One Secure Architecture, Enforced by Your Cloud's Own Controls.

CSPM scans your cloud for misconfigurations and reports them as findings, one resource at a time: this bucket is public, that role is over-permissioned. Your team triages, fixes, and repeats. Detection still matters, but by the time a finding lands, the gap has already been open, and AI keeps shrinking that window. CrowdStrike measured a 29-minute average eCrime breakout time in 2025, with the fastest at 27 seconds (CrowdStrike 2026 Global Threat Report).

Secure cloud architecture works at the level of principles. You declare what must be true, like “nothing in the regulated zone is reachable from the internet” or “agents can't delete production data,” and Native enforces it through the controls your providers already ship. One principle closes a whole class of findings at once.

Some of what Native enforces has no CSPM equivalent at all: third-party access perimeters, production and non-production segmentation, region restrictions, and boundaries around AI services and agents. Nothing is technically misconfigured in those cases, so there's no finding to raise. The boundary just isn't there until you build it.

Yes, and the two work together. CSPM and CNAPP tools find and prioritize risk that already exists. Native manages the secure architecture those tools observe and changes what the cloud will allow, so less risk gets introduced in the first place.

For the categories Native covers, the cloud blocks the unsafe state before a scanner ever sees it. Native also covers boundaries a scanner can't see, like region restrictions or what AI agents can do, because nothing's misconfigured when they're missing.

Cloud security intent enforcement means stating what must be true about your cloud once and having it enforced through each provider's own controls. Instead of writing separate policies inside AWS, Azure, Google Cloud, and OCI, you define the requirement, like “AI services can only be provisioned in approved accounts” or “data can't leave approved regions,” and the provider-specific work is handled for you.

Native maps each intent to the right provider-native controls: SCPs and RCPs on AWS, Azure Policy and RBAC on Azure, organization policies and VPC Service Controls on Google Cloud, Security Zones and IAM deny policies on OCI, and many more. Enforcement runs inside the providers' own infrastructure, so the boundary holds no matter where a request comes from, including an AI agent acting on its own.

Because they're enforced at the provider's core, underneath everything you run. Providers have spent a decade building SCPs, RCPs, organization policies, VPC Service Controls, Azure Policy, and service-specific controls at the identity and network choke points of the cloud. They work at cloud scale, and they're hard to route around. A control in the provider's own authorization path holds against direct console access, a compromised credential, or an agent that decides on its own to take a shortcut.

A provider-native control also applies across every account and workload from one place, with no pipeline to wire up.

Where teams get stuck is operating these controls across four providers while the providers keep shipping changes. Native does that work: it picks the right control for each requirement, validates the impact before rollout, and keeps it current.

Every provider has its own policy language, enforcement primitives, and implementation model. Most organizations end up with strong coverage on their primary cloud and thinner coverage everywhere else, and the weakest provider quietly sets their real security posture.

Native separates intent from implementation. You define what must be true once, and Native maps it to the right controls in each cloud. The same data perimeter becomes SCPs and RCPs on AWS, VPC Service Controls on Google Cloud, and the matching Azure Policy and OCI constructs, without your team learning four policy languages.

Yes, and that's where a lot of teams start. Each provider ships new security services and features every year, and a complete data perimeter or segmentation model on that provider alone can take dozens of coordinated controls across SCPs, RCPs, network policies, and more.

Most single-cloud teams are nowhere near full coverage, because operating those controls by hand doesn't scale. Native models your architecture, shows what's enforced against what could be, maps your intents to controls, validates the impact before rollout, and keeps everything current as the provider changes. AI raises the stakes on one cloud too, since every new model integration adds service and data paths to govern. If you add a second cloud later, you extend the same architecture.

An agent acts through a cloud identity, and it can reach whatever that identity's permissions allow. Agents are probabilistic by design, so give one a goal and broad permissions and it may find a path nobody intended. “Clean up failed jobs” should never turn into purging a production queue.

Native puts the boundary in the cloud, outside the agent. You define which services agents can use, what data and environments they can reach, and which actions stay unavailable, like deleting protected datasets or changing their own permissions. Native maps those limits to the identities that actually do the work, including the Lambda, Azure Functions, Cloud Run, or OCI tool behind each call, so a narrowly scoped agent can't borrow authority from a broadly scoped tool. MCP servers are covered the same way, as another principal with cloud permissions.

AI infrastructure runs on the same cloud architecture as the rest of your estate, so Native governs it with the same controls. That covers which AI services can be provisioned and where, which models can be called, and what data those services can reach. Native enforces each one through the cloud's own identity, network, data, resource, region, and service controls.

The boundary sits in a different place on each cloud:

• AWS (Amazon Bedrock, Bedrock AgentCore, models on EKS): narrowly scoped execution roles, resource policies, explicit denies, and SCPs across member accounts.
• Azure (Microsoft Foundry Agent Service, models on AKS): Microsoft Entra identities, Azure RBAC scoped to specific resources, and database roles in Azure SQL.
• Google Cloud (Gemini Enterprise Agent Platform, formerly Vertex AI, and models on GKE): a dedicated identity per agent and tool, narrow IAM roles, and IAM deny policies.
• OCI (OCI Generative AI, OCI Generative AI Agents, models on OKE): IAM policies scoped to the required resources and compartments, plus narrow SQL privileges.

Native maps one intent, like “only approved models in production” or “no AI service can reach cardholder data,” to each of those. It also covers which AI endpoints are reachable from the internet and whether training pipelines can touch regulated data.

Traditional compliance checks your environment at a point in time, finds gaps, and runs a remediation cycle before the next audit. Cloud environments change constantly between assessments, so you can be compliant when someone looks and out of compliance a week later.

Native maps compliance requirements to security intent and the provider-native controls that enforce it, then keeps the architecture aligned continuously. Controls that block non-compliant configurations are active at provisioning, so the requirements they cover can't slip between audits. When an exception is needed, it's tracked in one place with its business context, so it doesn't become permanent by default. AI workloads sit under the same requirements, which means a new model integration doesn't open a gap your auditor finds first.

A data perimeter is a set of controls that defines which principals can reach your data, from which networks, and through which services, enforced at the organization level instead of resource by resource. It's high-impact and hard to get right, even on one cloud. On AWS alone, a complete data perimeter takes dozens of individual policies across SCPs, RCPs, VPC endpoint policies, and more, spanning six access patterns, and the guidance keeps changing.

Native lets you define the perimeter once and maps it to the coordinated controls each provider needs. Data can't move to unapproved accounts or regions, third parties get in only through approved patterns, and AI services outside the boundary can't pull data from inside it.

IaC and IaC scanning are valuable, and Native works alongside them. Terraform is one of the ways Native deploys controls. But scanning works at the code layer, and plenty of the failures that matter happen outside it. Direct console access, credential compromise, and lateral movement between accounts all bypass your pipeline. Data perimeters, environment segmentation, and region restrictions live at the organization level and aren't set in application code at all. And once AI agents start generating infrastructure changes, betting on the code always being right gets riskier.

Coverage is the other problem. Scanning only works where a scanner is installed, and a large enterprise can run hundreds of pipelines. Native enforces through the provider, so coverage doesn't depend on every pipeline being wired up. It also handles work scanning wasn't built for: validating a control's impact before it's live, tracking exceptions with their business context, and keeping boundaries current as providers ship new services.

Much of a CSPM backlog comes from configurations that should never have been possible. The scanner flags them because the configuration is wrong, the queue grows faster than anyone can triage it, and engineers spend their weeks on resource-level tickets.

Native enforces the conditions that prevent those states. If a guardrail blocks the risky configuration at provisioning, the finding never appears. Teams typically see finding volume drop for the categories Native covers, which frees them for the architecture work that actually moves risk.

Wiz, Orca, and Prisma Cloud find and prioritize risk across your cloud. Native manages the secure architecture those tools observe: you define what must be true, Native maps it to provider-native controls across AWS, Azure, Google Cloud, and OCI, and it keeps that enforcement aligned as things change. The two run side by side. Native shrinks what the CNAPP has to find, and the CNAPP keeps watching for anything that gets through.

Some of what Native enforces never shows up in a CNAPP at all, because a missing boundary between a dev agent and production data isn't a misconfiguration.

Zero trust in the cloud means no identity, whether a person, a service account, or an AI agent, is trusted by default. Access is granted explicitly, scoped to what's required, and enforced continuously. Most organizations have the zero trust concept without the architecture behind it, because the controls aren't deployed consistently across providers and accounts.

Native makes zero trust operational at the architecture layer. You define least-privilege boundaries for every actor, and Native enforces them through each provider's own IAM and network controls across AWS, Azure, Google Cloud, and OCI. AI agents are actors like any other, so the boundary holds regardless of the permissions an agent inherits. Any exception to those boundaries is tracked in one place, with the business context behind it.