Gluware’s Titan rises to meet Mythos network vulnerability challenge

Anthropic’s Claude Mythos Preview, released earlier this year, showed that AI systems could identify and probe network vulnerabilities at a speed that traditional patch management cycles were not designed to match. Security teams that had operated on 30-to-90-day remediation windows began reassessing that assumption.

That’s the challenge that network automation vendor Gluware is taking on. This week the company announced Titan Exposure Management, a closed-loop agentic capability that determines which devices on a network are actually exposed to a given vulnerability and automates remediation.

The announcement extends the Titan platform that Gluware first announced in late 2025 as a framework for coordinating multiple AI agents safely on enterprise networks. Titan is built on Gluware’s proprietary Device Interface and Automation Layer (DIAL), focused on preventing conflicting changes from simultaneous agent actions. Titan Exposure Management applies the same foundation to a more pressing problem.

“What we wanted to make sure and get out was a use case, a solution that is critically important for all customers that need to be able to respond, especially in a new Mythos, or other AI, attack-driven world, Jeff Gray, Gluware CEO and co-founder, told Network World.

The vulnerability visibility gap

The Mythos release focused attention on a problem that’s familiar to network teams. When a vulnerability surfaces, determining actual device exposure across a large enterprise network is a slow, manual process.

A team running 10,000 devices on an affected OS must go device by device to determine whether the specific feature is enabled, actively configured, and in production use. Plugging an LLM into that problem does not solve it. Gray argues that while an LLM might understand what a particular vulnerability is, it will not understand how relevant it is to an organization’s network.

The consequence is that many CVEs go unremediated. CVEs (Common Vulnerabilities and Exposures) are identified vulnerabilities that have been publicly disclosed. The CVE framework also does not account for vulnerabilities detected directly by AI models, which may never appear in any advisory or bulletin.

“Every model is just going to get smarter. This is not a one-time thing. This is constant,” Gray said. “The only way to be able to respond and hope to be one step ahead, if that’s possible, is to be able to have the capability of a machine response.”

How Titan Exposure Management works

Titan Exposure Management is built on Gluware’s DIAL, which maintains a continuously updated model of network state across the entire fleet.

• Network intent, not configuration snapshots. DIAL performs continuous discovery of device configurations, operating states, and feature-level network intent across 56+ operating systems from 22 vendors, including legacy environments with years of pre-automation history.
• Feature-level CVE scoring. When a CVE or model-detected vulnerability is received, Titan Exposure Management maps it against vendor and OS-specific feature data to produce a per-device exposure score rather than a blanket OS-version match.
• External threat intelligence. The platform integrates EPSS (Exploit Prediction Scoring System) and KEV (Known Exploited Vulnerabilities) data to weight scores against real-world exploitation activity, producing a ranked view of actual fleet exposure.
• Compensating controls. Where patching cannot happen immediately, Titan Exposure Management can deploy ACL changes or network segmentation to reduce the attack surface and buy time. Pre-checks and post-checks are applied through the same DIAL layer at every step.
• Coordinated patching. For high-availability environments, the platform understands HA topology and sequences patches so one device fails over while its peer is updated. “Patching is the answer, and just being able to do it according to what the business needs and in a safe and predictable manner is key, and that’s not going out and doing it manually. That’s part of the machine response,” Gray said.

OpenClaw and the road ahead

Gluware is positioning Titan Exposure Management as one component in a broader platform direction. Frameworks such as OpenShell, NemoClaw, and OpenClaw are expanding the number of agents that can initiate network changes, and not all of those agents will originate within the network team.

Gluware’s MCP server architecture, introduced with the original Titan launch, is designed to serve as the coordination layer for that ecosystem. Third-party agents submit change requests through the MCP interface, and Gluware’s validation engine determines whether those changes are safe to execute before anything touches the network.

“There’s going to be more agents, more vibe coding, that’s out there. How do you get all of this to be able to work together?” Gray said. “There’s got to be a layer that is the arbiter of anything that agents want to do, that developers are going to create.”