When teams move from deciding on their migration strategy to mobilizing to act, agentic AI can be used to enforce secure-by-design practices and policies. Welcome back to the second in our two part series on using agentic AI for DevSecOps to drive secure-by-design architecture. (If you missed part one, check out our previous post The Speed of Relevance: Laying the Foundation for Strong DevSecOps Practices)

Infrastructure as Code Delivers Automated Compliance Enforcement

Secure-by-design principles are enabled through both infrastructure as code and robust security testing practices. First, deployments via infrastructure-as-code (IaC) produce consistent, repeatable, and hardened environments, reducing misconfigurations. This addresses a common security weakness. Additionally, these pipelines generate detailed logs and audit trails.

Furthermore, pipelines can embed policy-as-code and compliance-as-code frameworks, continuously validating that builds align with standards like NIST, CMMC, STIG, RMF, or agency-specific security baselines. They can also build custom workflows and templates that ensure their DevSecOps agents work consistently following their internal guidelines and processes.

There are many examples of how teams can use preconfigured DevSecOps templates and workflows to enhance their security:

  • Pipeline templates that enforce secure configurations by default (e.g., encryption turned on, least privilege IAM roles, logging enabled) across every environment.
  • Workflows that eliminate hardcoded credentials by integrating with vaults and key management services, ensuring sensitive data is injected securely at runtime.
  • Automated pipelines that support rolling updates and security patching, making it easier to quickly remediate vulnerabilities without manual intervention.

Automated Security Testing Provides Guardrails

The other component is security testing. Using ReDuX AI agents in combination with other automation tools, DevSecOps teams use security testing (static code analysis, dependency scanning, secret detection) early in the pipeline, ensuring vulnerabilities are caught before deployment.

Moreover, for every task performed by any ReDuX agent, output can be verified and corrected by a human team member. The self-learning agents improve their process, further improving efficiency gains. And because in enterprise implementations of ReDuX agents share skill improvements across the digital workforce, all agents improve when one agent improves. This process further enhances security beyond what is available with simply co-pilot tools or AI agents used for a single step of the process.

Ultimately, by both using OODA loops as described in our first post and automated compliance, teams can use AI for end enforcement of DevSecOps best practices. In addition to this, one of the most important ways DevSecOps supports security best practices is it fosters a culture of continuous improvement and collaboration, particularly between developers, security, and operations. At its core, DevSecOps best practices shift important security decisions to the left, moving them earlier in the process so that the tools use by modernization teams have the greatest impact at reducing risk.
If you want to see how your team can accelerate decision making and modernize with secure-by-design architecture, schedule a live demonstration with our team. And if you are headed to the AFCEA Belvoir Industry Days May 5-7, 2026, Let’s talk!