8 Best Sysdig Alternatives for Securing LLM & AI Agent Workloads at Runtime

Q1: Why Are Teams Looking Past Sysdig to Secure LLM and AI Agents at Runtime? [toc=1. Why Look Past Sysdig]

I keep getting the same message from SecOps leads. It usually lands late, often after a demo. "We like Sysdig. But our agents now touch production, and I am not sure a scan tells me what they actually do at 2 a.m." That is the real anxiety behind this search. You gave a large language model (LLM), an AI system that reads and writes text, a set of tools. Now it can reach into your database. The question is no longer "is my code clean." It is "what does this thing do when it runs, and can I stop it in time." This guide answers that, tool by tool.

Sysdig earned its reputation honestly. It is built on Falco, the open-source runtime detection engine it created and donated to the Cloud Native Computing Foundation (CNCF). Its Threat Research Team (TRT) does real work. In May 2026, they documented the first observed LLM-agent-driven intrusion: an attacker chained CVE-2026-39987, a Marimo notebook remote code execution flaw, to internal database exfiltration in four pivots, all inside one hour. So Sysdig is not the villain here. It is the baseline.

The gap is about fit for this specific job. Sysdig is a broad cloud-native application protection platform (CNAPP), and it leans on a cloud backend. That makes it heavier to run fully self-hosted, on-prem, or air-gapped (disconnected from the internet). Its AI-agent coverage is newer than its container heritage, and it ships detections built on Falco primitives that alert your team rather than block on the agent. Those are the exact axes an AI-workload owner has to weigh.

Attacks execute at runtime, not at scan time. A clean posture scan describes what your config looked like yesterday. It says nothing about the agent that reads an untrusted email and decides to act. This is the semantic gap: an AI model processes everything as text, so it cannot reliably tell a trusted instruction from your developer apart from a hostile instruction hidden in user input. There is no malware signature to catch. Just text that rewrites the agent's intent.

Speed makes it worse. A small company, Pocket OS, watched an AI coding agent delete its entire production database, then wipe the backups, in about nine seconds. Nine seconds is faster than you can read a Slack message. If your response has to round-trip to a cloud console first, you have already lost. That is why I keep saying runtime is the hero. You cannot secure what you only scanned.

📋 Our Evaluation Criteria

I evaluated eight tools against six criteria that actually decide a runtime AI-security purchase. I kept the list short on purpose.

• Runtime detection method: Does it watch live behavior at the kernel level using eBPF (a Linux feature that safely observes system calls), or does it only scan posture and configuration? Runtime is where attacks happen.
• Detection and response location: Does response (blocking, killing a process) run on the agent, or does it need a cloud brain to act? On-agent response is faster and survives outages.
• Cloud dependency: Does the tool keep working with no cloud backend, or does detection stop when the link drops? This decides air-gapped fit.
• Deployment reach: Where does it actually run, across cloud, data center, on-prem, and air-gapped nodes, plus Kubernetes and GPU inference hosts?
• LLM and AI-agent coverage: Does it map to real agent threats, like prompt injection and tool poisoning, referenced against the OWASP Top 10 for LLM Applications and the OWASP Top 10 for Agentic Applications?
• Coding-agent runtime coverage: Does it detect the behavior of AI coding agents like Claude Code, Codex, and Gemini CLI while they execute?

👥 Who This Guide Is For

• AI-workload and LLM-agent runtime-security owners securing agents, MCP servers (the Model Context Protocol connectors that give agents tools), and agentic systems in production.
• SMB SecOps and DevSecOps leads making cloud and GPU workloads production-safe without a 24/7 team.
• Kubernetes and container runtime-security owners moving from build-time scanning to live runtime detection.
• GPU-cloud and AI-infrastructure security leads protecting inference nodes and clusters.

🗂️ The Eight Tools at a Glance

This guide covers eight tools. Here is the situation each one genuinely fits.

• EdgeLabs: Best for runtime AI/agent detection and response that must keep working self-hosted, on-prem, or air-gapped, with no cloud backend.
• Sysdig: Best for teams that want a broad, Falco-rooted cloud CNAPP with mature threat research.
• Aqua Security: Best for securing the full container and Kubernetes lifecycle, from build to runtime.
• CrowdStrike Falcon Cloud Security: Best for endpoint-first teams extending an established XDR engine into cloud workloads.
• SentinelOne Singularity Cloud: Best for teams standardizing on one XDR vendor across endpoint and cloud.
• Oligo Security: Best for library-level application runtime risk, watching what your code actually loads and runs.
• Upwind Security: Best for cloud-native teams wanting runtime CNAPP with eBPF sensors.
• Falco (CNCF): Best for teams that want the free open-source runtime rules engine and will operate response themselves.

📊 Master Comparison Table

Darktrace and Vectra AI come up in these conversations too. I mention them only for contrast. Both are network-first, cloud-dependent, and heavyweight, so they solve a different problem and do not get a card here.

Here is where I put our own cards on the table. We built EdgeLabs as a runtime-security platform for AI workloads, agents, and infrastructure, and detection and response both run on the agent with no cloud dependency. That is the wedge this whole guide tests every tool against. Now let me show you the first two, starting with us, then the rival most of you already know.

1 out of 8

EdgeLabs

Runtime security for AI workloads and agents Agent-only, no cloud dependency One-agent consolidation

Category

Runtime detection and response for AI workloads

Detection

eBPF kernel-level, on the agent

Deployment

DaemonSet, one agent per node, no sidecars

Footprint

Typically under 5% CPU, under 500 MB RAM (EdgeLabs' own published claim)

Evaluated on the basis of

• Runtime detection method: eBPF syscall detection on the agent; catches fileless execution and container escapes at runtime.
• Detection and response location: both run on the agent; in-line blocking fires even with the cloud link cut.
• Cloud dependency: none required; Disconnected Mode keeps detection and response working offline.
• Deployment reach: cloud, data center, on-prem, air-gapped; Kubernetes, containers, and GPU inference nodes.
• LLM and AI-agent coverage: the Parallax engine acts as an LLM proxy for prompt-injection and tool-poisoning defense.
• Coding-agent runtime coverage: runtime behavior monitoring on the host where the agent executes, not build-time only.

Differentiator

One eBPF-based agent consolidates NDR, EDR, IPS/IDS, container/Kubernetes security, vulnerability management, and AI/agent security. Detection and response both run on the agent, so protection continues self-hosted, on-prem, or air-gapped with no cloud backend. This is the "agent-only, no cloud dependency" design, and it is the honest core of our pitch.

Proof of execution

• Parallax inspects agent input and output at runtime, aligning prompts and blocking tool-poisoning attempts against live agents.
• In-line prevention does host-local, time-boxed source-IP blocks (1 minute to 90 days) that fire with no cloud backend.
• Exploited-vulnerability scoring combines CVSS, EPSS, and CISA KEV so you patch what is actually exploited, not just scary on paper.
• Trader Joe's: 83% MTTR reduction, $1.3M operational savings, 573% ROI, under two-week deploy (EdgeLabs' own published case study; self-reported, not independently verified).

Pricing

Free/open-source self-serve entry, with paid tiers above it. The free tier is intentionally limited versus enterprise.

Potential limitation

I will be candid. Our Compliance Center covers runtime, vulnerability-handling, and detection-and-response controls. It does not cover lifecycle, governance, or program controls (backup, business continuity, HR training, the CRA long-term support obligations). Some direct rivals, Aqua for example, have build-time and lifecycle features we do not yet match. We do not do OT visibility, IoT-hardware, or "edge security" as a category.

My take

If you must run detection and response where the cloud cannot reach, this is the lane we built for. If your whole world is cloud-console CNAPP, a heavier platform may suit you better. I would rather tell you that now than after a PoC.

"Good IPS/IDS/EDR software. Webportal management is good. Docker container integration is useful."

Verified User in Computer Software, 4.0/5 · G2 verified review

"It's beneficial to secure any website or any server from hackers. [Dislike:] the commercial seems high."

Abhishek A., 4.5/5 · G2 verified review

4.5/5 ★★★★★

Based on verified reviews

2 out of 8

Sysdig

Falco-rooted cloud CNAPP Runtime threat detection Mature threat research

Category

Cloud-native application protection (CNAPP/CWPP)

Detection

eBPF/syscall via Falco, its own OSS core

Deployment

Agent plus cloud console for analysis

AI coverage

Runtime detections for AI coding agents (2026)

Evaluated on the basis of

• Runtime detection method: strong eBPF/syscall detection, built on Falco, the engine Sysdig created and donated to CNCF.
• Detection and response location: agent detects, but analysis and response lean on the cloud console.
• Cloud dependency: a cloud backend is expected; harder to run fully self-hosted or air-gapped.
• Deployment reach: broad cloud coverage across containers, Kubernetes, hosts, and serverless.
• LLM and AI-agent coverage: AI Workload Security plus new coding-agent detections built on Falco primitives.
• Coding-agent runtime coverage: yes, it flags Claude Code, Codex, and Gemini CLI behavior, but it alerts rather than blocks on-host.

Differentiator

Deep Falco heritage and a respected Threat Research Team. Sysdig correlates risks, attack paths, and AI workloads into one cloud view, which makes investigation fast and intuitive.

Proof of execution

• Sysdig TRT documented the first observed LLM-agent-driven intrusion: CVE-2026-39987 Marimo RCE to internal database exfiltration in four pivots, under one hour (May 2026).
• Runtime detections for AI coding agents flag sensitive-file access and risky command arguments (March 2026).
• Detections are constructed on standard Falco primitives by Sysdig's Threat Research Team.

Pricing

Custom, usage-based. Practitioners note pricing can climb with scale, so model your node count early.

Potential limitation

It is cloud-oriented, so fully air-gapped detection and on-agent response are harder. AI-agent coverage is newer than its container heritage, and its coding-agent detections alert rather than block at the host.

My take

Sysdig is a credible, mature choice if your environment lives in the cloud and you want breadth plus strong research. If your constraint is self-hosted or air-gapped response, that is the seam where a different design fits better.

3 out of 8

Aqua Security

Container and Kubernetes lifecycle security eBPF runtime detection Build-to-runtime coverage

Category

Cloud-native security (container/Kubernetes)

Detection

eBPF runtime (Tracee open-source heritage)

Coverage

Build-time scanning plus runtime detection

Deployment

Cloud, data center, on-prem

Evaluated on the basis of

• Runtime detection method: eBPF runtime detection, with open-source roots in Tracee, its syscall-tracing project.
• Detection and response location: platform-managed; response is coordinated through the Aqua platform, not purely on-agent.
• Cloud dependency: supports self-managed deployments, so it can run without a vendor cloud in some setups.
• Deployment reach: strong across containers and Kubernetes, spanning cloud, data center, and on-prem.
• LLM and AI-agent coverage: focused on container and Kubernetes workloads more than purpose-built LLM-agent runtime defense.
• Coding-agent runtime coverage: not a stated focus; strength is the container lifecycle, not AI coding agents specifically.

Differentiator

Full-lifecycle depth. Aqua covers the path from image scanning at build time to runtime detection, with mature Kubernetes controls and a well-known open-source presence in Tracee.

Proof of execution

• Tracee, Aqua's open-source project, uses eBPF to trace container runtime events and detect suspicious behavior.
• The platform pairs image and infrastructure scanning with runtime protection across the container lifecycle.
• Long track record in container and Kubernetes security.

Pricing

Custom, platform-based. Model your cluster and node count early, since scope grows with lifecycle coverage.

Potential limitation

Its center of gravity is the container lifecycle, not LLM-agent runtime defense. I will be fair here: Aqua has build-time and lifecycle features we at EdgeLabs do not yet match. If your priority is purpose-built agent security, that is a different job.

My take

If your problem is "secure my containers and Kubernetes end to end," Aqua is a serious, mature pick. If your problem is "supervise what my LLM agent does at runtime," weigh a purpose-built option.

4 out of 8

CrowdStrike Falcon Cloud Security

Endpoint-first XDR Cloud-workload module Threat intelligence

Category

XDR/endpoint-first with cloud security

Detection

Endpoint engine extended to cloud workloads

Response

Cloud-console coordinated

Deployment

Cloud and endpoint, cloud-anchored

Evaluated on the basis of

• Runtime detection method: strong endpoint detection extended to cloud workloads; runtime is an add-on to the endpoint engine.
• Detection and response location: response is coordinated through the Falcon cloud console, not fully on the local agent.
• Cloud dependency: the cloud console is central, so air-gapped operation is not the design target.
• Deployment reach: broad across endpoint and cloud, anchored to the Falcon platform.
• LLM and AI-agent coverage: general threat coverage rather than purpose-built LLM-agent runtime defense.
• Coding-agent runtime coverage: not a stated, specific focus.

Differentiator

Category-leading threat intelligence and a mature endpoint engine. If you already run Falcon, extending it to cloud workloads keeps tooling familiar for your team.

Proof of execution

• Well-established XDR (extended detection and response) platform with wide enterprise adoption.
• Cloud security module brings workload protection into the same console as endpoint.
• Deep threat-intelligence operation behind detections.

Pricing

Custom, module-based. Costs stack as you add cloud, identity, and other Falcon modules.

Potential limitation

It is endpoint-first, so runtime for cloud and AI workloads is a bolt-on, not the first stop. The console-centric model makes fully self-hosted or air-gapped runtime response hard.

My take

A safe pick if Falcon is already your standard and endpoint is your center of gravity. If runtime AI-workload defense with no cloud dependency is the goal, an endpoint-first engine is the wrong starting point.

Q2: What is runtime security for AI agents, and why doesn't scanning catch it? [toc=2. Runtime AI Security Explained]

AI-agent runtime security detects and stops threats to LLM agents, MCP servers (Model Context Protocol connectors that hand agents tools), and AI coding assistants while they execute. It uses kernel-level telemetry, meaning signals read straight from the operating system, to catch prompt-injection consequences, tool poisoning, and excessive-agency actions that posture and build-time scanning miss. You cannot patch a brain, so you have to watch it act.

🧠 Why a clean scan lies to you

Let me define the two words that get confused. Build-time scanning checks your code and images before they run. Posture scanning checks how your cloud is configured. Both describe yesterday's snapshot.

Runtime is different. Runtime is the moment the workload actually executes, when a real process makes real system calls. That is where an attack happens, and it is the one moment a scan never sees. This is precisely why we built our runtime workload and application security around live execution rather than static snapshots.

📖 The concept: the semantic gap

Here is the core idea in plain English. An AI model reads everything as text. It cannot reliably tell a trusted instruction from your developer apart from a hostile instruction buried in the data it reads.

Security folks call this the semantic gap. There is no malware signature to match. There is just text that quietly rewrites what the agent decides to do next.

Borrowing a line I wish I had written first, you can patch a bug and be almost certain it is fixed. Try that with an AI system, and you can be almost certain the problem is still there. Code logic does not fix a weight-based decision.

🧪 The example: the lethal trifecta

Concrete example. Security researchers describe a "lethal trifecta," the dangerous combination of untrusted content, access to private data, and a way to communicate out.

A recruiter's automated agent once read a candidate profile online. Hidden text in that profile told the agent to fetch private files. It returned the contents of an id_rsa file, which holds private SSH keys, straight to the attacker.

⚙️ The application: what a runtime tool must do

So what do you actually check when you evaluate a tool? Ask whether it watches live behavior or just config.

• Does it see syscalls? eBPF (a safe Linux feature for reading kernel events) catches fileless execution, like memfd_create, and container escapes as they happen.
• Does it map to real agent threats? The OWASP Top 10 for LLM Applications 2025 lists prompt injection (LLM01) and excessive agency (LLM08) as top risks.
• Can it act, not just alert? Detection without response is a smoke alarm with no sprinkler.

This is the frame I keep coming back to. Distributed AI computing demands distributed security, so protection has to run where the agent runs, not in a far-off dashboard. An LLM agent is a new kind of insider, and it needs runtime supervision, not a pre-flight check. That is the thinking behind our AI and LLM security engine.

🛡️ Where EdgeLabs fits

At EdgeLabs, this is exactly why we built the Parallax engine. It sits in front of the agent as an LLM proxy, meaning a checkpoint for what goes in and comes out, aligning prompts and blocking tool-poisoning attempts against live agents. I offer it here as one example of the category, not the only answer. The test that matters is simple: does your tool watch the agent act, or only scan it beforehand? You can see the mechanics in how our approach works.

Q3: Agent-based vs agentless, and why are Falco and eBPF the runtime baseline? [toc=3. Agent vs Agentless & Falco]

Agentless tools give fast, broad visibility, but they generally cannot kill a malicious process at runtime. So AI agents that take autonomous actions need eBPF-based agent enforcement, not posture scanning alone. Falco, the CNCF-graduated, eBPF-powered engine underneath much of the category and Sysdig itself, is the free open-source baseline every serious alternative is measured against.

👀 Agentless feels enough, until it isn't

Agentless means no software installed on the workload. The tool reads cloud APIs and snapshots to map risk. It is quick to deploy and great for broad visibility.

Then an agent acts. Agentless can see that something happened, but it usually cannot step in and stop the process mid-action. Visibility is not enforcement.

⚡ Why speed decides this

Here is the stakes cue. A small company, Pocket OS, watched an AI coding agent wipe its production database, then delete the backups, in about nine seconds.

Nine seconds is faster than you can read a Slack message. If your response has to round-trip to a cloud console before it acts, the damage is already done. On-agent response is not a luxury here. It is the difference between a blocked action and a post-mortem.

🧩 The concept: agent-based enforcement

An agent-based tool runs on the host. With eBPF, it reads system calls directly and can act in-line, meaning it blocks the bad action as it happens.

That in-line step is what agentless cannot match. Killing a process, blocking a source IP, or halting a container escape needs something present on the node, watching the kernel. Our network protection layer does exactly this kind of in-line blocking on the node.

🌱 The example: Falco as the shared root

Now the baseline. Falco is an open-source runtime detection engine that reads syscalls and fires rules, for example when a shell spawns inside a container. It is a CNCF-graduated project, which signals real maturity and adoption.

Here is the part people miss. Sysdig created Falco and donated it, and much of this category, including detections in Sysdig's own AI coding-agent work, builds on Falco primitives. So when you compare "alternatives," you are often comparing what each vendor adds on top of the same root.

🔧 The application: detection plus response

Falco detects. It does not respond, manage multi-tenancy, or add network detection on its own. You wire that up yourself.

That gap is the real evaluation question. There is a useful parallel from alert investigation: traditional playbooks are just if-and-else decision trees, but real investigation needs improvisation, like a detective following a lead. A rules engine alone rarely gets you there, which is why our AI security assistant and generated playbooks exist.

🛡️ Where EdgeLabs fits

We built EdgeLabs to be Falco-ecosystem compatible, layered above it, so detection AND response both run on the agent. In-line prevention does host-local, time-boxed source-IP blocks, from one minute to ninety days, and it fires even with no cloud backend (EdgeLabs' own product claim). Our Disconnected Mode keeps full detection and response working offline. Falco is a fine baseline if you have the engineering muscle to operate and extend it. If you want the response layer built in, that is the seam we fill, and it is a core reason teams choose AI EdgeLabs.

Q4: How did we evaluate these alternatives, and who is this guide for? [toc=4. Criteria & Who It's For]

I judged every tool on runtime detection method (eBPF versus agentless posture), whether detection and response both run at runtime, cloud dependency (agent-only versus cloud-required), deployment reach (cloud, data center, on-prem, air-gapped), and LLM/AI-agent coverage mapped to the OWASP LLM and Agentic Top 10. No pricing column, no stars, and no numeric ranks. Situations, not scores.

📐 Our evaluation criteria

I kept this list short on purpose. Six criteria decide almost every runtime AI-security purchase, and padding the list just hides the ones that matter.

• Runtime detection method: Does it read live syscalls with eBPF, or only scan posture and config? Runtime is where attacks execute, so this is non-negotiable.
• Detection and response location: Does response run on the agent, or does it need a cloud brain to act? On-agent response is faster and survives outages.
• Cloud dependency: Does it keep working with no cloud backend? This single answer decides air-gapped and on-prem fit.
• Deployment reach: Where does it actually run, across cloud, data center, on-prem, air-gapped, Kubernetes, and GPU inference nodes?
• LLM and AI-agent coverage: Does it map to real agent threats? I reference the OWASP Top 10 for LLM Applications 2025 and the OWASP Top 10 for Agentic Applications 2026.
• Coding-agent runtime coverage: Does it watch AI coding agents like Claude Code, Codex, and Gemini CLI while they run? Sysdig, for one, shipped detections here in March 2026.

🧭 A note on how I scored coverage

One design rule shaped the AI-agent criterion. A safe agent should do at most two of three things: read files, reach the internet, or write and run code. All three together is the trifecta that leaks data.

So I did not just ask "does it mention AI." I asked whether a tool can actually see and stop that third capability turning dangerous at runtime. That is a higher bar, and most tools clear only part of it. Our Kubernetes runtime protection was designed against exactly that bar.

👥 Who this guide is for

I wrote this for the person who now owns AI-agent risk when it runs, not on a slide. If that is you, one of these bullets is your day job.

• AI-workload and LLM-agent runtime-security owners securing agents, MCP servers, and agentic systems in production.
• SMB SecOps and DevSecOps leads making cloud and GPU workloads production-safe without a 24/7 team.
• Kubernetes and container runtime-security owners moving from build-time scanning to live runtime detection.
• GPU-cloud and AI-infrastructure security leads protecting inference nodes and clusters.

⚖️ Meeting you where you are

I have a bias here, and I will name it. I think humans are not the weakest link. If a system blames the operator, someone forgot to build for how people actually work.

So these criteria assume you are busy and skeptical, not that you are a runtime-security specialist. At EdgeLabs, we chose the same criteria to hold ourselves to, including where we fall short, and I will keep flagging those gaps as we go through the tools. If you want to see how that maps to real deployments, our customer case studies show the criteria in practice. The rubric is the master. Every card that follows gets measured against these same six lines, in the same order.

Q5: The 8 best Sysdig alternatives for AI-agent runtime security, compared [toc=5. The 8 Alternatives Compared]

Eight runtime options, judged by constraint, not brand: EdgeLabs (agent-only detection AND response, no cloud dependency), Sysdig (Falco-rooted cloud CWPP), Aqua Security (container and Kubernetes lifecycle), CrowdStrike and SentinelOne (endpoint-first XDR with a cloud module), Oligo (library-level app runtime), Upwind (cloud-native runtime CNAPP), and Falco (the open-source rules-engine baseline). The full comparison table and all eight cards sit in the roundup above.

🧭 How to read the cards

Every card runs the same six criteria in the same order. So you can scan one line, say cloud dependency, straight down the list and compare like for like.

I stayed opinionated on categories and neutral on tools. I will tell you an endpoint-first engine treats runtime as a bolt-on. I will not pretend one good vendor is bad to make us look better. If you want the fuller argument, we lay it out in our take on unified runtime security.

🌐 Two names that get a mention, not a card

Darktrace and Vectra AI come up in these buying conversations, so I owe you a word on them. Both are network-first, cloud-dependent, and heavyweight.

They solve a different problem than agent-level runtime defense for AI workloads. That is why neither gets a standalone card here. Including them would pad a comparison they were never built to win.

🛡️ Where EdgeLabs lands

We put EdgeLabs at position one for one honest reason: this is the lane we built for. One eBPF-based agent consolidates NDR, EDR, IPS/IDS, container and Kubernetes security, vulnerability management, and AI/agent security via Parallax, with detection AND response both running on the agent and no cloud backend required (EdgeLabs' own product claim). Concrete runtime moves include default-deny egress, blocking an agent from calling out unless allowed, and stripping hidden "invisible ink" instructions before they reach the model. Our published Trader Joe's case study reports an 83% MTTR reduction and 573% ROI (EdgeLabs' own figures, self-reported, not independently verified). If your world is pure cloud-console CNAPP, a heavier platform may fit you better, and I would rather say that now. You can also see the mechanics in how our agent works.

Q6: How should you prioritize AI-workload vulnerabilities and prove CRA/NIS2 compliance? [toc=6. Vuln Scoring & Compliance]

Ranking fixes by CVSS severity alone is unsafe when flaws get exploited within a day of disclosure. CVSS is the standard 0 to 10 severity score. Exploited-vulnerability scoring layers CISA KEV and EPSS on top of CVSS and CWE, so you patch what is actually being exploited first. It also turns runtime evidence into audit-ready proof for the EU Cyber Resilience Act (CRA) and NIS2, not posture-only reports.

🎯 The claim: severity is not the same as risk

Here is my top-line view. A high CVSS score tells you a flaw is scary on paper. It does not tell you anyone is using it.

That gap gets people hurt. Attackers do not read your severity spreadsheet, they hit what works, and speed is now the whole story. Our vulnerability management approach was built around exactly that reality.

📊 The proof: exploited fast, patched slow

The numbers make it concrete. VulnCheck's Q1 2025 research found 159 vulnerabilities were exploited in the wild, and 28.3% were exploited within 24 hours of disclosure.

Let me define the two signals that fix this. EPSS (the Exploit Prediction Scoring System from FIRST.org) estimates the chance a flaw gets exploited soon. CISA KEV (the Known Exploited Vulnerabilities catalog) lists flaws confirmed exploited in the real world.

Stack them and the math changes. You stop draining a week on a scary-but-quiet CVE and start with the one that is being used today. Do not mistake quiet for safe.

📋 The payoff: evidence, not attestation

This matters for compliance, not just triage. The CRA (Regulation EU 2024/2847) sets security requirements for products with digital elements, including an Annex I "no known exploitable vulnerabilities" expectation. NIS2 (Directive EU 2022/2555) adds strict incident-reporting duties.

Runtime evidence is what makes those provable. A posture scan says how things were configured. Runtime shows what actually executed, which is the difference between attesting and proving. If you are mapping obligations now, our practical CRA roadmap walks through it.

⚠️ What this approach does not cover

I will be candid about scope. At EdgeLabs, our shipping CRA and NIS2 Compliance Center maps controls to requirement IDs and runs that "no known exploitable vulnerabilities" check, tied to exploited-vulnerability scoring across CVSS, EPSS, CISA KEV, and CWE.

It covers runtime, vulnerability-handling, and detection-and-response controls. It does not cover lifecycle and program controls, like business continuity, backup, HR training, or the CRA's multi-year support obligations. Those stay your job, and I would rather name that than let an audit surprise you. For the wider regulatory picture, see why NIS2 and CRA change the game.

Q7: How should you choose the right alternative, and what should you do Monday morning? [toc=7. How to Choose & Monday Actions]

Choose by constraint, not brand. If you must run self-hosted or air-gapped (disconnected from the internet), prioritize agent-only tools that detect AND respond with no cloud backend. If you are container-heavy, weigh Falco-rooted runtime. If CRA or NIS2 evidence matters, demand exploited-vulnerability scoring, not CVSS alone.

🧭 Three scenarios that decide it for you

Analysis paralysis across eight tools is real. So collapse it into the one constraint that actually rules out options for you.

• You must run self-hosted or air-gapped: prioritize agent-only detection and response that survives a cut cloud link. Cloud-console tools stop being an option here.
• You are container and Kubernetes heavy: weigh eBPF, Falco-rooted runtime and lifecycle depth. Aqua and Sysdig earn a close look.
• Compliance evidence is the pressure: demand exploited-vulnerability scoring and runtime proof mapped to CRA and NIS2 controls.

✅ What to do Monday morning

Every insight should turn into something you can do at your desk. Here is the short list I would run first.

• Map each shortlisted tool to the OWASP Top 10 for Agentic Applications 2026, then mark where each one has no answer.
• Default-deny agent egress. Block outbound calls unless allowed, which breaks the untrusted-content-plus-exfiltration chain.
• Verify eBPF-native detection. Ask to see live syscall detection, not a slide about it.
• Re-rank your backlog by CISA KEV and EPSS, not CVSS alone, and fix what is exploited now.
• Ask every vendor one question: does response still fire if the backend goes dark?

🔭 Where my head is going next

Here is the shift I keep circling. Distributed AI computing forces distributed security, and over the next two years, supervising the LLM agent at runtime stops being niche and becomes the default question every SecOps team answers.

At EdgeLabs, we sit at the self-hosted, air-gapped, agent-only end of that decision tree, and I offer that as one honest fit among several, not the only one. If you are building agentic systems into production, I would genuinely like to hear what you are running and where it breaks. Tell us what you are building, and I will tell you straight whether this is your lane or someone else's.