![DR Technology Logo DR Technology Logo](https://eu-images.contentstack.com/v3/assets/blt6d90778a997de1cd/blt4c091cd3ac9935ea/653a71456ad0f6040a6f71bd/Dark_Reading_Logo_Technology_0.png?width=700&auto=webp&quality=80&disable=upscale)
News, news analysis, and commentary on the latest trends in cybersecurity technology.
6 Security-Tech Innovations We're Excited to See in 2022
The details on cybersecurity technologies that we expect to advance rapidly in the coming year.
December 27, 2021
![lightbulb lightbulb](https://eu-images.contentstack.com/v3/assets/blt6d90778a997de1cd/blt7ce6ac8cc48bcaa5/64f15283d5f7ca265265bf91/innovation_tiero_AdobeStock.jpeg?width=700&auto=webp&quality=80&disable=upscale)
Source: tiero via AdobeStock
Who says there's nothing new in cybersecurity? The coming year is looking to be an exciting one as security startups and established players alike bring new technologies to bear on existing and emerging security problems. Many of the most exciting technologies revolve around enabling growing trends in artificial intelligence (AI), data sharing, and digital ecosystem development. They're at the heart of secure digital transformation. Read on for what to expect.
Privacy-enhancing computation is a growing body of encryption, data obfuscation, and privacy technologies meant to help secure data as it's being crunched and handle particularly tricky situations, such as when data is shared within digital ecosystems across geographic boundaries, brand lines, or different corporate entities. Technologies like homomorphic encryption, differential privacy, and trusted execution environments make it possible for various entities to combine and analyze data sets without sharing the data they own in the clear. This will be key for getting the most out of digital transformation while remaining compliant and keeping the trust of customers and partners. Gartner says that by 2025, half of large organizations will utilize privacy-enhancing computation, and 2022 is likely to be a big year for building that momentum.
The latest studies show a whopping 97% of organizations have experienced delays in releasing new applications and software features due to their concerns about API security. The struggle is real, as business needs dictate better integration of applications — both inside and outside organizational boundaries — but security and compliance demands require it be done securely. API security solutions are starting to grow more mature, and the venture funding in this niche over the past year points to more innovation inbound on this front in 2022. Some key investments in 2021 highlighting the trend included $100 million in funding across two rounds for Salt Security in December 2020 and May 2021, a $17 million Series A for 42Crunch in May, and a $20.7 million round to send newcomer Neosec out of stealth in September.
As enterprises increasingly depend on AI modeling for everything from predicting supply chain needs to fraud prevention, the confidentiality, integrity, and availability of AI technology will continue to grow in importance in 2022. Security leaders are increasingly getting their arms around the idea that AI models and AI data have the potential to become the next battlefield of cybersecurity. Fortunately, researchers and innovators are working on bringing some discipline to the field of AI hardening. Earlier in 2021, Microsoft released a new AI security risk assessment framework designed to help improve AI security, which is a solid follow-up to ongoing work by MITRE on a collaborative project called the Adversarial ML Threat Matrix. All signs point to more work in this field unfolding over the next 12 months as practitioners and researchers alike innovate to secure the next generation of enterprise AI tooling.
Major challenges in applying machine learning and AI to cybersecurity include the typical necessity of large training data sets, as well as constant retraining in the face of changing conditions to make the models perform well. Security researchers are trying to get over the hump of these limitations by using siamese neural networks (SNNs) — a type of model that uses smaller sampling of data for better predictions — in order to make usable predictions. For example, at Black Hat USA 2021, a group of Microsoft researchers demonstrated how this type of modeling can be used to detect brand impersonation in phishing attacks. Another piece of research out earlier in the spring demonstrated the use of SNNs for improved detection in intrusion-detection systems.
It’s been at least a decade now since the cybersecurity pundits first declared identity as the new perimeter, but it has taken a while for innovators and practitioners to catch up to this idea in the real world. Now, though? Identity innovation is red-hot, as evidenced by the latest numbers from Omdia, which show that the identity, authentication, and access market grew 13.4% in 2021 to reach $28.9 billion, with lots more runway to go in 2022 and beyond.
That bucket includes everything from maturing privileged access management (PAM) and identity-as-a-service (IDaaS) to increasingly viable-looking passwordless authentication technologies. In 2021, for example, we saw the largest Series A investment in cybersecurity history go to one such company, Transmit Security, which bills its biometric authentication platform as the first natively passwordless identity and risk management solution. There's plenty of space for debate there, but one thing is certain: There's a lot of money and hope at stake for such a technology to make good on that promise.
The explosion in containerization, microservices, and cloud prevalence across the enterprise has stimulated a huge need for improved cloud workload security. Not only are the major cloud and security providers working on folding these protections in their native stacks, but the market is seeing a big influx of new and newly funded startups seeking to bring their cloud workload protection innovations to the market. The most dramatic and recent example of this was the staggering $1.3 billion funding round in November for Lacework, a developer of automated containerized workload defense.
The explosion in containerization, microservices, and cloud prevalence across the enterprise has stimulated a huge need for improved cloud workload security. Not only are the major cloud and security providers working on folding these protections in their native stacks, but the market is seeing a big influx of new and newly funded startups seeking to bring their cloud workload protection innovations to the market. The most dramatic and recent example of this was the staggering $1.3 billion funding round in November for Lacework, a developer of automated containerized workload defense.
Who says there's nothing new in cybersecurity? The coming year is looking to be an exciting one as security startups and established players alike bring new technologies to bear on existing and emerging security problems. Many of the most exciting technologies revolve around enabling growing trends in artificial intelligence (AI), data sharing, and digital ecosystem development. They're at the heart of secure digital transformation. Read on for what to expect.
About the Author(s)
You May Also Like
CISO Perspectives: How to make AI an Accelerator, Not a Blocker
August 20, 2024Securing Your Cloud Assets
August 27, 2024