9 Best Zenity Alternatives for AI Agent Security in 2026

Q1: Why is securing AI agents suddenly the hardest problem on your desk? [toc=1. Why Agent Security Is Different]

AI agents no longer just answer questions. They act: reading files, calling tools, executing code, and touching production databases. That makes choosing an agent security tool a high-stakes call, because a runtime miss executes in seconds, not scan cycles. One agentic incident wiped an entire production database, and its backups, in nine seconds, when the agent deleted a shared volume to "clean up" a credential issue. I evaluated the alternatives below against four criteria: runtime enforcement versus governance-only, agent-only versus cloud-dependent operation, deployment reach, and category maturity. This guide is for AI-workload and LLM-agent runtime-security owners, and the SecOps leads standing beside them.

🎯 What this guide actually decides for you

I want to be honest about the frame before you read one card. Zenity is a strong, Gartner-recognized governance platform, and I am not here to pretend otherwise. What I am here to do is show you where governance stops and runtime enforcement starts, because that gap is where agents hurt you.

Runtime is the hero. You cannot secure what you only scanned. A clean posture report tells you what should be true at build time. It says nothing about what an agent does at 2 a.m. when a poisoned tool call convinces it to exfiltrate a secret. I have watched that distinction decide whether a PoC (proof of concept) survives its first real incident, which is exactly why runtime detection and response sits at the center of this comparison.

Our Evaluation Criteria

I picked criteria that matter for AI-agent security specifically, not a generic security checklist.

• Runtime enforcement vs governance-only. Does the tool stop an action, or just log and score it? Governance maps risk. Enforcement blocks the delete.
• LLM/agent runtime protection. Coverage for prompt injection (tricking an agent through untrusted input), tool poisoning (a malicious tool an agent calls), and MCP (Model Context Protocol, the standard agents use to reach tools).
• Agent-only vs cloud-dependent operation. Does detection and response keep working self-hosted, on-prem, or air-gapped, with no cloud backend? Or does it break without egress?
• GPU-cluster and inference-node coverage. Can it protect the actual AI compute, not just the SaaS agent surface?
• Category maturity. This space is young and thinly reviewed. I flag where a vendor has no third-party proof rather than inventing it.

Who This Guide Is For

• AI-workload and LLM-agent runtime-security owners securing agents and agentic systems in production.
• SMB SecOps and DevSecOps leads making AI and cloud workloads production-safe without a 24/7 team.
• GPU-cloud and AI-infrastructure security leads protecting inference nodes and clusters.

This guide is not for OT (operational technology) asset-visibility buyers, IoT-hardware security, or teams wanting build-time-only code scanning. Those are different jobs and different markets.

The 9 Alternatives at a Glance

No tool here ranks objectively first. Each fits a different situation.

• 1.1 EdgeLabs: Best for runtime, agent-only detection and response across AI workloads and agents with no cloud dependency (self-hosted, on-prem, air-gapped).
• 1.2 Zenity: Best for buildtime-to-runtime governance across SaaS agent platforms like Microsoft Copilot Studio and Salesforce Agentforce.
• 1.3 Oligo Security: Best for library-level runtime application protection and catching exploited code in execution.
• 1.4 WitnessAI: Best for governing how employees and agents use AI, and enforcing policy on data inside prompts.
• 1.5 Astelia: Best for emerging, agent-specific runtime controls in early-stage agentic deployments.
• 1.6 Microsoft open-source agentic-security project: Best for teams standardizing on Microsoft agent tooling (exact repo name pending client confirmation before publish).
• 1.7 AgentSystems (local open runtime): Best for running third-party agents on your own infrastructure without sending data to the creator.
• 1.8 Guardrails AI: Best for validating and structuring LLM outputs in code before they reach a user.
• 1.9 SonarQube Advanced Security: Best for automating CRA (EU Cyber Resilience Act) evidence and release gates at build time.

📊 Master comparison table

💬 Where EdgeLabs actually sits

I put EdgeLabs at 1.1 because this guide lives in its lane: runtime security for AI workloads and agents, with detection and response running entirely on the agent. When we first shipped agent-only response that keeps working with the cloud link cut, the question a security lead kept asking, mid-PoC, was blunt. Would the agent still block an attack if the backend went dark, three days before an audit? That is the gap governance-only tools leave open, and it is the whole reason the next cards exist.

Now to the cards. Total providers: 9. Below are the first two. The rest follow in the next batch.

1 out of 9

EdgeLabs

Runtime detection & response Agent-only, no cloud dependency AI/agent + Kubernetes security

Detection & Response Model

Agent-only runtime detection AND response (eBPF kernel-level)

AI/Agent Engine

Parallax (LLM proxy/firewall: prompt-injection and tool-poisoning defense)

Deployment

Kubernetes DaemonSet, single privileged container, no sidecars

Footprint

Typical under 5% CPU and under 500 MB RAM (EdgeLabs' own published claim)

Evaluated on the basis of

• Runtime enforcement vs governance-only: Enforces at runtime; in-line, host-local IP blocking fires even with no cloud backend (Disconnected Mode).
• LLM/agent runtime protection: Parallax defends live agents against prompt injection, tool poisoning, and MCP tool-call abuse; input/output alignment and hallucination checks.
• Agent-only vs cloud-dependent: Detection AND response run on the agent; works self-hosted, on-prem, and air-gapped with zero data egress.
• GPU-cluster and inference-node coverage: Secures GPU workloads using only CPU (no GPU tax); eBPF syscall monitoring catches cryptojacking on shared GPU nodes.
• Category maturity: Established enterprise product now opening an SMB self-serve or open-source motion; young in the agentic niche but shipping in production.

Differentiator

One eBPF-based agent consolidates NDR, EDR, IPS/IDS, container and Kubernetes security, vulnerability management, and AI/agent security. Exploited-vulnerability and explainability scoring (CVSS, EPSS, CISA KEV, and CWE) ties into a shipping CRA/NIS2 Compliance Center (control-to-requirement-ID mapping, the CRA Annex I "No Known Exploitable Vulnerabilities" check).

Proof of execution

• Deployed as a DaemonSet across a customer GKE cluster, it flagged a privileged-pod escape on day one.
• eBPF syscall detection on shared GPU nodes caught cryptojacking that never touched the customer's GPU budget.
• Published Trader Joe's case study (EdgeLabs' own): 83% MTTR reduction, 573% ROI, roughly $1.3M operational savings, deploy in under two weeks, 0 outages.

Pricing

Custom or usage-based, with a free self-serve tier (intentionally limited vs enterprise).

Potential limitation

The Compliance Center covers runtime, vuln-handling, and detection-and-response controls, NOT lifecycle, governance, or program controls (backup, MFA-as-a-program, HR training, technical documentation, CRA 5-year and 10-year support obligations). The free or SMB tier is deliberately narrower than enterprise. EdgeLabs does NOT do OT visibility, IoT-hardware, or "edge security."

My take

If you run agents where the cloud can't always reach (self-hosted, on-prem, air-gapped), agent-only detection AND response is the criterion that matters most, and it is where I'd start. If your agents live entirely inside managed SaaS suites, weigh a governance-first platform alongside it.

"Good IPS/IDS/EDR software. Webportal management is good. Docker container integration is useful." (Dislikes: "Description of issues can be complicated for non-technical folks.")

Verified User in Computer Software, 4/5 · EdgeLabs G2 Verified Review

"It's beneficial to secure any website or any server from hackers." (Dislikes: "the commercial seems high.")

Abhishek A., 4.5/5 · EdgeLabs G2 Verified Review

4.6 ★★★★★

Based on verified reviews

2 out of 9

Zenity

AI agent governance AISPM + AIDR SaaS agent platforms

Detection & Response Model

Governance layer (observability, AISPM posture, AIDR detection)

Coverage

Copilot Studio, Agentforce, ChatGPT Enterprise, and more

Lifecycle

Buildtime to runtime (governance-first)

Analyst signal

Gartner named it "Company to Beat" in AI agent governance

Evaluated on the basis of

• Runtime enforcement vs governance-only: Strong governance, posture, and detection; leans toward mapping and monitoring rather than agent-side inline enforcement.
• LLM/agent runtime protection: Covers prompt injection, tool poisoning, and data exfiltration across supported agent platforms; aligns policy to OWASP LLM Top 10 and MITRE ATLAS.
• Agent-only vs cloud-dependent: Cloud-oriented delivery; not built for air-gapped, zero-egress operation.
• GPU-cluster and inference-node coverage: Focused on the SaaS and enterprise agent surface, not GPU or inference-node protection.
• Category maturity: A recognized leader in agent governance with strong analyst standing.

Differentiator

End-to-end discovery, posture management, and detection for AI agents across major enterprise SaaS agent platforms, backed by Gartner recognition.

Proof of execution

• Gartner independently named Zenity the "Company to Beat" in AI agent governance.
• Publicly documented agent-security research, including a file-system access finding on a browser agent (disclosed after repeated attempts before it succeeded).

Pricing

Custom or enterprise; not publicly listed.

Potential limitation

Documented gaps include no offensive red teaming and limited coverage of homegrown agents built on frameworks like LangChain, CrewAI, or AutoGen. Cloud-oriented, so it is not designed for self-hosted, on-prem, or air-gapped runtime enforcement.

My take

If your agents live inside managed SaaS suites and governance breadth is the priority, Zenity is a credible benchmark. If you need enforcement that keeps working with no cloud backend, or coverage for framework-built agents and GPU nodes, pair it with a runtime tool or look elsewhere.

3 out of 9

Oligo Security

Runtime application security Library-level detection Cloud & Kubernetes apps

Detection & Response Model

Agent-based runtime application detection (library/function-level)

Focus

What code actually executes at runtime, not just what is installed

Prioritization angle

Highlights vulnerabilities in libraries that are actually loaded and used

Deployment

Cloud and Kubernetes application workloads

Evaluated on the basis of

• Runtime enforcement vs governance-only: Runtime-focused; watches libraries in execution rather than scoring posture on paper.
• LLM/agent runtime protection: Strong on application and library runtime risk; less centered on prompt injection, tool poisoning, or MCP tool-call defense.
• Agent-only vs cloud-dependent: Uses an in-environment agent; verify air-gapped and zero-egress operation directly with the vendor.
• GPU-cluster and inference-node coverage: Application-runtime focused; GPU and inference-node protection is not its core pitch.
• Category maturity: A credible runtime application security name, frequently cited in the Zenity-alternatives conversation.

Differentiator

Ties vulnerability risk to what libraries the application actually loads and runs, so teams chase exploited, in-use code instead of a full CVE (Common Vulnerabilities and Exposures) backlog. Our own exploited-vulnerability scoring takes a comparable execution-aware view.

Proof of execution

• Positioned as a runtime application security alternative in independent Zenity-alternatives roundups.
• Public technical material centers on library-level runtime detection and execution-context prioritization.

Pricing

Custom or not publicly listed.

Potential limitation

Its center of gravity is application and library runtime risk, not agent-specific threats like prompt injection, tool poisoning, or MCP abuse. Confirm agentic coverage against your actual agent stack before you assume it fits.

My take

If your risk is "which vulnerable library is actually running in my app," Oligo speaks your language. If your risk is "my LLM agent got tricked into a bad tool call," you'll want an agent-runtime layer alongside it. I could be off on the newest features, so verify their current agentic roadmap directly.

4 out of 9

WitnessAI

AI usage governance Prompt-data policy Cloud-delivered

Detection & Response Model

AI usage governance and policy enforcement layer

Focus

How people and agents use AI, and what data goes into prompts

Primary control

Policy on inputs and outputs to AI apps and agents

Deployment

Cloud-delivered, positioned in front of AI apps and agents

Evaluated on the basis of

• Runtime enforcement vs governance-only: Enforces usage and data policy in the request path; oriented to governance of AI usage rather than kernel-level workload defense.
• LLM/agent runtime protection: Guards prompt-data exposure and AI usage; less about container escape, syscall abuse, or GPU-node threats.
• Agent-only vs cloud-dependent: Cloud-delivered; not built for air-gapped, no-egress runtime enforcement.
• GPU-cluster and inference-node coverage: Not its focus.
• Category maturity: A recognized name in AI governance, though thinly reviewed by third parties.

Differentiator

Focuses on visibility and policy over how employees and agents actually use AI, including what sensitive data lands inside prompts.

Proof of execution

• Public positioning centers on AI usage governance and prompt-data policy enforcement.
• Appears in the broader AI-security governance conversation alongside agent platforms.

Pricing

Custom or not publicly listed.

Potential limitation

Governance of AI usage is a different job from runtime workload defense. It won't catch a privileged-pod escape or a poisoned tool call executing on a node. Verify the exact vendor name and scope against your requirement, since governance vendors overlap confusingly.

My take

If your first problem is "I don't know what my staff and agents are feeding into AI," WitnessAI is aimed right at that. If your first problem is "an agent could execute something dangerous on my infrastructure," that's a runtime-enforcement job, not a usage-policy one.

5 out of 9

Astelia

Agentic-AI security Early-stage category Agent runtime controls

Detection & Response Model

Agent-focused runtime controls (early category, limited public detail)

Focus

Security controls specific to autonomous and semi-autonomous agents

Maturity

Emerging entrant in a young category

Deployment

Varies by deployment; not publicly detailed

Evaluated on the basis of

• Runtime enforcement vs governance-only: Positioned toward agent runtime controls; public detail is limited, so confirm what it enforces versus observes.
• LLM/agent runtime protection: Agent-specific by design; validate exact coverage of prompt injection, tool poisoning, and MCP against your stack.
• Agent-only vs cloud-dependent: Not publicly documented; ask directly.
• GPU-cluster and inference-node coverage: Not publicly claimed.
• Category maturity: Early. Treat vendor claims as a starting point for your own testing.

Differentiator

A newer, agent-specific entrant built for the agentic era rather than retrofitted from a legacy security product.

Proof of execution

• Appears among emerging agentic-security names in the alternatives conversation.
• Limited public, primary-source technical documentation at this stage.

Pricing

Not publicly listed.

Potential limitation

This is a young category, and Astelia is thinly documented in public. There are no third-party reviews yet, so run your own proof of concept and demand specifics before you commit budget. I'd rather flag that honestly than pretend the evidence exists.

My take

Worth a look if you want an agent-native option and you're willing to test it yourself. Don't buy on the category label alone; make them show you enforcement against a live, poisoned tool call.

🔒 Where runtime enforcement closes the loop

Look across these nine and a pattern shows up. Governance tools map risk, isolation runtimes contain it, output libraries filter it, and build-time scanners gate it. Each is useful. None of them, on its own, watches what an agent actually does on your infrastructure at 2 a.m.

That gap is the reason we built EdgeLabs the way we did. Detection AND response run on the agent itself, using eBPF (a Linux kernel technology that safely observes system calls) to catch things like fileless execution, container escapes, and cryptojacking on GPU nodes. When we shipped Disconnected Mode, the point was simple: the block still fires when the cloud link is cut, three days before an audit.

I'll stay honest about the boundary. EdgeLabs does not do OT visibility, IoT-hardware, or "edge security," and our Compliance Center covers runtime and vulnerability-handling controls, not lifecycle or program controls like backup or HR training. If your agents live entirely inside managed SaaS suites, a governance-first platform may fit better. If they run where the cloud can't always reach, agent-only runtime detection and response is where I'd start.

Q2: What exactly does Zenity's AI agent security platform do, and where does it stop? [toc=2. What Zenity Does & Its Limits]

Zenity is an end-to-end AI agent security and governance platform. It discovers, governs, and defends agents from buildtime to runtime across Microsoft Copilot Studio, Salesforce Agentforce, and ChatGPT Enterprise. It combines AI observability, AI Security Posture Management (AISPM), and AI Detection and Response (AIDR). Gartner named it the "Company to Beat" in AI agent governance as of April 17, 2026. Its documented gaps: no offensive red teaming, and limited coverage of homegrown agents built on frameworks like LangChain, CrewAI, or AutoGen.

🧩 The three pillars, in plain English

Think of Zenity as three jobs stacked together. First, observability: it finds every AI agent running across your SaaS apps, custom apps, and endpoints, including shadow AI nobody registered. Second, posture (AISPM): it checks how those agents are configured and where they drift from policy.

The third job is detection and response (AIDR). This is the runtime layer that watches agent behavior and flags actions an agent was never meant to take. Zenity's own framing is blunt: prompt filtering alone misses the real risk, which is an agent connected to your CRM or email taking a silent, unauthorized action.

📋 Why the framework alignment matters

Credibility in this category comes from mapping to shared standards, not marketing claims. Zenity aligns its work to the OWASP LLM and Agentic Top 10, and it has contributed research to MITRE ATLAS. ATLAS is MITRE's public knowledge base of real attacks on AI systems.

That framework grounding is why the Gartner recognition carries weight. Gartner said its assessment looked at technical capabilities, customer deployments, business model, and ecosystem strength, not a survey. For a buyer, that means Zenity is a legitimate benchmark, not a hype cycle entry.

🔍 What it looks like on one real surface

Take Copilot Studio, Microsoft's tool for building custom agents. Zenity discovers each agent someone builds there, maps what data and systems it can reach, and watches its runtime behavior for intent that breaks policy. That is the governance loop working end to end on a single platform.

I'll be fair here. This is genuinely strong work on the SaaS agent surface, and I am not going to pretend otherwise.

⚠️ Where it stops, and why alternatives exist

Two gaps come up consistently in independent analysis. Zenity does not do offensive red teaming, meaning it will not proactively attack your agents to find weaknesses. And its coverage of homegrown, framework-built agents (LangChain, CrewAI, AutoGen) is limited.

There is a third boundary worth naming. Zenity is cloud-oriented and built around SaaS agent platforms, so it is not designed for self-hosted, air-gapped, no-egress runtime enforcement. That gap is exactly where a runtime-first, agent-only platform like EdgeLabs sits, and it is the reason this guide exists rather than ending at Zenity.

Q3: How should you choose a Zenity alternative, and who is this guide for? [toc=3. How To Choose & Who It's For]

Judge every alternative on five axes: runtime enforcement versus governance-only; LLM and agent runtime protection against prompt injection, tool poisoning, memory poisoning, and MCP or A2A abuse; agent-only versus cloud-dependent operation including air-gapped reach; GPU-cluster and inference-node coverage; and category maturity, since many vendors are early and thinly reviewed. This guide is for LLM-agent runtime-security owners, SMB SecOps and DevSecOps leads, and GPU-cloud security leads, not OT/ICS asset-visibility buyers.

🧭 The contrarian starting point

Here is where the standard read gets it backwards. Most buyers start by comparing governance dashboards. I'd start by asking one question: when an agent tries something dangerous, does the tool stop it, or just log it?

A rule written in a config file is not enforcement. In one documented incident, an agent read its safety rules, acknowledged them, and then ignored them anyway. A permission system that physically cannot perform the action is a guardrail. A polite instruction is not.

✅ Our evaluation criteria

Five axes, and why each one earns its place.

• Runtime enforcement vs governance-only. Does it block the action, or map and score the risk? Governance tells you what could go wrong. Enforcement stops it mid-execution.
• LLM and agent runtime protection. Coverage for prompt injection (tricking an agent through input), tool poisoning (a malicious tool it calls), memory poisoning (corrupting its stored context), and MCP or A2A abuse. MCP (Model Context Protocol) and A2A (agent-to-agent) are how agents reach tools and each other.
• Agent-only vs cloud-dependent operation. Does detection and response keep working self-hosted, on-prem, and air-gapped, with no cloud backend? Or does cutting egress break it?
• GPU-cluster and inference-node coverage. Can it protect the actual AI compute, not just the SaaS agent surface?
• Category maturity. This space is young. I flag vendors with no third-party proof instead of inventing it.

👥 Who this guide is for

• AI-workload and LLM-agent runtime-security owners securing agents and agentic systems in production.
• SMB SecOps and DevSecOps leads making AI and cloud workloads production-safe without a 24/7 team.
• GPU-cloud and AI-infrastructure security leads protecting inference nodes and clusters.

This guide is not for OT (operational technology) asset-visibility buyers, IoT-hardware security, or teams wanting build-time-only code scanning. Those are different jobs.

⏰ A five-minute test you can run today

Here is a heuristic I keep coming back to. An agent can usually do three risky things: touch files, reach the internet, and execute code. Let it do only two.

That single constraint shrinks the blast radius more than most policy pages. It is the kind of enforcement-first thinking behind how we built EdgeLabs, where detection and response run on the agent itself, so the block still fires even with the cloud link cut. The criteria above stay vendor-neutral on purpose, but that is the lens I'd hold each tool up against.

Q4: Which 9 Zenity alternatives should be on your shortlist? [toc=4. The 9 Alternatives At A Glance]

The strongest Zenity alternatives each fit a distinct situation, with no tool ranked objectively first. EdgeLabs fits runtime, agent-only detection and response with no cloud dependency. Oligo fits library-level runtime app protection, WitnessAI fits AI usage governance, and Astelia plus the Microsoft open-source agentic project fit emerging agent controls. The master table below compares each on detection-and-response model and deployment reach.

🗺️ The shortlist, by situation

Distributed AI computing forces distributed security. So I've sorted these by the situation each one actually fits, not by a made-up score.

• 1.1 EdgeLabs: Best for runtime, agent-only detection and response across AI workloads and agents with no cloud dependency (self-hosted, on-prem, air-gapped).
• 1.2 Zenity: Best for buildtime-to-runtime governance across SaaS agent platforms like Copilot Studio and Agentforce.
• 1.3 Oligo Security: Best for library-level runtime application protection and catching exploited code in execution.
• 1.4 WitnessAI: Best for governing employee and agent AI usage and enforcing policy on data in prompts.
• 1.5 Astelia: Best for emerging, agent-specific runtime controls in early-stage agentic deployments.
• 1.6 Microsoft open-source agentic-security project: Best for teams standardizing on Microsoft agent tooling (exact repo name to confirm before publishing).
• 1.7 AgentSystems (local open runtime): Best for running third-party agents on your own infrastructure without sending data to the creator.
• 1.8 Guardrails AI: Best for validating and structuring LLM outputs in code before they reach users.
• 1.9 SonarQube Advanced Security: Best for automating CRA evidence generation and release gates at build time.

📊 Master comparison table

💡 How to read this table

Watch the last two columns together. A tool can look strong on detection and still break the moment you cut its cloud connection.

That is the split that matters for regulated, self-hosted, or air-gapped teams. It is also why EdgeLabs sits at 1.1 for this guide's lane: detection and response run on the agent, so a poisoned tool call still gets blocked three days before an audit, with no backend to phone home to. Full per-vendor cards follow, so treat this as the map, not the territory.

Q5: How do the alternatives actually compare, provider by provider? [toc=5. Provider Breakdowns]

Each provider is assessed on the same five criteria: runtime enforcement versus governance-only; LLM and agent runtime protection (prompt injection, tool poisoning, MCP); agent-only versus cloud-dependent operation; GPU and inference-node coverage; and category maturity. EdgeLabs leads on runtime, agent-only detection and response that works self-hosted and air-gapped. Zenity leads on governance breadth across SaaS agent platforms, and the rest fit narrower runtime or usage-governance needs.

🧪 Why I score every tool the same way

I've watched too many comparisons quietly change the yardstick per vendor. So each card in the full breakdown uses the identical five criteria, in the same order. That is the only way a reader can tell a real gap from a marketing gap.

The tell I look for first is simple. When an agent tries something dangerous, does the tool watch what it actually did, or trust what a config file said it would do? One documented incident had an agent that fell into the "lethal trifecta": untrusted input, access to private data, and a way to send data out. Guardrails written as instructions did not stop it.

🗂️ How the roster splits

The nine group into four honest buckets. Reading them this way saves you a week of demos.

• Runtime detection and response: EdgeLabs (agent-only, no cloud dependency), and Oligo (library-level runtime application security).
• Governance and posture: Zenity (SaaS agent governance), and WitnessAI (AI usage and prompt-data policy).
• Emerging and open-source: Astelia, the Microsoft agentic-security project, and AgentSystems (local zero-egress runtime).
• Adjacent build-time and output controls: Guardrails AI (output validation), and SonarQube Advanced Security (build-time CRA gating).

Deterministic enforcement is the thread that separates the first bucket. A hook that fires at the kernel level does not ask the agent for permission. The agent cannot talk it out of firing.

⭐ Where EdgeLabs sits, in its own words and users'

EdgeLabs holds position 1 in this guide's lane because detection and response both run on the agent, with no cloud backend required. It consolidates NDR, EDR, IPS/IDS, container and Kubernetes security, vulnerability management, and AI/agent security into one eBPF-based agent, with the Parallax engine handling LLM and agent runtime defense. Those are EdgeLabs' own published claims, not independently benchmarked, and I'll say so plainly.

Two verified user notes, kept balanced:

"Good IPS/IDS/EDR software. Webportal management is good. Docker container integration is useful." (Dislikes: "Description of issues can be complicated for non-technical folks.")

Verified User in Computer Software, 4/5 EdgeLabs G2 Verified Review

"It's beneficial to secure any website or any server from hackers." (Dislikes: "the commercial seems high.")

Abhishek A., 4.5/5 EdgeLabs G2 Verified Review

The full per-vendor cards (1.1 through 1.9), each with facts, differentiator, limitation, and my take, sit in the roster section above. Competitors in this young category have few or zero third-party reviews, so I lead their cards with primary-source facts instead of manufacturing praise.

Q6: What separates the leaders, runtime enforcement, no-cloud reach, and exploitation-aware compliance? [toc=6. What Separates The Leaders]

Three things separate the leaders. First, runtime enforcement, deterministic hooks and kernel-level (eBPF) detection that watch what an agent does, not what a config file says, beats guardrails a determined attacker slips past. Second, agent-only operation keeps detection and response working self-hosted, on-prem, and air-gapped with zero data egress. Third, exploited-vulnerability scoring (CVSS + EPSS + CISA KEV) maps to CRA and NIS2 evidence instead of drowning teams in CVSS-only noise.

🧯 Guardrails are not enforcement

Here is the belief the category leans on: turn on AI guardrails and you're covered. I think that gets it backwards. As one practitioner put it, "we catch everything" is a complete lie, because a guardrail written as an instruction is a suggestion an agent can ignore.

Deterministic hooks work differently. They fire at the kernel level, and the agent cannot stop them, the way a trip wire does not negotiate. eBPF (a Linux kernel technology that safely observes system calls) is how we watch fileless execution, container escapes, and namespace abuse as they happen. Your Monday action: pick one agent and confirm whether its "safety rules" are enforced in the kernel or just written in a config file.

🔌 No-cloud reach is the quiet dealbreaker

Most alternatives in this space are SaaS-first. That is fine until your agents run somewhere the cloud can't reach: an on-prem cluster, a regulated enclave, an air-gapped node. Then a cloud-dependent tool goes quiet exactly when you need it.

A security lead once asked me, mid-PoC, whether the agent would still block an attack if the backend went dark, three days before an audit. That question is why we built Disconnected Mode, where full detection AND response continue offline. You can patch a bug, but you can't patch a brain, so the response has to live where the agent lives.

📑 Exploitation-aware compliance, not CVSS theater

Researchers published tens of thousands of CVEs in 2025, far more than any team can patch. CVSS ranks how scary a flaw looks on paper. It does not tell you what attackers are actually exploiting.

That is where EPSS (a FIRST.org model predicting exploitation probability) and the CISA KEV catalog (vulnerabilities confirmed exploited in the wild) come in. EdgeLabs ties this exploited-vulnerability scoring into a shipping CRA/NIS2 Compliance Center, mapping controls to requirement IDs and running the CRA Annex I "No Known Exploitable Vulnerabilities" check. I'll name the boundary too: that Compliance Center covers runtime, vulnerability-handling, and detection-and-response controls, not lifecycle or program controls like backup, HR training, or the CRA multi-year support obligations.

Q7: Which Zenity alternative fits your environment, and what do you do Monday morning? [toc=7. Which One Fits You]

Match the tool to your situation. Choose governance-breadth platforms if your agents live entirely inside managed SaaS suites. Choose agent-only runtime tools if you run self-hosted, on-prem, or air-gapped workloads where a cloud backend can't reach. Layer exploitation-aware vulnerability scoring if CRA or NIS2 evidence is on your roadmap. On Monday, inventory every MCP server and tool your agents can call, then test one for tool poisoning.

🧭 Three scenarios, three picks

Skip the feature grid and start with where your agents actually run.

• SaaS-only, governance-first. Your agents live in Copilot Studio, Agentforce, or ChatGPT Enterprise. A governance-breadth platform like Zenity is the natural fit.
• Self-hosted, on-prem, or air-gapped. A cloud backend can't reach your workloads. This is EdgeLabs' lane: detection and response run on the agent, so the block still fires with the link cut.
• Compliance-driven. CRA or NIS2 evidence is on your roadmap. Layer exploited-vulnerability scoring (CVSS + EPSS + CISA KEV) so you fix what is actually exploited, not what is scary on paper.

⏰ Your Monday-morning move

Do one concrete thing this week. Inventory every MCP server and tool your agents can call, then pick one and test it for tool poisoning.

An agent can usually touch files, reach the internet, and execute code. Let it do only two. That single limit shrinks the blast radius more than another policy page ever will.

Where my head is right now is this: over the next two years, distributed AI computing forces distributed security, and securing the agent at runtime stops being niche and becomes the default question every SecOps team answers. If you're wrestling with where your agents run and what they can reach, tell me what you're building. I'd rather trade notes on a real deployment than send you a demo link.