Gluware will showcase Titan Exposure Management, a closed-loop agentic Mythos threat defense to eliminate false positives and patch active vulnerabilities, at Red Hat Summit and ONUG AI Networking Summit
Gluware, the leader in intelligent network automation, announced Titan Exposure Management, a new capability in its Titan AI platform that solves one of enterprise network security’s most persistent operational failures: the inability to accurately assess which devices on a network are actually affected by a vulnerability, and act on that assessment at speed. The new closed-loop agentic remediation capability compresses weeks of manual vulnerability investigation into minutes, with safe and validated outcomes at every step.
The urgency has never been greater. AI-powered exploitation tools – most recently exemplified by Anthropic’s Claude Mythos Preview – are accelerating attackers’ ability to identify and exploit network vulnerabilities faster than human teams can respond.
“Mythos is overturning decades of patch management assumptions. Previously, when a CVE was issued, teams could assume plenty of time would pass before attackers could exploit the vulnerability, allowing for 30 to 90 day patching cycles,” said Jim Frey, Omdia Principal Analyst, Enterprise Networking. “Now, the window between identification to exploitation is collapsing, and every vulnerability could become a zero-day issue. With AI-powered exploitation methods looming on the horizon, time to remediation is more important than ever. And with the likely coming tsunami of CVEs being issued, network teams are further at risk of being overwhelmed by false positives.”
Current CVE management is blunt by necessity. Without visibility into actual device configurations and network state, security teams are forced to either apply broad OS updates across the entire network as a brute-force effort or rely on engineers to manually investigate each vulnerability. Either approach breaks down at enterprise scale and leaves most CVEs unremediated.
Gluware’s threat exposure management solution is anchored to something no other vendor provides: a continuously validated map of every device’s configuration and operational state across the entire network — even legacy networks that have years of pre-automation history. That foundation is what makes feature-level CVE assessment possible – and remediation both efficient and trustworthy – whether executed by a network administrator, an automated workflow, or an AI agent.
The capability will be offered with the General Availability of Gluware Titan AI in early June, 2026.
“We all know that AI is rapidly evolving the capacity of attackers to find and exploit network vulnerabilities, as shown by Mythos,” said Jeff Gray, Gluware CEO and Co-Founder. “We also know that AI and agentic frameworks hold tremendous potential to unpack the meanings of CVEs beyond OS and version. The missing piece is reliably mapping what’s in the network to CVEs so that network admins, automated workflows, and agents can confidently validate remediation steps and take action. This new Titan AI Exposure Management capability changes the game.”
The Gluware solution is built on continuous discovery of network devices, operating systems, configurations, and network state, which is translated into a working network intent model that spans any legacy enterprise environment, including 56+ network operating systems from 22 vendors. This foundation, delivered by Gluware’s proprietary Device Interface and Automation Layer (DIAL) technology, is what makes the full remediation cycle trustworthy. Unlike multi-party integrations that operate without a validated understanding of the live network, Gluware anchors every CVE assessment and remediation action to verified network state. The result is accurate remediation that avoids wasteful false positives.
Titan Exposure Management translates that network intelligence into vendor and OS-specific feature mappings that can be matched against CVE advisories, identifying precisely which devices are affected and in what way, without manual investigation. That intelligence is accessible to network administrators directly through the Gluware platform, through automated workflows, and via Gluware’s MCP server to agentic platforms such as OpenShell and OpenShift AI used for CVE analysis and remediation.
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The result is a genuinely closed-loop process: from iterative vulnerability discovery and validation against the live network, through to execution of proposed remediations with built-in safeguards to eliminate false positives and deliver efficient, predictable outcomes.
For enterprise security teams, the operational impact is significant:
- Elimination of false positives that would otherwise burden the network with thousands of OS upgrades per week
- 100x improvement in time to remediation for relevant network vulnerabilities
- Reduction from 98% unremediated to fully remediated CVEs
- Dramatically reduced compliance liabilities
- Measurable security posture improvement for security-sensitive industries
“Our customers have consistently demonstrated that our platform offers 10x improvements in speed, accuracy, and coverage for network remediation,” said Ernest Lefner, Gluware Chief Product Officer. “By unleashing the potential for safe and predictable agentic analysis and action, customers will get an additional 10x impact to the reliability and security of their network operations.”
Further AI Innovations in Gluware Titan
Alongside the closed-loop CVE remediation capability, Gluware announced additional AI-driven capabilities included in the forthcoming General Availability of Titan. Using natural language, network teams can now perform tasks that previously required specialized scripting expertise or significant manual effort, including:
- Dynamic compliance audits: “Generate a multi-vendor audit for security measures, such as checking SNMPv3 passwords.”
- Network state assessments: “Check for interface errors on Palo Alto, Cisco, and Juniper devices.”
- Network automation building: “I want a self-service form to present a drop-down that only allows our operators to choose a VLAN in a specific range.”
- Report generation: “How many BGP peers are up and running?”
- Regex creation and output validation: “I need to search for these parameters and have the output look like this.”
- Data model generation and network business logic into code into the Gluware IDE
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