Wherever your runtime program is today,
there's a logical next step.
Most teams don't buy a runtime platform all at once. They start with visibility. Then they harden. Then they automate response. Then they make compliance continuous. Then they scale across distributed sites. The five-stage arc below — modelled on NIST CSF and the NIS2 / CRA implementation roadmap — shows what to turn on first, second, and third inside AI EdgeLabs.
Five stages. One agent. No re-platforming between them.
Each stage adds capability — never replaces it. The same eBPF-based agent collecting inventory in Stage 1 is the same agent enforcing autonomous response in Stage 3 and exporting NIS2 evidence in Stage 4. You move forward without re-architecting.
Know what you actually run.
Both NIS2 risk-management obligations and CRA product requirements assume clear visibility into what is in scope.
The starting point. Most distributed environments cannot answer "what's running where" with confidence — Linux, Kubernetes, GPU nodes, edge devices, containers, and AI workloads all live in different inventories. Stage 1 turns the runtime itself into the source of truth.
A single lightweight AI agent continuously discovers and secures workloads across hybrid cloud, Linux hosts, edge nodes, Kubernetes clusters, and GPU systems — including what is running, where, and which third-party packages it depends on.
Harden the runtime.
"It is not enough to have a bare-minimum security level like a perimeter only — it is a wake-up call to implement multi-layered measures."
Reduce the attack surface. Stage 2 turns the agent from passive observer into an active control. Misconfigurations are flagged and remediated, vulnerable images are blocked at admission, network policies are enforced inline, and AI agent tool calls are checked against your guardrails before execution.
This is where most teams realise they can decommission their first redundant tool — typically a host-hardening scanner, a separate K8s policy engine, or a stand-alone DLP product.
Stop attacks before they execute.
Detection without response is just expensive logging. NIS2 requires detection and response — within 24 hours of awareness.
From "we saw it" to "we stopped it." Stage 3 activates the kernel-level detection stack — eBPF telemetry, network DR, behavioural ML — and the autonomous response layer. Pre-defined playbooks fire instantly; AI-generated playbooks handle novel and APT-class threats with executable remediation in seconds.
This is where MTTR collapses from hours into milliseconds and where your SOC stops triaging five thousand noisy alerts to focus on the five that matter.
Make compliance continuous.
"Auditors and regulators do not accept 'the SOC saw something.' They expect logs, timelines, and verifiable control evidence."
Audit-ready every day. Stage 4 connects everything the agent has been collecting since Stage 1 to a single control map. Coverage and gap analysis are continuous; executive risk-posture reports, regulator evidence exports, and structured incident timelines are generated on demand. NIS2's 24-hour reporting becomes a button, not a scramble.
For CRA, manufacturers can issue post-market monitoring proof and coordinated vulnerability disclosure evidence in the same workflow.
Run everywhere your workloads run.
A master-node architecture secures 50–500 workloads per agent and scales securely to thousands of sites in minutes.
From cluster to continent. Stage 5 takes everything proven in your first environment and replicates it across hybrid cloud, GPU clusters, sovereign zones, edge cells, and air-gapped sites. Because the agent runs on-host with zero data egress, you can deploy the same configuration into environments where most modern security platforms simply cannot operate.
Multi-tenant management, custom integrations, and dedicated SLAs come with the Enterprise tier — typical scale-out is thousands of nodes in a single business day.
Different stages, measurable outcomes.
Each stage produces metrics your CFO and your auditor can both read.
Inventory completeness ≈ 100%
Every host, container, package, and exposed service is accounted for in a single risk-scored view — typically replacing two or three partial inventories from cloud, IT, and OT teams.
Hardening score ↑, attack surface ↓
Critical misconfigurations close, vulnerable images stop reaching production, AI agents stop calling tools they shouldn't. Most teams retire their first redundant tool here.
MTTR: hours → milliseconds
Autonomous response contains threats before your analyst reads the alert. Alert volume drops to a fraction of historical levels — five alerts that matter, not five thousand that don't.
Audit prep: weeks → days
NIS2 / CRA / ISO evidence is generated on demand from continuous telemetry. Quarterly compliance scrambles become export jobs.
Coverage everywhere — including air-gap
Runtime protection lands in environments where cloud-only platforms cannot operate. Same configuration, same telemetry model, same compliance evidence — across thousands of sites.
2 = 20
A two-person security team gains the response capability of a twenty-person SOC. AI-generated playbooks, on-host enforcement, and continuous evidence collection do the work that headcount used to.
Tell us your stage. We'll tell you what to turn on next.
A 20-minute working session: walk through what's already deployed, where compliance pressure is biggest, and where the next bit of automation pays back fastest.