TL;DR
- AI security tools protect the AI itself, its models, LLM agents, and the infrastructure they run on, against threats like prompt injection, tool-poisoning, and excessive agency that have no malware signature.
- Protection matters most at runtime, where attacks actually execute; discovery and posture tools cover only build-time, leaving the thinnest-covered window that this guide focuses on.
- Guardrails filter probabilistically and can be bypassed, so real protection is deterministic and enforced outside the model, at runtime, where the agent physically cannot talk its way around it.
- We evaluated tools on runtime enforcement, agent-only no-cloud operation, LLM and agent coverage, shadow-AI discovery with GPU protection, and category maturity, deliberately excluding pricing.
- The roster spans EdgeLabs, Oligo, Zenity, WitnessAI, Astelia, and a Microsoft open-source project, split between enforcement tools and governance layers in a young, thinly reviewed category.
- No single tool makes you CRA or NIS2 compliant; runtime platforms cover concrete controls, but governance, business continuity, and staff training remain yours to own.
Q1. What are AI security tools, and why does protecting AI workloads and agents have to happen at runtime?
AI security tools are platforms that protect the AI system itself: the models, the LLM agents, and the infrastructure they run on. They defend against threats with no malware signature, like prompt injection, tool-poisoning, data exfiltration, and "excessive agency," where an autonomous agent takes a destructive action on its own. They span discovery, build-time posture, and runtime. Protection matters most at runtime, because a coding agent once deleted a company's entire database, backups included, in nine seconds.
🧠 What "AI security tool" actually means
Let me clear up the biggest confusion first. "AI security tools" splits into two very different jobs, and buyers conflate them constantly.
One job is using AI to defend traditional systems, like an AI-powered firewall or an EDR (Endpoint Detection and Response) agent. The other job, the one this guide covers, is protecting the AI itself: the model, the agent, and the compute it touches.
I care about the second job. When you hand an LLM agent (a language model that can take actions, not just chat) access to your data and your compute, it becomes a new kind of insider. It needs supervision while it runs, not just a pre-flight check. This is the core of our AI and LLM security approach.
🗂️ The main categories, in plain language
The market is young, so vendors slice it differently. Here is the taxonomy I use when a SecOps lead asks me to map the field.
- AI security posture management (AI-SPM): finds AI assets and misconfigurations before deployment.
- LLM and agent runtime enforcement: inspects prompts, tool calls, and outputs while the agent runs.
- Adversarial testing and red-teaming: attacks your model on purpose to find weak spots.
- AI-aware vulnerability scanning: flags exposed models, keys, and vulnerable libraries.
- Model supply-chain and SBOM tools: track what went into the model (SBOM means Software Bill of Materials, a parts list for your software).
- Runtime detection and response: catches and stops attacks during live execution.
🔄 Discovery, posture, then runtime: three windows, not one
Most tools crowd into the first two windows. They scan your setup, check your config, and hand you a clean report. Discovery and posture are build-time and theoretical.
Runtime is where the attack actually executes, and it is the thinnest-covered window in almost every roundup you will read. That gap is the whole reason this article exists, and it is why our workload and application security starts at execution.
⚠️ Why signatures fail here: the lethal trifecta
Traditional defenses hunt for a malware signature, a known-bad fingerprint. AI attacks have none. They are just context-rich text that manipulates the model's decision-making.
There is a clean way to see the risk. Security researcher Simon Willison calls it the "lethal trifecta": untrusted content, private data, and external communication. If your agent can read an untrusted email, reach your database, and send a network request, exfiltration is trivial.
A practical rule I share with teams comes from that same logic. An agent can access files, access the internet, and write and run its own code. You should let it do only two of those three things at once.
🎭 The contrarian part: guardrails are mostly theater
Here is where the standard read gets it backwards. The reflex is to bolt "AI guardrails" or prompt-based defenses onto the model and call it secure. I think that is a comfortable illusion.
Guardrails filter probabilistically. A determined attacker gets past them, and some prompt-injection techniques succeed roughly 88% of the time in published testing. Prompt injection is ranked the number-one risk in the OWASP Top 10 for LLM Applications for exactly this reason.
There is a sharper way to say it. You can patch a bug, but you cannot patch a brain. Fix a software bug and you are 99.99% sure it is gone; "fix" a model and you are 99.99% sure the problem is still there.
✅ The bar that actually counts: deterministic, runtime enforcement
So what does real protection look like? It is deterministic, and it runs where the agent runs.
One founder learned this the hard way. Their agent had safety rules written into a config file. The agent acknowledged the rules, then ignored them, and the production database was gone. Rules in a config file are not a guardrail. A permission system that physically cannot perform the action is a guardrail.
That is the lens I will judge every tool by in this guide: does it enforce outside the model, at runtime, in a way the agent cannot talk its way around? This is the runtime, agent-based approach we built EdgeLabs on, and it is why I keep coming back to one question in every PoC (proof of concept): would this still block the attack if the cloud link went dark? The rest of this article scores the field on exactly that.
How should you evaluate AI security tools, and which platforms make the shortlist?
Our Evaluation Criteria
I picked five criteria that actually change a runtime buying decision. I ignored everything that just pads a spec sheet.
- Runtime enforcement vs observe-only: does it stop an action, or only log it after the fact?
- Agent-only, no cloud dependency: do detection and response keep working self-hosted, on-prem, or air-gapped, with no cloud backend?
- LLM and agent coverage: does it defend live agents against prompt injection, tool-poisoning, and MCP abuse (MCP is the Model Context Protocol, a standard way agents call external tools)?
- Discovery and GPU or inference protection: can it find shadow AI and protect GPU and inference nodes? You cannot govern what you do not know exists.
- Category maturity: how proven and how reviewed is the vendor, given how young this space is?
Who This Guide Is For
- SecOps or DevSecOps leads at SMBs making cloud and AI or GPU workloads production-safe.
- AI-workload and LLM-agent runtime-security owners securing agentic systems in production.
- Kubernetes and container runtime-security owners moving from build-time scanning to runtime detection.
- GPU-cloud and AI-infrastructure security leads protecting inference nodes and clusters.
The shortlist
Total providers: 6.
- 1.1 EdgeLabs: Best for agent-only runtime detection and response on AI workloads and agents, with no cloud dependency.
- 1.2 Oligo Security: Best for runtime application and library-level detection.
- 1.3 Zenity: Best for enterprise AI-agent governance and inventory.
- 1.4 WitnessAI: Best for LLM access governance and policy enforcement.
- 1.5 Astelia: Best for emerging agentic-security controls.
- 1.6 Microsoft open-source agentic-security project: Best for teams building their own controls.
Master comparison table
| Company Name | Best For | Detection & Response Model | Deployment & Environment Coverage |
| EdgeLabs | Self-hosted, on-prem, or air-gapped AI workloads that need runtime detection and response without a cloud backend | Agent-only runtime: detection AND response run on a single eBPF-based agent (NDR, EDR, IPS/IDS, container/Kubernetes, vuln management, and AI/agent security via the Parallax engine) | Cloud, data center, on-prem, and air-gapped; Docker, Kubernetes, OpenShift, Talos, K3S; x86_64 and ARM_64; GPU/inference nodes (secured using CPU) |
| Oligo Security | Teams needing runtime application and open-source library-level detection | Runtime application detection and response focused on library and package behavior in execution | Cloud and Kubernetes/container environments; requires its platform backend |
| Zenity | Enterprises governing and inventorying AI agents and low-code/copilot builds | Governance and posture layer: discovery, inventory, and policy for agents; observe-and-govern, not on-host enforcement | SaaS-delivered governance across agent and copilot platforms; cloud-oriented |
| WitnessAI | Teams enforcing LLM access, usage, and data-protection policy | AI access governance: an in-line proxy for LLM traffic policy and visibility; policy enforcement, not host runtime detection | Cloud/network-delivered guardrail layer in front of LLM usage |
| Astelia | Early adopters piloting agentic-security controls | Emerging agentic-security controls; category is new and capabilities are still maturing | Cloud-oriented; verify deployment specifics with the vendor |
| Microsoft OSS agentic-security project | Platform teams building their own agentic-security controls | Open-source building blocks for agent security; you assemble detection and enforcement yourself | Self-hosted, wherever you run it; no managed backend by default (confirm exact repo before relying on it) |
EdgeLabs
- Runtime enforcement vs observe-only: Enforces in-line at runtime; host-local, time-boxed source-IP blocking (1 minute to 90 days) fires even with no cloud backend.
- Agent-only, no cloud dependency: Detection AND response run entirely on the agent; Disconnected Mode keeps both working fully offline.
- LLM and agent coverage: Parallax defends live agents against prompt injection and MCP tool-poisoning, with input/output alignment and hallucination checks.
- Discovery and GPU or inference protection: eBPF syscall monitoring covers containers, Kubernetes, and GPU/inference nodes; secures GPU workloads using only CPU.
- Category maturity: Roots in Scalarr's AI/ML work since 2017; enterprise deployments plus a new free/open-source SMB tier; the AI-agent category itself is young.
- Deploys as a Kubernetes DaemonSet (one privileged container per node, no sidecars) sharing host PID and network namespaces; Falco-ecosystem compatible and layered above it. See how it works.
- The Trader Joe's published case study reports 8x reduced CAPEX/OPEX, an 83% MTTR reduction, $1.3M operational savings, 573% ROI, a sub-2-week deploy, and zero outages (EdgeLabs' own published case-study figures, not independently verified).
- Exploited-vulnerability and explainability scoring combines CVSS, EPSS, CISA KEV, and CWE, so teams fix what is actually exploited, not just what scores high on paper. This drives our vulnerability management.
"Good IPS/IDS/EDR software. Webportal management is good. Docker container integration is useful."
Verified User in Computer Software · EdgeLabs G2 Verified Review
"It's beneficial to secure any website or any server from hackers." (Noting the same reviewer felt "the commercial seems high.")
Abhishek A. · EdgeLabs G2 Verified Review
Oligo Security
- Runtime enforcement vs observe-only: Detects application and library behavior at runtime; strongest as a runtime application-security signal rather than host-local in-line network blocking.
- Agent-only, no cloud dependency: Operates as a platform with a backend, so it is not positioned for air-gapped, no-cloud detection and response the way an agent-only design is.
- LLM and agent coverage: Centered on application and library runtime behavior; less focused on live LLM-agent prompt-injection or MCP tool-poisoning defense.
- Discovery and GPU or inference protection: Surfaces which vulnerable libraries actually load and run, which is genuinely useful for cutting vulnerability noise.
- Category maturity: A credible, named runtime application-security vendor, though third-party review volume in this young category is limited.
- Runtime library-level detection is its core, documented capability.
- Positioned in the runtime application-security space alongside other runtime-first vendors.
Zenity
- Runtime enforcement vs observe-only: Strong on visibility and policy across agents; it governs and flags rather than enforcing in-line on the host.
- Agent-only, no cloud dependency: Delivered as a cloud/SaaS governance layer, so it is not built for air-gapped, no-cloud detection and response.
- LLM and agent coverage: Genuinely useful for finding and inventorying agents and copilots across an enterprise estate.
- Discovery and GPU or inference protection: Discovery of agents and low-code apps is its core strength; not focused on GPU or inference-node protection.
- Category maturity: A recognized name in agent governance, though independent, third-party reviews remain thin in this young space.
- Focused on agent and copilot discovery, inventory, and policy across enterprise platforms.
- Positioned in the agent-governance layer, adjacent to but distinct from on-host runtime enforcement.
WitnessAI
- Runtime enforcement vs observe-only: Enforces policy on LLM traffic in-line, which is useful; it sits at the access layer, not at the host or kernel.
- Agent-only, no cloud dependency: A cloud/network-delivered guardrail layer, so it is not designed for air-gapped, agent-only detection and response.
- LLM and agent coverage: Focused on who uses which LLM, with what data, under what policy; strong on access and data-loss questions.
- Discovery and GPU or inference protection: Surfaces shadow LLM usage; not focused on GPU or inference-node runtime protection.
- Category maturity: An active name in AI access governance; verify the exact product scope with the vendor, as the category is new and evolving.
- Positioned as an in-line governance and observability layer for enterprise LLM traffic.
- Addresses the access and data-exposure side of AI usage.
Astelia
- Runtime enforcement vs observe-only: Early-stage; verify whether controls enforce in-line or mainly observe before you rely on them.
- Agent-only, no cloud dependency: Not established as an agent-only, no-cloud design; confirm deployment model directly.
- LLM and agent coverage: Aimed at agentic-security use cases, which is the right problem for this space.
- Discovery and GPU or inference protection: Scope is still forming; validate against your specific agent and infrastructure needs.
- Category maturity: A genuinely young entrant, so treat published capabilities as a starting point for your own testing.
- Positioned in the emerging agentic-security category.
- Best evaluated through a hands-on pilot given how new the category is.
Microsoft open-source agentic-security project
- Runtime enforcement vs observe-only: You decide, because you assemble the controls; enforcement quality depends on your own build.
- Agent-only, no cloud dependency: Self-hosted by nature, so it can run without a vendor cloud, but you own the operational load.
- LLM and agent coverage: Provides primitives for agent security; coverage is what you make of it.
- Discovery and GPU or inference protection: Not a turnkey capability; depends entirely on how you extend it.
- Category maturity: Open-source and evolving; confirm the exact repository name and current status with the client before relying on it in production.
- Open-source primitives for securing agentic systems.
- Best suited to teams with the engineering capacity to integrate and maintain their own stack.
Q2. How should you evaluate AI security tools, and which platforms make the shortlist?
Judge an AI-workload security platform on five things: runtime enforcement (does it stop actions or only observe?); agent-only operation (does detection AND response keep working with no cloud backend, on-prem or air-gapped?); LLM and agent coverage (prompt injection, tool-poisoning, MCP); discovery of shadow AI plus GPU and inference protection; and category maturity, since many vendors are early and thinly reviewed. Pricing is excluded, because it produces false comparability.
I built this rubric the way I would brief a peer over coffee. These are the five questions I would actually ask a vendor before I let their code near my nodes.
🎯 Why these five criteria, and not twenty
Most comparison grids drown you in checkboxes. I cut the list to the five that change the decision, and dropped the rest.
Every criterion here maps to a real failure I have watched happen. A tool that only observes, a tool that dies when the cloud link drops, a tool blind to the agent layer: each one leaves a gap an attacker walks through. This is why our AI and LLM security starts at execution.
- Runtime enforcement vs observe-only: Does it stop the action, or just log it after the damage? Observing a database deletion is not the same as blocking it.
- Agent-only, no cloud dependency: Do detection and response both keep running with the backend cut? This is the air-gapped and on-prem question.
- LLM and agent coverage: Does it defend live agents against prompt injection (malicious text that hijacks the model) and tool-poisoning (a corrupted tool the agent trusts)?
- Shadow-AI discovery and GPU protection: Can it find AI you did not know was running, and guard GPU and inference nodes?
- Category maturity: How proven is the vendor, and how much independent review exists?
💸 Why discovery sits in the rubric
You cannot govern what you do not know exists. That line drives the fourth criterion, because shadow AI is not a rounding error.
Organizations with a breach involving shadow AI paid about $670,000 more, per the figure I keep citing to skeptical buyers. When roughly 1.3 billion AI agents may enter the workforce over three years, discovery stops being optional.
⚠️ Why I left pricing out
I did not include a pricing column, and that is deliberate. Runtime-security pricing is custom, tiered, and usage-based, so a single number invites a false apples-to-apples comparison. You can review our own model on the pricing page.
I would rather you weigh detection model and deployment reach, then get real quotes. A cheap tool that cannot reach your air-gapped cluster is not cheap; it is useless spend.
👥 Who this guide is for
- SecOps or DevSecOps leads at SMBs making cloud and AI or GPU workloads production-safe.
- AI-workload and LLM-agent runtime-security owners securing agentic systems in production.
- Kubernetes and container runtime-security owners moving from build-time scanning to runtime.
- GPU-cloud and AI-infrastructure security leads protecting inference nodes and clusters.
The full shortlist (1.1 EdgeLabs through 1.6 the Microsoft open-source project) and the four-column master table sit in the section above, so I will not repeat them here.
🧭 Where EdgeLabs sits in this rubric
I will be straight about my bias. We built EdgeLabs to score well on the first two criteria specifically: detection AND response run on one agent, and both keep working in Disconnected Mode with the cloud link cut. That is not a feature we bolted on; it is the reason the company exists. On the other criteria, I would still have you test us against the field, because a rubric only matters if you run it yourself.
Q3. EdgeLabs: runtime security for AI workloads and agents
EdgeLabs runs detection AND response entirely on one lightweight agent, so it keeps protecting AI workloads self-hosted, on-prem, and air-gapped, where cloud-dependent tools cannot reach. The Parallax engine defends LLM agents at runtime against prompt injection and tool-poisoning. eBPF kernel-level monitoring (eBPF lets us watch system calls safely inside the Linux kernel) covers containers and Kubernetes. One agent consolidates NDR, EDR, IPS/IDS, vulnerability management, and AI and agent security.
The full provider card for EdgeLabs, with all the field detail, sits in the shortlist section above. Here I want to give the context behind it, the part a spec sheet cannot carry.
🛠️ What "agent-only" actually feels like in a deployment
Let me make this concrete. When we deploy the agent as a Kubernetes DaemonSet (one agent per node, no sidecars) across a customer's cluster, it starts watching syscalls on day one.
I remember a security lead asking me, mid-PoC, whether the agent would still block an attack if the backend went dark, three days before an audit. The answer is yes, and that question is exactly why we designed it this way.
A permission system that physically cannot perform an action is a guardrail; a rule the agent can ignore is not. Our in-line prevention, host-local, time-boxed source-IP blocking, one minute to ninety days, fires without a cloud round-trip. You can see the mechanics on our how it works page.
💰 What the numbers say, and what I will not overclaim
Our published Trader Joe's case study reports an 83% MTTR reduction (MTTR means mean time to respond), $1.3M in operational savings, and 573% ROI, with a sub-two-week deploy. Those are EdgeLabs' own published figures, not independently audited, and I would rather you know that than pretend otherwise.
Here is where I stay honest about scope. EdgeLabs is Linux-focused, the AI-agent category is young, and our Compliance Center covers runtime, vulnerability-handling, and detection-and-response controls, not lifecycle or program controls like business continuity or HR training. Some direct rivals, Aqua for one, have posture features we do not yet match.
⭐ What users actually say
The category is young and under-reviewed, so I am giving you the verified G2 reviews we have, mixed, not cherry-picked.
"Good IPS/IDS/EDR software. Webportal management is good. Docker container integration is useful."
Verified User in Computer Software EdgeLabs G2 Verified Review
"It's beneficial to secure any website or any server from hackers." (The same reviewer noted "the commercial seems high.")
Abhishek A. EdgeLabs G2 Verified Review
That 4-star note about "issues can be complicated for non-technical folks" is fair, and it is on my list to fix. EdgeLabs' whole design bet is that detection and response belong on the agent, at runtime, where the workload actually executes, and the reviews and the case-study numbers are the evidence I would ask you to weigh, not my adjectives. Our vulnerability management reflects that same runtime-first bet.
Q4. Oligo, Zenity, WitnessAI, Astelia, and the Microsoft open-source project: how do the other AI security tools compare?
The rest of the field splits by job. Oligo focuses on runtime application and library detection; Zenity and WitnessAI lead on agent governance, inventory, and access control rather than on-agent enforcement; Astelia and Microsoft's open-source agentic-security project serve teams building their own controls. Most are early and thinly reviewed, which signals a young category, not a disqualifier.
The individual cards for these five (1.2 through 1.6) are in the shortlist section above. This section draws the one line that matters when you compare them: enforcement versus governance.
🧩 The enforcement-versus-governance split
Here is the distinction the category tends to blur. Some tools enforce (they stop the action on the host), and some govern (they see it, inventory it, and recommend).
Both jobs are real. But if your fear is an agent taking a destructive step at runtime, a governance layer will show you the step and not stop it. Our workload and application security is built for the enforcement side.
- Enforcement (on-host, runtime): blocks or contains the action as it happens.
- Governance (discovery, policy, access): finds agents, sets policy, and flags violations.
Oligo sits closest to enforcement on the application and library layer. Zenity and WitnessAI are strong governance and access plays. Astelia and the Microsoft open-source project hand you building blocks you assemble yourself.
⚠️ Why "young and thinly reviewed" is not a red flag
I want to be fair here, because these are credible teams solving a hard problem. The lack of third-party reviews reflects the category's age, not the vendors' quality.
One caution I will offer from experience. There is a real line between security research that finds genuine vulnerabilities and research produced mainly as marketing. In one widely cited agent exploit, the researchers had to run the query around 200 times before it worked on the 201st attempt, which tells you reproducible agent exploitation is genuinely hard.
🔍 How I would actually test them
My advice is boring and it works: pilot against your own workloads. A brochure claim means little until you watch what surfaces when you run it live.
For the Microsoft open-source project, confirm the exact repository name and current status before you rely on it in production, and budget the engineering hours, because free software still costs staff time.
🧭 Where EdgeLabs fits in the comparison
I will keep the contrast fair and short. Governance and access tools protect the doorway and the org chart; EdgeLabs is built to stop the action at the host, at runtime, with no cloud dependency, which is a different and complementary job. In practice, I have watched teams pair a governance layer for agent discovery with our network protection agent for runtime enforcement, and that combination covers more ground than either alone. The category is young enough that honest scoping beats big claims, so I would rather tell you where each tool genuinely fits than pretend one tool does everything. If you want to see where it fits your stack, reach out to our team.
Q5. Why are XDR and cloud-NDR platforms like CrowdStrike, SentinelOne, and Vectra different from agent-only AI-workload security?
CrowdStrike and SentinelOne are XDR/endpoint-first, so runtime is a module on an endpoint engine, not the starting point (XDR means extended detection and response). Vectra and Darktrace are network-first and cloud-dependent, so detection and response degrade when the cloud link is severed. For self-hosted, on-prem, or air-gapped AI workloads, the deciding question is not brand strength. It is whether detection AND response run on the agent, with no cloud backend.
I get this question in almost every PoC. Buyers assume their existing XDR already covers AI workloads, so let me draw the real line, the same one behind our workload and application security.
🖥️ XDR is endpoint-first, and runtime is the bolt-on
CrowdStrike and SentinelOne are excellent at what they were built for: the endpoint. Their engines started on laptops and servers, and cloud runtime came later.
That order matters. When runtime is an extension of an endpoint engine, container and Kubernetes coverage tends to feel bolted on, not native. The "just turn on your XDR's runtime module" reflex quietly assumes an endpoint-shaped world, which is why our Kubernetes security is built runtime-first instead.
🌐 Cloud-NDR is heavyweight, and it leans on the cloud
Vectra and Darktrace lead on network detection and response (NDR means watching network traffic for threats). They are strong, and they are also heavy and cloud-dependent.
Here is the catch for AI workloads. If detection and response route through a vendor cloud, a severed link means degraded protection, exactly when your air-gapped inference cluster needs it most. Our network protection agent is designed for that offline case.
There is a deeper incentive problem too. Many SIEMs (log-analysis platforms) would not even tell you when log collection broke, because reducing data hurt their pricing. The blunt version from the field: the motivation for these companies is to make money, not to solve your problem.
✅ The deciding question for AI workloads
So the choice is not "which brand is biggest." It is architectural.
For AI workloads that run self-hosted, on-prem, or air-gapped, ask one thing: do detection and response both keep working with the cloud cut? This is where EdgeLabs sits by design, agent-only, lightweight, with detection and response on the agent and no cloud dependency, rather than an endpoint engine extended sideways or a heavyweight cloud-NDR that goes quiet offline.
Q6. Does an AI security tool make you CRA and NIS2 compliant?
No single tool makes you compliant. Runtime AI-security platforms can cover concrete CRA and NIS2 controls: vulnerability handling, detection and response, SBOMs (a software parts list), and exploitation-aware prioritization (CVSS plus EPSS plus CISA KEV) instead of severity alone. The clock is real. The EU Cyber Resilience Act's actively-exploited-vulnerability reporting starts 11 September 2026, with main obligations from 11 December 2027. Governance and business continuity stay with you.
Let me be blunt, because vendors are not. Anyone selling you a "compliance button" is selling security theater.
📋 What a tool can genuinely cover
A runtime tool can carry real, named CRA and NIS2 controls. It cannot carry your whole program.
- Vulnerability handling and the CRA Annex I "No Known Exploitable Vulnerabilities" check.
- Detection and response, including NIS2 incident-reporting timelines.
- SBOM generation, both build-time and runtime.
- Exploitation-aware prioritization, so you patch what attackers actually use.
That last point is where I push back on habit. CVSS-first patching fixes what looks scary on paper; add EPSS (exploit-probability scoring) and the CISA KEV catalog (known exploited vulnerabilities), and you fix what is actually being exploited. That logic drives our vulnerability management.
⏰ The dates, and why "installed" is not "enforced"
The CRA is in force now, with exploited-vulnerability reporting from 11 September 2026 and main obligations from 11 December 2027. Auditors will not accept a screenshot of an installed tool. Our practical CRA roadmap walks through this in detail.
I once saw a team block all untrusted software in the config, then leave the machine in monitor-only mode. The rule existed; nothing enforced it. Audits check enforcement, not intentions.
⚠️ The honest boundary
Here is what an AI-security tool does not do, ours included. It will not write your business-continuity plan, run your backup regime, manage MFA as a program, train your staff, or satisfy the CRA's multi-year support obligations.
EdgeLabs ships a CRA/NIS2 Compliance Center that maps controls to requirement IDs and runs the "No Known Exploitable Vulnerabilities" check as live runtime evidence, not a posture-only report. It covers the runtime, vulnerability-handling, and detection-and-response slice, and I would rather name that boundary than pretend we cover your governance program.
Q7. Which AI security tool is right for your situation?
Match the tool to the job. If your AI runs self-hosted, on-prem, or air-gapped and you need detection AND response without a cloud backend, an agent-only runtime platform fits. If you mainly need agent inventory and access governance, a governance layer fits. If you are building your own controls, start open-source. The one non-negotiable: protection must run where your agents actually execute.
🧭 Route yourself to a fit
I will not pretend one tool wins for everyone. Here is how I would route the personas I meet most.
- If you run air-gapped, on-prem, or self-hosted AI: choose agent-only runtime detection and response, so protection survives a cut cloud link. This is EdgeLabs' lane.
- If your main pain is unknown agents and access sprawl: start with a governance and inventory layer.
- If your team has engineering time and wants full control: begin with an open-source project and build from there.
- If you are a lean SMB SecOps team: try a free or self-serve tier before you spend, because your budget is finite and probably sitting in something else already.
💰 Where I think this goes next
You can patch a bug, but you cannot patch a brain. That single line is why I think the next two years belong to runtime.
Distributed AI computing forces distributed security. Securing the LLM agent while it runs stops being a niche and becomes the default question every SecOps team has to answer.
So here is the question I am sitting with, and I would genuinely like your answer. When you cut the cloud link three days before an audit, does your current stack still detect and respond, or does it go quiet? If you want to test that against your own workloads, EdgeLabs has a free self-serve on-ramp, and I would rather hear what you are actually building than send you a demo script, so reach out to our team.
FAQs
AI security tools split into two very different jobs that buyers constantly conflate, so we want to be precise.
- Using AI to defend traditional systems: an AI-powered firewall or endpoint agent that protects laptops, servers, and networks.
- Protecting the AI itself: the model, the LLM agent, and the compute it touches, which is what this guide covers.
The second job matters because when you hand an LLM agent access to your data and your compute, it becomes a new kind of insider that needs supervision while it runs, not just a pre-flight check.
These tools defend against threats with no malware signature, like prompt injection, tool-poisoning, data exfiltration, and excessive agency, where an autonomous agent takes a destructive action on its own. They span discovery, build-time posture, and runtime enforcement.
We built our AI and LLM security around the runtime layer, because that is where an attack actually executes. A coding agent once deleted a company's entire database, backups included, in nine seconds, which is exactly the kind of runtime failure a build-time scan never catches.
Runtime is where the attack actually executes, and it is the thinnest-covered window in almost every roundup you will read.
Most tools crowd into discovery and posture. They scan your setup, check your config, and hand you a clean report, but discovery and posture are build-time and theoretical. An attacker does not exploit your architecture diagram; they exploit the running agent.
Traditional defenses hunt for a malware signature, a known-bad fingerprint, and AI attacks have none. They are just context-rich text that manipulates the model's decision-making.
- Guardrails are mostly theater: they filter probabilistically, and some prompt-injection techniques succeed roughly 88% of the time in published testing.
- Rules in a config file are not a guardrail: one founder's agent acknowledged its safety rules, then ignored them, and the production database was gone.
Real protection is deterministic and runs where the agent runs. A permission system that physically cannot perform the action is a guardrail; a rule the agent can ignore is not.
That is why our workload and application security starts at execution, enforcing outside the model in a way the agent cannot talk its way around.
We judge an AI-workload security platform on five things that actually change a buying decision, and we ignore what just pads a spec sheet.
- Runtime enforcement vs observe-only: does it stop the action, or only log it after the damage?
- Agent-only, no cloud dependency: do detection and response both keep working self-hosted, on-prem, or air-gapped, with the backend cut?
- LLM and agent coverage: does it defend live agents against prompt injection, tool-poisoning, and MCP abuse?
- Shadow-AI discovery and GPU protection: can it find AI you did not know was running and guard inference nodes? You cannot govern what you do not know exists.
- Category maturity: how proven and how reviewed is the vendor, given how young this space is?
We deliberately left pricing out, because runtime-security pricing is custom and usage-based, so a single number invites a false comparison.
A breach involving shadow AI cost organizations about $670,000 more, which is why discovery earns its place in the rubric. You can weigh detection model and deployment reach for yourself, then check our own model on the pricing page and get a real quote.
We get this question in almost every proof of concept, because buyers assume their existing XDR already covers AI workloads.
The difference is architectural, not brand strength.
- XDR is endpoint-first: CrowdStrike and SentinelOne started on laptops and servers, so container and Kubernetes runtime tends to feel bolted on, not native.
- Cloud-NDR is heavyweight and cloud-dependent: Vectra and Darktrace are strong on network detection, but if detection and response route through a vendor cloud, a severed link means degraded protection.
For AI workloads that run self-hosted, on-prem, or air-gapped, the deciding question is simple: do detection and response both keep working with the cloud cut?
This is where our agent-only architecture sits by design. Detection and response run on one lightweight agent with no cloud backend, rather than an endpoint engine extended sideways or a heavyweight cloud-NDR that goes quiet offline. That single question, would it still block the attack if the cloud link went dark, is the one we would put to any vendor before letting their code near your inference nodes.
No single tool makes you compliant, and anyone selling you a compliance button is selling security theater.
A runtime AI-security platform can carry real, named controls, but it cannot carry your whole program.
- What a tool can cover: vulnerability handling and the CRA Annex I No Known Exploitable Vulnerabilities check, detection and response with NIS2 incident-reporting timelines, SBOM generation, and exploitation-aware prioritization using CVSS plus EPSS plus the CISA KEV catalog.
- What stays with you: business-continuity planning, backup regimes, MFA as a program, staff training, and the CRA's multi-year support obligations.
The clock is real. The EU Cyber Resilience Act's actively-exploited-vulnerability reporting starts 11 September 2026, with main obligations from 11 December 2027, and auditors check enforcement, not a screenshot of an installed tool.
Our CRA and NIS2 Compliance Center maps controls to requirement IDs and runs the No Known Exploitable Vulnerabilities check as live runtime evidence, not a posture-only report. We would rather name that boundary than pretend we cover your governance program.
