Build and Deploy

Securing AI-generated code, validating AI models, testing AI applications, and enforcing secure build and deployment controls.

Validating AI Artifacts and Securing AI-Enabled Software Delivery

Artificial intelligence introduces new security considerations into the build and deployment process. AI-generated code, machine learning models, prompts, agent configurations, and supporting dependencies must be validated before they are incorporated into production software.

Phase 2: Build and Deploy focuses on ensuring that AI-generated code and AI application components are securely built, tested, verified, and authorized for deployment.

This phase extends traditional CI/CD security practices to address AI-specific vulnerabilities, model integrity, adversarial testing, agent permissions, and the traceability of AI-related software artifacts.

The objective is to establish automated security controls that verify the integrity and security of AI-enabled software before it reaches production.

Phase 2 Outcomes

By implementing these practices, DevOps teams establish automated security controls for validating and deploying AI-enabled software.

Expected outcomes include:

  • AI-generated code is validated using established secure development and testing practices.
  • AI models and supporting artifacts are verified for integrity and provenance.
  • AI applications are tested for prompt injection, unsafe behavior, and unauthorized tool execution.
  • Autonomous agents and AI services operate within defined deployment permissions.
  • Software releases include traceable AI artifacts, dependencies, and security evaluation results.
  • Deployment authorization and security policies are consistently enforced.
  • Security evidence is available for ongoing vulnerability management and compliance assessments.

These outcomes help ensure that AI-enabled applications enter production with verified artifacts, tested security controls, and traceable release information.

The following frameworks provide guidance relevant to Phase 2 AI security activities.

Framework Relevant Guidance
NIST SP 800-218 – Secure Software Development Framework Secure software production, security testing, artifact protection, and vulnerability identification.
NIST SP 800-218A – AI SSDF Profile AI-specific secure development practices, including AI model security and lifecycle considerations.
NIST AI Risk Management Framework AI risk measurement, evaluation, validation, and risk management.
OWASP Top 10 for LLM Applications Prompt injection, sensitive information disclosure, supply chain vulnerabilities, excessive agency, and other AI application risks.
OpenSSF Model Signing Signing and verification of AI model artifacts to support integrity and provenance.
SLSA Software supply chain integrity, build provenance, and protection against artifact tampering.

These frameworks and projects provide guidance and technical capabilities that can support AI security controls. They do not all establish mandatory regulatory compliance requirements.

Integrating AI Security into the CI/CD Pipeline

AI security controls should be incorporated into existing CI/CD automation rather than implemented as disconnected manual processes.

This section will cover a typical AI-enabled software delivery workflow to include:

AI-Generated Code Validation

Validating AI-generated code through automated security testing, dependency analysis, regression testing, and controlled CI/CD release gates.

AI Model and Artifact Integrity

Verifying AI model provenance, signatures, artifact integrity, and trusted model distribution throughout the CI/CD pipeline.

AI Security Testing and Red Teaming

Evaluating AI applications for prompt injection, sensitive data disclosure, excessive agency, unsafe outputs, and adversarial security risks.

Secure AI Deployment

Deploying AI applications and agents with least-privilege access, secure configurations, secrets protection, workload isolation, and release authorization.

AI Release Evidence and Inventory

Maintaining traceable inventories of AI models, prompts, dependencies, configurations, security evaluations, and deployment evidence.