Today, computer networks have open backdoors everywhere — especially in remote locations — even though a lot of capital is being spent in ineffective, time- and resource-consuming tools and processes to protect them.
The number of attached network devices is exploding and each one of them has the potential to undermine even the most rigorous network security practices. Most enterprises have no visibility into 80% of the devices attached to their networks.
Current cybersecurity products are clearly not sufficient to curb the increasing threats, in great part because they use old, static, techniques that are outdated and ineffective.
In essence, Suavei was borne out of 3 fundamental issues we identified in other active vulnerability scanning products:
- They don’t identify the devices accurately and reliably
- They can’t handle slow network environments
- They cannot be trusted to scan sensitive (e.g. medical) devices
Suavei accurately identifies all devices that are connected to a network without the installation of any additional hardware or software agents.
You wouldn’t trust a self-driving car that had no cameras and relied on databases to figure out even the most basic things like the speed limit. Reliable self-driving cars are being made possible by the use of Deep Learning techniques like categorization and image recognition. It is no surprise that antiquated active scanners that rely on signature databases to identify devices are unreliable and ineffective. Suavei is different — it uses the same Deep Learning techniques that self-driving cars use, but to identify and navigate network-connected devices.
The Suavei platform actively scans all devices attached to a network, correctly identifies them, self-calibrates and continuously improves using AI.
Suavei provides the only combination of an Enterprise-ready (multi-tenant, multi-site, hierarchical Role Based Access Control, unlimited scalability) product with a fast, everything-in-under-3-clicks, streamlined UI for Security Operations Center users.
We are also built for integration, as a critical piece in an effective “Defense in Depth” cybersecurity strategy. We provide a fully featured, richly documented JSON REST API that is public and available for both integration partners and customers to leverage.
Out of the box, we also provide complete reports that can be distributed internally as well as provided to auditors, including to fulfill regulations like California’s SB327.
Besides using AI for device identification, we use it to self-optimize bandwidth usage making it the only option for automated assessment of devices on low bandwidth and/or high latency networks.
The scanning engine we created is therefore unique, patent-pending, and extremely flexible; besides using Deep Learning to identify devices, we can also leverage traditional vulnerability signatures (written in the industry standard Nessus Attack Scripting Language aka NASL), including both from public sources and custom, premium signatures provided by integration partners.
Finally, as part of our out-of-the-box UI, we provide our users with a curated Threat Intel feed, that leverages public sources out of the box, but can also be customized and augmented by integration partners with premium content.
There are already 7 Billion Internet-connected devices in the world, and, according to Ericsson, by 2022 there will be 17.6 Billion – on average, two for every human.
– Afonso Infante CTO, Suavei