By Ernest Lefner, Chief Product Officer, Gluware
AI is pressuring Network Patch Management
Anyone who has had to do patch management for networks knows how labor-intensive it can be. Gluware already makes that far better through closed-loop automation. In fact, we have customers who are achieving 100% remediation rates.
But the pressure on patching vulnerabilities is going way up. Fast. AI platforms, like the recently announced Claude Mythos Preview, are cutting the time between discovery and exploitation of vulnerabilities to under 24 hours.
The problem with that is the amount of potential OS upgrades needed. Why? Because there are thousands of CVE’s published per week. And CVEs are only classified by OS and version.
If you only have that information to go on, you might be facing an avalanche of upgrades in order to stay “safe.” Too many to do, really. How do you make the right choice? It’s an impossible situation. When we talk to customers at large enterprises, this potential has really gotten their attention.
AI Can Help, But Quickly Runs into a Wall
That’s where AI analysis comes in. The gold in CVEs lies buried in the human-readable details published in them. But if engineers have to review all of those, you’d never keep up. But AI can do it fast.
However, there’s an additional challenge. The details that engineers and AI read point to features. That means that if you know which features are actually deployed on the machines with the relevant OS and version.
Sadly, your Joe Blo AI noodling machine doesn’t know that about your network. It’s not connected to your network. And after what happened to PocketOS, would you actually want to just connect some AI/agent to your critical network infrastructure with no filter or guardrails? (Hint: NO!)
But it gets worse, because your network doesn’t know about features either. Configurations don’t self-interpret to features named in CVEs. Features are essentially like metadata tags for collections of configurations. Sadly, your configs don’t have that metadata. Whomp whomp.
Oh and then it gets even worse, because even if a configuration is present on a relevant device, it’s not actually vulnerable unless it’s operationally active in the running device state. Bad news here too–there’s no mapping of such active operating configurations to features either. That is, to quote George Orwell’s 1984, double plus ungood.
Your AI analysis just ran into a wall of real network operations. Is there a possible way around this using AI? Maybe. And only partially, and only if you have a totally homogeneous network. Please fist pump and step forward three paces if you’re lucky enough to qualify. Otherwise, you can join the vast majority of us sad robots in the back.
Gluware Titan Exposure Management
Titan Exposure Management is a new capability of the 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.
This new remediation capability compresses weeks of manual vulnerability investigation into minutes, with safe and validated outcomes at every step.
Titan Exposure Management is the culmination of many years of building from the ground-up in order to deal with the messy realities of automating brownfield networks. That includes:
- Continuous discovery of network devices, operating systems, configurations, and network state
- Translation of imperative configurations and operational device states across a heterogeneous network into a working intent model that spans any legacy enterprise environment, including 55+ network operating systems from 22 vendors.
This foundation, delivered by Gluware’s proprietary Device Interface and Automation Layer (DIAL) technology, is what makes it possible to run a closed-loop, full remediation cycle that is trustworthy.
Titan Exposure Management uses Gluware’s unique insight and intent modeling capabilities to convert configuration and device state into vendor and OS-specific feature mappings that can be matched against CVE advisories. This allows humans, machines, and agents to identify precisely which devices are affected and in what way, without manual investigation.
We make that intelligence 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.
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.
The Big So What: 100x Impact
To start with, Titan Exposure Management makes it possible for network teams to efficiently and accurately counter the predations of AI-powered vulnerability discovery and exploitation. Let’s spell the impact out in more detail:
- 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
We like to recount how customers have told us that the Gluware platform has 10x’d their capability as a team to keep their networks clean, policy-compliant, standards-based, and up-to-date. In the realm of network threat exposure management, we believe that this new capability does another 10x. In other words, compared to manual approaches, this is a 100x force
multiplier.
