Delivering on the Promise of AI-Powered DevSecOps for the Enterprise

In the previous post, we introduced the concept of AI-powered DevSecOps for the enterprise and explained why it will be far more impactful than other buzz-worthy AI apps and tools. We explained why its benefits are a win for all stakeholders—from developers and DevSecOps managers to IT managers and business leaders.

The unique capabilities of Digital.ai solutions can transform the hype and promise of AI into real-world business results. Let’s take a closer look and see what is required to make AI-driven software development possible across the enterprise.

There are three core requirements for elevating AI to an enterprise-scale capability:

1. Establish responsible AI and maintain control of your data: Organizations need to apply the lessons learned from early, small-scale experimentation with AI-powered app development to larger-scale DevSecOps practices. These include how to set policies for proper use and how to keep control of your data.
2. Empower developer innovation with the right governance: When investing in AI to help accelerate developer innovation, organizations need to be thoughtful about how AI impacts governance and the broader software development lifecycle (SDLC).
3. Increase team output while managing OpEx: As AI helps expand the opportunities for developer creativity, organizations need to monitor and manage the technology sprawl and redundant spending closely.

Here’s Where Digital.ai Fits In

Digital.ai was formed to support enterprise-scale initiatives. We help customers ramp up software development and delivery, manage DevSecOps tooling complexity, and harmonize processes across the software development lifecycle. We both infuse AI into our solutions and help customers manage the complexities of leveraging AI tools and concepts like generative AI and code-assist.

We have quickly become the market leader in enterprise-scale AI-powered DevSecOps solutions, and our products are now being used by 50% of Fortune 500 companies. Our broader customer portfolio includes more than 1100 global enterprise customers. The companies that joined forces to create Digital.ai have more than 50 years of combined market experience.

In the realm of AI, we focus on three main areas:

1. Test, secure, and govern AI-generated software: AI and software governance have had little to do with each other—until now. Digital.ai offers expert guidance on a number of testing, security, and governance challenges, including:

• • Regulation and compliance: We help organizations implement policies to ensure that AI-generated code complies with relevant standards, laws, and regulations.
  • Quality assurance and security: Organizations will establish QA protocols through automated continuous testing that can absorb the 2-3x increase in code created by AI.
  • Continuous delivery and monitoring of release pipelines: In the face of increased code and release volumes, we help organizations identify and address security vulnerabilities, improve performance, and adapt to changing requirements.
  • Planning and collaboration: Our approach helps increase communication among teams to manage portfolio dependencies and improve the understanding of where and how AI impacts software planning and delivery.

2. Harness AI for software delivery workflows: To help productivity gains from the rapid adoption of AI-assisted software development, we are utilizing the power of AI to automate further and accelerate software delivery workflows with the upcoming features:

• • Test creation to simplify editing and creating test cases based on updated or new feature requirements.
  • User story generation to automatically turn product descriptions into requirements and user stories.
  • Knowledge Assistance to identify useful information in planning and DevOps repositories.
  • Threat Insight to inform security experts on recommended changes to protected apps by analyzing historical trends.

3. Manage tradeoffs and decision-making aided by AI: Our predictive intelligence solutions apply machine learning algorithms to data across the Digital.ai platform as well as integrated third-party data sets to help predict risk, remove software delivery bottlenecks, and speed up CI/CD pipelines. Solutions include:

• • Flow Acceleration to accelerate DevOps workflows and predict cycle times.
  • Quality Improvement to prevent defect leakage through early detection and assess code quality effectiveness.
  • Change Risk Prediction to identify risky changes, reduce change failures, and allow teams to identify and manage risk before production.
  • Service Management Process Optimization anticipates future service risks, mitigating risks of major incidents.

Once you understand the “why” of moving to AI-powered DevSecOps for the enterprise, the question becomes “how.” In the next post, we’ll offer an overview of the journey and specific advice for making the transition with confidence, speed, and budgets in mind.

If you missed our first post in the series, click here to be introduced to an AI use case that has a large impact on the way we work and live.