Crush Your AI Network Vulnerabilities with Titan Exposure Management
AI has collapsed the time between vulnerability disclosure and active exploitation from weeks to under 24 hours. Your patching process was not built for that. The severity of this shift has not gone unnoticed in recent reporting on data breaches. In fact, Verizon’s 2025 Data Breach Investigations Report found that exploitation of vulnerabilities in network edge devices grew nearly 8x year-over-year from 3% in 2024 to 22% of all breaches in 2025.
For network teams, this creates an unsustainable burden: coping with thousands of CVE’s published every week, classified only by OS and version. Without feature-level context, teams face an avalanche of potential upgrades, but with no reliable way to know which ones actually apply to their environment.
AI-powered CVE analysis can extract feature-level details from vulnerability descriptions, but that information is only useful if it can be matched against real device configurations at scale. Manual validation across hundreds or thousands of network devices is not a viable path forward.
Gluware’s Titan Exposure Management solves this with an agentic approach: automatically identifying active features from live network configurations and operational state, then matching them against CVE details to surface only the vulnerabilities that truly matter to your environment.
What Attendees Will Learn
- Why AI is making network vulnerabilities dramatically more dangerous and time-sensitive
- The technical and operational challenges of accelerating exploitation cycles, including the false positive problem
- What LLMs can and cannot do for CVE assessment
- How Gluware’s “ground up” approach to network automation enables more accurate CVE relevance analysis
- What Titan Exposure Management is, how it closes the CVE relevance gap, and a demo to showcase its capabilities so you can crush your AI network vulnerabilities
Who Should Attend
- Network and network security leaders and engineers