Crush Your AI Network Vulnerabilities
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 CVEs 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.
An agentic approach can help address this challenge: automatically identifying active features from live network configurations and operational state, then matching them against CVE details to surface the vulnerabilities most relevant to the environment.
What Attendees Will Learn
- Why accelerating exploitation cycles are changing how network teams approach vulnerability management
- The technical and operational challenges behind CVE prioritization, including false positives
- Where LLMs can help with CVE assessment and where their limitations remain
- How network configuration and operational data can improve CVE relevance analysis
- How an agentic approach can automate feature-level vulnerability assessment at scale
Who Should Attend
- Network and network security leaders
- Network engineers
- Security engineers