Learn how to secure AWS, Azure, and GCP using policy-as-code, drift detection, least-privilege IAM, microsegmentation, and automated remediation.

Zero Networks today announced results from its participation in the Anthropic Cyber Verification Program showing that a frontier AI model was unable to move beyond an intentionally exposed machine after gaining administrator access and extracting credentials. In the evaluation, Zero Networks’ microsegmentation prevented the model from breaching protected systems and identities, containing the attack to the initial point of compromise.

ColorTokens announces the launch of Xshield™ on the CrowdStrike Marketplace, enabling organizations to enhance security through Zero Trust microsegmentation.

IonQ poursuit sa diversification dans la cybersécurité. Le spécialiste américain des ordinateurs quantiques à ions piégés (des atomes chargés électriquement) pour effectuer des calculs, a annoncé le 12 octobre 2026 un accord pour acquérir Elisity, un éditeur de logiciels de contrôle des accès et de microsegmentation des réseaux.

📅 Publication Date: September 2026 | ⏳ Forecast Period: 2026-2033 📊 Market Intelligence Overview | Get the full PDF sample copy of the report: (Includes full table of contents, list of tables and figu...

IonQ has agreed to buy microsegmentation firm Elisity in an all-stock deal worth up to $1.3 billion, including $150 million in earnouts.

Groups Conversations All groups and messages Sign in     Securing Agentic AI with Zero Trust Microsegmentation 0 views Eyal Estrin unread, 11:59 AM (30 minutes ago)    to https://cloudsecurityalliance.org/blog/2026/10/06/securing-agentic-ai-with-zero-trust-microsegmentation Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

Pictured above: Another one of my totally organic, AI-free, hand-drawn “back of a literal envelope” diagrams showing the “limit the blast radius” benefits of network segmentation and microsegmentation...

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Application inventories and network data typically live apart in enterprise IT. Application details sit in application performance management (APM) platforms and configuration management databases (CMDB). Network paths, cloud resources, and security controls sit in separate network tools, cloud consoles and microsegmentation systems. That fragmentation limits what operations teams, and increasingly AI agents, can reliably determine about the infrastructure supporting a given application. It’s a gap that IP Fabric is now aiming to solve. IP Fabric builds a read-only digital twin of enterprise network infrastructure, discovering devices, paths and configuration to give network teams a current model of how traffic actually moves. The company released version 8.1 of the platform this week. The update adds application infrastructure mapping, which connects application workloads to the network and cloud paths they depend on, along with a redesigned cloud-native data model. The release follows IP Fabric 7.9, which expanded discovery and path analysis for Azure and Google Cloud Platform environments. “For the first time, we will now have the service-aware digital twin, meaning that you can ask how this application path looks like today,” Pavel Bykov, CEO of IP Fabric, told _Network World_. ## Where application maps meet the network Application teams and network teams have largely worked from separate maps. “For a really long time, like for decades, the world of applications, systems, databases, code, workloads, and the world of networking, the cables, the connections, the cloud, were separate,” Bykov said. **First-class objects.** IP Fabric 8.1 treats applications, workloads, and flows as first-class objects in the platform. For each application, the system calculates the end-to-end path for every workload it depends on and assembles those paths into a dependency map. **Bidirectional queries.** The mapping works in both directions. Teams can query what infrastructure a given application depends on, or which applications depend on a specific device before it is upgraded or taken offline. **Automated reporting.** The platform can also generate daily dependency reports and pre- and post-change impact summaries, which can feed external automation tools and AI agents. **Troubleshooting example.** Bykov pointed to a common scenario. A link appears overloaded and traffic is dropping, but comparing the current path against the prior day’s model shows the application actually shifted to a different path entirely. IP Fabric has always been able to discover network infrastructure natively, but application data is a separate problem, and the platform does not discover it directly. That data is ingested from outside sources, starting with a partnership with Illumio. Bykov explained that further integrations are planned with other vendors, and application data can also be imported through CSV or API. ## Modeling cloud on its own terms IP Fabric previously captured cloud data and normalized it into a model built around on-premises concepts such as next hops and devices. Bykov said that approach did not hold up, since cloud paths include traffic manipulators, and transformers with no direct on-premises equivalent. **New model, new entities.** VPCs, VNets, subnets, virtual network interfaces, route tables, peerings, and security policies are now first-class entities in the platform rather than elements normalized into an on-premises shape. **Still a translation layer.** A layer still exists underneath, normalizing objects such as transit gateways and load balancers, but into a cloud-native model rather than an on-premises one. **What it enables.** Path analysis strengthens within a single cloud, across peered networks and between cloud and on-premises infrastructure. IP Fabric evaluates AWS Security Groups, network ACLs and Azure Network Security Groups to determine whether a given path is permitted or blocked, and identifies which policy is responsible. “We are working with how the cloud is actually communicating instead of trying to basically massage that model onto the on-prem worldview,” Bykov said. ## AI and the path forward IP Fabric enables AI agents to query the platform via its hosted MCP server, which predates the 8.1 update. What changes with this release is the depth of what sits behind that interface, since agents can now query the same verified application and cloud context described above through the same server. Bykov said the stakes for that access are rising because AI agents are increasingly logging into network devices and making changes directly, which raises the need to validate what those changes actually did. “We all know that AI is only as good as the integrations to other systems, and there is now this one interface to the network,” Bykov said. Asked about direction beyond 8.1, Bykov pointed to continued work on scaling the platform to larger and more complex networks, along with broader vendor and cloud coverage. “General direction is definitely right now more system improvements to cover larger, more complex networks,” Bykov said.

The Cloud Security Alliance published new guidance on Zero Trust Microsegmentation that formally separates two segmentation models: topology-defined (enforcement based on network position like zones, VLANs, and firewalls) and connection-defined (enforcement based on identity, device posture, and context at session establishment). The paper positions these models as complementary rather than competing, and covers scope including IT, OT, IoT, cloud, edge, and AI workloads with distinct granularities of macro/micro/nano-segmentation. The guidance outlines an operational model of continuous visibility, policy derivation, simulation, enforcement, drift monitoring, and exception management rather than treating segmentation as a one-time deployment milestone.