By Rajiv Pimplaskar, CEO, Dispersive Holdings, Inc.
Agentic AI changes the network security problem from inspection to authorization. As more traffic is generated by agents, models, and workloads operating at machine speed, the network has to make trust decisions continuously, evaluate policy in real time, revoke access automatically, and keep communications resilient even when no human is in the loop.
This week at Black Hat USA 2026, one theme is dominating the security conversation: how to govern AI agents. Vendors are converging on guardrails, containment, and access controls for the moment an autonomous agent is compromised or over-permissioned. Identity-based microsegmentation, intent-based access control, and network-level quarantine are becoming product categories in their own right, not just features bolted onto legacy controls.
That is not a coincidence. It confirms a shift we have seen building across the market for some time: the old model of inspecting traffic at a fixed point cannot keep up with what enterprise networks have become. That model assumed applications lived in data centers, users worked from offices, and most traffic could be inspected. None of those assumptions hold anymore.
Applications now live in clouds and infrastructure the enterprise does not own. Encryption reduces what inspection can reveal. And more traffic is generated not by people, but by AI agents, models, and workloads communicating at machine speed with no human watching in real time.
The result is an inspection model that sees less of what matters while becoming more expensive to operate. Post-quantum cryptography will only accelerate that shift: as enterprises harden encryption for the quantum era, security models that depend on decrypting and inspecting traffic at a cloud edge become less sustainable. Building bigger inspection points does not solve the problem when the traffic teams need to secure has already moved beyond the places those inspection points can reliably reach.
This is where traditional SASE starts to show its age. SASE was valuable because it consolidated fragmented VPN, SD-WAN, ZTNA, SWG, CASB, and firewall functions into a cloud-delivered model. But consolidation is not adaptation. Routing more traffic through a service edge does not solve the harder problem: AI agents, unmanaged devices, branch assets, thick applications, GPU workloads, and machine-to-machine flows require continuous authorization and resilient transport, not just inspection.
The implication is that SASE cloud should no longer be treated as the default control point. It can remain useful for some web and SaaS inspection use cases, but it should not be the center of gravity for securing AI-era traffic. For AI agents, unmanaged endpoints, branch traffic, thick applications, and workload-to-workload communication, control has to move closer to where risk forms: the endpoint, the browser session, the branch gateway, and the transport path.
AI agents need a secure overlay because enterprise networks will not transform fast enough to protect them on their own. Most organizations will continue to operate a mix of legacy networks, SASE services, SD-WAN, cloud infrastructure, branch environments, and unmanaged access patterns for years. Waiting for that estate to converge is not realistic. Agentic traffic needs a control layer that can sit above existing infrastructure, enforce policy continuously, and protect communications between users, devices, agents, models, applications, and workloads without requiring the underlying network to be rebuilt first.
That shift from inspection to authorization is where Dispersive has been investing. The same design principle applies across two environments: real-time endpoint and identity risk driving network enforcement today, and Dispersive 6.0 extending that model to AI-native communications.
This is not a new direction prompted by this week's announcements. It is already visible in the current Dispersive platform: a live integration proving the model in the field, and Version 6.0, purpose-built for traffic legacy architectures were never designed to see.
The Dispersive Continuous Trust Authorization Network (CTAN) integration with CrowdStrike Falcon® moves enforcement to the network layer itself, driven by continuous, real-time context rather than a one-time login check. CrowdStrike Falcon assigns continuous risk scores based on endpoint and identity signals. Dispersive monitors those scores and adjusts network access instantly, reducing privileges, segmenting activity, or isolating a device the moment risk crosses a threshold, without waiting for a person to act.
That distinction matters because risk now emerges during active sessions, not just at login. Traditional SASE and ZTNA architectures can validate entry, but they often rely on downstream cloud controls or orchestration to respond once conditions change. The model we are building assumes trust has to be recalculated continuously and enforced where the session actually depends on it: at the endpoint, across the browser or application session, and in the network path itself.
The important distinction is granularity. Policy can be enforced at the level of an identity, a managed endpoint, an unmanaged endpoint using a secure enterprise browser, a branch site, or a specific traffic flow. A risky user can have access narrowed. A compromised endpoint can be isolated without disrupting everyone else. Suspicious branch traffic can be contained at the gateway while the branch remains operational. And unmanaged endpoints can still be governed through browser-session controls working with Dispersive enforcement. This is the difference between alerting on risk and autonomously enforcing against it in real time.
We are already seeing this at scale. In a pilot with a financial services company protecting employees, contractors, vendors, and applicants across sensitive business workflows, this approach replaced manual, dashboard-driven response with automated containment in seconds. Previously, isolating a device could take 10 to 60 minutes. Now, policy enforcement operates as an autonomous control loop: risk is detected, policy is evaluated, and access changes in seconds without waiting for analyst review or manual block provisioning. The result is precise remediation: one compromised device can be quarantined without disrupting anyone else's access.
The same context-first philosophy shaped Dispersive 6.0, built to protect one of the fastest-growing and hardest-to-see categories of traffic on modern networks: AI agents, model inference calls, and machine-to-machine communication between models, GPUs, and infrastructure. Legacy VPN, ZTNA, and SD-WAN architectures were designed around human users and predictable application paths, not autonomous systems.
The same issue shows up in the rise of enterprise browsers. Browser-layer controls can isolate risky sessions, enforce policy, and reduce data leakage before traffic reaches a cloud inspection point. But browser-only and proxy-only approaches still leave gaps when work moves into native applications, APIs, model-to-model communication, and edge or branch infrastructure. The answer is not to choose browser, endpoint, or network. It is to make them work as one continuous enforcement model.
Dispersive 6.0 extends that model with five capabilities: post-quantum communications across the full stack, traffic that blends in and resists targeting, URL filtering for policy enforcement, a simpler operator experience, and one platform for both legacy and AI-native infrastructure.
The pilot proved that continuous, context-driven enforcement works in a regulated, high-trust industry where the cost of a wrong call is high. The latest platform release addresses the next version of that same problem: traffic where no human is watching, generated by agents and models operating at machine speed.
The practical takeaway is simple: security teams should stop assuming that every important exchange can be routed through an inspection point. In the AI era, the control plane has to understand context, the data plane has to stay resilient, and enforcement has to happen automatically as risk changes — at the level of the identity, managed endpoint, unmanaged browser session, branch, workload, session, or traffic path.
That is the deeper limitation of legacy SASE. It centralizes security at the service edge just as the control point is moving outward: into the endpoint, the browser session, the branch, the workload, and the transport layer. The next architecture will follow the session, adapt as risk changes, and make the path itself harder to observe, intercept, or disrupt.
There is also a sovereignty dimension to this shift. As AI agents operate across clouds, regions, partners, and infrastructure an enterprise may not own, organizations need more than data residency guarantees. They need control over how communications are authorized, encrypted, routed, revoked, and made resilient. Sovereignty in the AI era is not only about where data sits. It is about the control plane and whether the enterprise retains authority over the paths its users, devices, agents, models, and workloads depend on.
Black Hat this week is a useful signal because it confirms the direction of the roadmap rather than changing it. When an industry converges on identity-aware, agent-level containment in the same week, it is a sign that the inspection-point era is ending faster than many organizations have planned for. The opportunity is not to build a bigger inspection point. It is to make the paths enterprise traffic takes resilient, adaptive, and difficult to target from the start, whether the entity communicating across them is a person, a device, or an AI agent acting on its own. That is the network model the AI era requires — and it is already beginning to run in production today.
See how Dispersive Stealth Networking enforces continuous, risk-based access across your network, from users and devices to AI agents and workloads, in real time.
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Header image courtesy of Elchinator from Pixabay.
