SHARED INTEL: VCs pumped $21.8 billion into cybersecurity in 2021: Why there’s more to come

SHARED INTEL: VCs pumped .8 billion into cybersecurity in 2021: Why there’s more to come

Earlier this year, analysts identified a number of trends driving the growth of cybersecurity. Among them: an expanding digital footprint, growing attack surfaces, and ever-increasing government regulation.

Related: Taking API Proliferation Seriously

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Last year we saw a An unprecedented $21.8 billion in venture capital invested in cybersecurity companies worldwide. Investors more than doubled in 2021, increasing investment by about 145 percent.

Based on the early-stage startup launches we’re seeing in Differential ventures, this trend is not going to stop in the short term. The main drivers for the continued growth of cybersecurity are: the growing need to protect the API supply chain, the inadequacy of existing identity management systems, and the unfulfilled promise of AI-based cybersecurity systems. in data.

API protection

The SolarWinds attack made API supply chain security a front-page news story in 2020. Major breaches at Parler, Microsoft Exchange Server, Experian, and LinkedIn increased the intensity of supply chain attack concern of APIs in 2021. The Log4j vulnerability reported in late 2021 further raised the concern. According to Gartner, a triple increase from 2021.

Given all this new concern about API supply chain security, where are the tools to solve this problem? Current tools are inadequate, brittle, statically rule-based, and require a lot of manual intervention and processing. Every week, we see a new release for an API supply chain security startup. Many of them are pre-product and still in the design stage. But they are founded by highly skilled and experienced cybersecurity experts, and are likely to transform the API supply chain security landscape in the years to come.

Improved identity management


For a long time, enterprise customers have been dissatisfied with cybersecurity solutions for identity management. Existing systems suffer from clunky interfaces, an overwhelming IT management burden, and oscillations between being too permissive and too promiscuous. COVID-driven remote work has made the issue of identity management systems a much higher priority. Furthermore, the growth of assets stored in digital wallets, as well as the promised growth of the metaverse and other Web 3.0 projects, makes the urgency for more robust and portable identity management systems all the more imminent.

Existing tools that attempt to manage user identities and access permissions are proving inadequate, leading frustrated IT administrators to become cybersecurity entrepreneurs. Many of the startups trying to tackle this vexing problem offer the promise of data science and machine learning to automate the identity management process, yet none of them have the data collected to demonstrate the accuracy and robustness of their proposed solutions. .

Still, given the impact that data science has had on other areas of software development, it seems likely that in the next few years one or more of these proposed solutions will produce a significant improvement in identity management systems.

Leveraging data science

Almost all cybersecurity startups submitted to our fund promise artificial intelligence embedded in their software, powered by data science trained on cybersecurity data. These releases fall into two categories: pre-product companies and companies with working prototypes of their solutions. The one common point in almost all of these systems: They still don’t have data to train their models, let alone prove that their approaches will lead to improvements over state-of-the-art static systems.

Data science has improved software performance in many industries, but fails in many cases. The only way to know if data science will lead to improvements is to collect the appropriate data, annotate it (if necessary), and analyze the annotated data to see if there is information in the data that can reduce the uncertainty of the phenomena to be predicted. . If that analysis leads to a positive outcome, you still need to train models on that data and figure out how to integrate those models’ predictions into software to produce insights that solve existing problems better than current systems.

With enough resourceful cybersecurity software developers and data scientists collecting data, iteratively building models, and using these models to address puzzling unsolved or poorly resolved cybersecurity problems, they will inevitably find ways to make a significant impact on those problems, and a minority of startups being funded today will have the opportunity to become unicorns in the next few years.

The recent swoon in public tech stock markets may lead one to predict a pause in funding for cybersecurity solutions, along with a drop in valuations. However, I believe that the impact of the market correction will be offset by the growing need for new solutions to many cybersecurity problems, and by the ingenuity of new approaches being taken to solve these problems.

About the essayist: David Magerman is co-founder and managing partner of Differential ventures. He was previously at Renaissance Technologies, a quantitative hedge fund management company.

*** This is a syndicated Security Bloggers Network blog from the last watchdog written by bacohido. Read the original post at:

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