startup

Strategies to Combat Emerging Gaps in Cloud Security

cloud monitoing, cybersecurity, deep observability, network observability, startup

As cloud clients input 2023 with a hybrid presence in multiple clouds, they work on prioritizing techniques to fight rising gaps in cloud security.

Most big agencies are getting access to cloud offerings in numerous public clouds, whilst preserving organization structures and personal clouds of their company’s facts centers.

One of the ways of closing these gaps in security could be adopting deep observability. We have already reviewed a few Deep Observability providers such as Gigamon. While Gigamon probably can be considered a current market leader in this relatively new and small market with under $2B annual market size, they still should watch out for the newcomers who come with shiny new products and great technologies under the hood.

CtrlStack is one of these startups and they recently got a second round of funding from Lightspeed VC, led by Kearny Jackson and Webb Investment Network.

The delivery of features and applications by today’s digital-first companies and developers is accelerating. Teams from information technology operations and software development must collaborate closely to do this, forming a practice known as DevOps. When events occur, they may involve any number of digital environment systems, including operations, infrastructure, code, or any combination of modifications made to any of them.

The CtrlStack platform connects cause and effect to make troubleshooting easier and incident root cause analysis faster by tracking relationships between components in a customer’s systems. Developers and engineers can solve problems quickly by giving DevOps teams the tools they need.

By forming an understanding graph of all of the infrastructure, interconnected offerings, and impact, CtrlStack can supply the full picture while capturing the devices’ modifications and relationships throughout the whole device stack. Using CtrlStack product DevOps groups can view dependencies, measure the impact of modifications and examine occasions in actual time.

Key capabilities of the platform encompass an occasion timeline that permits groups to browse and clear out out extrade occasions, without having to sift via log documents or survey users, and a visual representation that offers insights into operational data. Both of those capabilities additionally force dashboards for builders and DevOps groups.

Developers can also access their dashboards that give visibility for any modifications to code commits, configuration documents, or function flags, – all in one click. DevOps groups get a dashboard for root reason evaluation that permits them to seize all of the context for the time being they came about with a searchable timeline of dependencies displaying the whole impacted topology and impacted metrics.

Metrist raises $5.5M for eBPF-based cloud monitoring

cloud monitoing, investment, monitoring, network observability, startup

Metrist, a startup with DevOps roots, raises $5.5M to help companies to deal with cloud services outages. Metrist was founded by two DevOps veterans, Jeff Martens and Ryan Duffield, whose past experience includes working for New Relic, PagerDuty and similar observability and monitoring companies.

Metrist Founders
Metrist Founders, Image Credit: Metrist

Metrist’s idea is not very original: negotiate outages that vendors’ SLAs do not cover. Surprisingly, there are not too many competitors in this area. Some competition for Metrist’s business comes from Parametric Insurance, which sells insurance policies that include cloud and CDN outages.

In contrast to selling insurance, Metrist is willing to play the role of the trusted arbiter in negotiating outage outcomes with vendors and the affected company.

One of the interesting parts of this story is that according to TechCrunch report Metrist team plans to run an eBPF agent to gather data services a customer runs. There are a few issues associated with this technical approach:

  1. Metrist is going to miss all container deployments, e.g. ECS at AWS or any K8s+dockers infrastructure. It is quite a big part of cloud infrastructure that Metrist won’t be able to observe with eBPF-based agents.
  2. On top of that, eBPF can not see into Serverless deployments, e.g. AWS Lambda-s. This further reduces the world of apps that Metrist can monitor.
  3. And there is a third factor that limits Metrist scale-up: most enterprises become very suspicious once they are asked to run yet another agent on their cloud VM or a barrel metal machine. While companies like PageDuty or New Relic have already overcome this psychological barrier by being on the market for long enough, it still could be a showstopper for a young startup that needs to prove itself to its customers.

Having said this, we wish the Metrist’s team all the success.

Yet Another Investment in a Cloud Network Monitoring and Cyberdefense Startup

cloud monitoing, cybersecurity, investment, network observability, startup

SynSaber has recently announced a $13 million series A investment. SynSaber is an early-stage cybersecurity and network monitoring company that develops OT visibility and detection solutions for machine learning cloud monitoring and network observability. SynSaber develops vendor-agnostic software for critical cloud and edge infrastructure that allows sending OT data to empower SIEM, SOAR, or MSSP. Cloud edge assets are often targeted by cybercriminals and SynSaber provides a new line of defense and a solution for intelligent cloud monitoring on the edge.

The latest round brings total investment in the startup to $15.5 million. SynSaber is well positioned on the industrial asset and cloud edge and network monitoring market. The company expands its global footprint and gains market momentum.

SynSyber’s  H1-2022 report shows the efficiency of the startup’s solution, which uncovers that 13% of CVEs reported in 2022 have no patch or fix currently available from the vendor, while 34% of CVEs could only be patched after a firmware update. Furthermore, 23% of CVEs require local or physical access to the system. These numbers demonstrate the growing need for sophisticated fully automated machine learning cloud monitoring solutions for edge computing, hybrid and private clouds. Intelligent computing edge and cloud monitoring help timely detect infrastructure issues, including security flaws and misconfiguration issues, and fix them before they are exploited by cybercriminals.