As the tech industry continues to gather lessons learned from the SolarWinds and now Codecov breaches, it’s safe to say that artificial intelligence and machine learning are going to play a role in the future of DevSecOps. Enterprises are already experimenting with AI and ML with the hopes of reaping future security and developer productivity investments.
While even DevSecOps teams with the budget and time to be early adopters are still figuring out how to implement AI and ML at the scale, it’s time more teams look to the future:
1. Cloud-Native DevSecOps tools and the Data they Generate
As enterprises rely more on cloud-native platforms for their DevSecOps toolchains, they also need to put the tools, frameworks, and processes to make the best use of the backend data that their platforms generate. Artificial intelligence and machine learning will enable DevSecOps teams to get their data under management faster while making it actionable for technology and business stakeholders alike.
There’s also the prospect that AI and machine learning offer DevOps teams a different view of development tasks and enable organizations to create a new set of metrics
Wins and losses in the cloud-native application market may very well be decided by which development teams and independent software vendors (ISVs) turn their data into actionable intelligence. Creating actionable intelligence gives their stakeholders and developers views into what their developers and sysadmins are doing right security and operations wise.
2. Data-Backed Support for the Automation of Container Scanning
As the automation of container scanning becomes a standard requirement for commercial and public sector enterprises, so will the requirements to capture and analyze the security data and the software bill of materials (SBOM) that come with containers advancing through your toolchains.
The DevSecOps teams of the future are going to require next-generation tools to capture and analyze the data that comes from the automation of vulnerability scanning of containers in their DevSecOps toolchains. AI and ML support for container vulnerability scanning offer a delicate balance of autonomy and speed to help capture and communicate incident and trends data for analysis and action by developers and security teams.
3. Support for Advanced DevSecOps Automation
It’s a safe assumption that automation is only going to mature and advance in the future with no stopping. It’s quite possible that AI and ML will take on the repetitive legwork that powers some operations tasks such as software management and some other rote management tasks that fill up the schedules of present-day operations teams.
While AI and ML won’t completely replace their operations teams, these technologies may certainly shape the future of operations team duties. While there’s always the fear that automation may replace human workers, the reality is going to be closer to ops teams becoming more about automation management.
4. DevOps to DevSecOps Transformation
The SolarWinds and Codecov breaches are the perfect prompts for enterprises to make the transformation from DevOps to DevSecOps to protect their toolchains and software supply chain. Not to mention, cloud migrations by commercial and government enterprises are going to require better analytics over development and operational data their teams and projects currently produce for on-premise applications.
5. DevSecOps to NoOps Transformation
Beyond DevSecOps lies NoOps, a state where an enterprise automates so much that they no longer need an operations team, While the NoOps trend has been around for the past ten years, it still ranks as a forward-looking trend for the average enterprise.
However, there are lessons you can learn now from NoOps in how it conceptualizes the future of operations automation that you can start applying to your DevOps and DevSecOps pipelines, even today.
Final thoughts
For the mature DevSecOps shop of the future to remain competitive, it must make the best use of data from the backend systems in its toolchain; SBOMs; and container vulnerability scanning. Artificial intelligence and machine learning are becoming the ideal technology solutions for enterprises to reach their future DevSecOps potential.
