AI and cloud security are two hyper-growth industries that are not only foundational to our future, but share similarities in the battles going on right now in the industries between open-source startups and proprietary incumbents. They're on a collision course that's unavoidable.
Let's explore the importance of these similarities on the open source front and share a few ways in which AI will be utilized to further enhance cloud security and present new challenges that security professionals must overcome.
A battle for Open Source Software (OSS) in AI and cloud security is raging daily between startups who fundamentally believe these technologies should be open source first and proprietary incumbents who gatekeep this technology to pursue commercial GTM strategies. The similarities are:
- Transparency and trust: In AI, open-sourcing algorithms allows the community to understand how decisions are made in order to detect any biases. In cloud security, open-sourcing allows the community to have a common baseline for fighting threats.
- Cost savings fuel innovation: In AI, OSS libraries can save companies from having to develop their own algorithms, which jumpstarts vertical startups in the space. In cloud security, OSS can consolidate expensive proprietary solutions from the 75+ tools used on average.
- Community development: In AI, there are open-source libraries such as TensorFlow and PyTorch that are widely used by a large community of developers. In cloud security, there is OSS such as ThreatMapper which is used and improved upon by the community.
Beyond the similarities going on for the battle of the heart of OSS in these respective industries, they are on a collision course due to the amplifying effect AI will have on attacks from threat actors and corresponding detection and response efforts in the cloud.
First, AI can be used to accelerate and exponentially expand the toolkit of threat actors.
Second, AI can be used for detection and response efforts in the cloud.
- It can be used to write steps for remediating compliance configuration issues.
- Writing detection and response playbooks for SIEM/SOAR
- Analyzing large stores of threat intel found across dark web
- Detecting behavioral abnormalities.
- Modeling behavior of human and machine identities to identify anomalies and inappropriate actions
- Modeling the risk surface for exposed assets and assessing vulnerabilities & exposures that might occur due to config changes
The AI in Cybersecurity Market is estimated to be USD $14.1 billion in 2022 and is projected to reach USD $41.94 billion by 2027, growing at a CAGR of 24.36%.
The market is HUGE and the industries will collide. The critical question is whether that future is an open one?
To find out more about how Deepfence supports an open future in cloud security and utilizes AI & ML to improve detection & response, schedule a quick call with our Head of Product, Ryan Smith.