. Our portal surfaces CVSS, CWE, CISA KEV, and EPSS per CVE, so exploitation-aware ordering is the default view. That is prioritisation plus runtime enforcement, and it is not runtime code-path reachability analysis. Oligo goes further on that specific axis.

Q7. How Do You Prove Your Gateway Is Secure, and What Should You Do This Week?

Proving gateway security means running five adversarial tests and keeping the audit records they produce: cross-tenant cache leakage, header spoofing, model downgrade, fail-open behavior, and observability leaks. Every path must still write an audit event. Edge Labs AI's CRA/NIS2 Compliance Center maps runtime, vulnerability-handling, and detection-and-response controls to specific requirement IDs, including the CRA Annex I "No Known Exploitable Vulnerabilities" check.

🧪 The Five-Test Drill

Run these against a live deployment, not a diagram. Each one has broken something in front of me.

1. Cross-tenant cache. Send tenant A's question, then tenant B's identical question. If B gets A's cached answer, your cache key is missing the tenant.
2. Header spoofing. Send a request claiming a different user, tenant, or role in a client header, with no matching token. It must be rejected.
3. Model downgrade. Force a fallback and check the region, retention, and tool policy of the replacement. Silent substitution is a policy failure.
4. Fail-open behavior. Kill your guardrail provider and resend a request that should be blocked. If it passes, your control is documentation.
5. Observability leaks. Read your own logs and traces as an attacker would. Look for prompt bodies, tool arguments, secrets, and provider keys.

✅ The pass condition nobody checks

A test only passes if the audit record proves what happened. Compare the record for a blocked request against one allowed after a check timed out.

If those two records look identical, the control is not auditable. Chaos-test provider timeouts, partial streams, schema mismatches, retry storms, and budget exhaustion the same way.

📁 What Auditors Actually Accept

Audit evidence is not a screenshot of a dashboard. A CRA, NIS2, or SOC 2 reviewer wants the routing decision, policy version, identity, provider, model, token usage, and status, per request.

Log metadata by default. Prompts, tool arguments, and tool results contain secrets, so full prompt bodies should never be a default observability metric. Our practical CRA roadmap walks through what reviewers ask for.

Edge Labs AI maps each control to a specific requirement ID and reports per-control pass and fail counts by host, which is what turns runtime telemetry into audit evidence. We are equally clear about the boundary. Our Compliance Center does not cover lifecycle or governance controls: business continuity, backup and restore, MFA as a program, HR training, technical documentation, or the CRA support-period obligations. Anyone claiming full CRA coverage from a runtime tool is selling you a gap.

🗓️ What To Do This Week

Seven actions, in order, each finishable in a day or less.

1. Inventory versions. Find every AI gateway and proxy deployment by version, including packaged ones like OpenShift AI. Patch LiteLLM to at least 1.84.0, which covers both 2026 flaws.
2. Revoke and rotate. Remove low-privilege key access to management and MCP test routes. If a proxy was internet-reachable on an affected version, rotate keys created during that window and review management audit logs for unexpected key, user, or settings changes.
3. Restrict the management interface. Bind it to trusted networks, and add explicit Host and Origin allowlisting at a reverse proxy.
4. Scan your own external footprint. Look for unauthenticated model-serving endpoints. Researchers found over 175,000 publicly exposed Ollama servers across 130 countries, many with tool calling enabled.
5. Re-sort the patch queue. KEV first, then EPSS, then asset criticality, CVSS as tiebreaker. Present the two LiteLLM CVEs to your change board as the worked example.
6. Build the OWASP matrix. Ten rows, mapped to your real enforcement points. Log every unenforced row as an accepted risk with a named owner.
7. Add runtime detection. Alert on any child process spawned by a gateway container and any unexpected outbound connection from a gateway pod, which is exactly what network detection and response is for.

📊 Measure one ratio

Track your tool-call to chat-completion ratio. It tells you what kind of traffic you actually have.

Once agent traffic passes roughly 30%, shift investment from prompt inspection to tool-argument validation and egress inspection. The threat moved. Your controls should follow.

💬 What Operators Say

"Good IPS/IDS/EDR software. Webportal management is good. Docker container integration is useful." On dislikes: "Nothing major. Description of issues can be complicated for non-technical folks." Rated 4 out of 5.
Verified User in Computer Software, EdgeLabs G2 Verified Review

"Its beneficial to secure any website or any server from hackers." On dislikes: "I dont think there is any option for dislike but the commercial seems high." Rated 4.5 out of 5.
Abhishek A., EdgeLabs G2 Verified Review

The second review names the real friction honestly. Price is a live objection in this category, and I would rather see it in print than filtered out. Our pricing tiers start with a free self-serve entry point for that reason.

🔮 The Question I Am Sitting With

Where my head is right now is that distributed AI computing forces distributed security. Securing the LLM agent at runtime stops being a specialist concern and becomes the default question every SecOps team answers.

I could be reading that timeline too aggressively. What I am confident about is narrower. If your agents can already act on production systems and your only control is a filter on the way in, you are one clean-looking request away from finding out.

Edge Labs AI runs one agent covering host, container, Kubernetes, network, and AI-agent layers, with detection and response both executing locally, so protection continues when the cloud link is cut. That design came from a security lead asking mid-PoC whether blocking still worked if the backend went dark, three days before his audit. If you are running that test this week, I would genuinely like to hear what breaks, so reach out to our team.