The AI development lifecycle is the modern software delivery process in which AI coding agents help plan, generate, and refine application code. It compresses development cycles and shifts much of the work earlier and faster, which means security validation must also move upstream to keep pace with how software is actually produced.
Expanded Definition
AI-DLC refers to the development lifecycle pattern in which AI coding agents assist with planning, code generation, refactoring, test creation, and remediation across software delivery. Unlike traditional development, where security checks often happen after code is written, AI-DLC shifts decisions earlier and accelerates the pace at which code can be produced, reviewed, and revised. That changes the security posture of the entire pipeline: design assumptions, secrets handling, dependency selection, and release approvals can all be affected before a human engineer fully inspects the output.
Definitions vary across vendors and teams, because some use AI-DLC to describe any AI-assisted software engineering workflow, while others reserve it for agentic, tool-using systems with execution authority. NIST Cybersecurity Framework 2.0 is useful here because it frames security as an ongoing governance and risk activity, not a phase that begins after implementation. In practice, AI-DLC is best understood as a delivery model that compresses engineering time while increasing the need for policy, review, and verification at the point of generation. The most common misapplication is treating AI-generated code like a finished developer draft, which occurs when teams skip provenance review, dependency checks, or threat modeling because the output appears syntactically correct.
Examples and Use Cases
Implementing AI-DLC rigorously often introduces governance overhead, requiring organisations to weigh faster delivery against tighter review, traceability, and approval controls.
- An AI coding agent drafts a service endpoint and its unit tests, but the pipeline blocks merge until the code is scanned for insecure calls, hardcoded secrets, and risky library imports.
- A product team uses an AI assistant to generate infrastructure-as-code, while security requires policy-as-code validation before deployment to catch excessive permissions or exposed network paths.
- A platform team uses NIST Cybersecurity Framework 2.0 to align AI-assisted development with governance, change control, and risk review expectations.
- An engineering group lets an AI agent propose patches from issue tickets, but human reviewers must confirm that the patch does not break authentication, logging, or dependency integrity.
- A release team uses AI to accelerate test creation, then adds manual verification for edge cases that an LLM may not infer correctly from limited context.
Why It Matters for Security Teams
AI-DLC matters because it changes where risk enters the software supply chain. Security teams that still rely on end-stage review often discover that AI-generated code has already propagated into repositories, build systems, and release pipelines before controls can intervene. That creates exposure across secrets management, software composition risk, insecure defaults, and approval workflows. The security challenge is not just code quality; it is control over the generation process itself, including who can prompt, what data the model can access, and what actions an agent can execute.
This term also intersects with identity and non-human identity governance because AI coding agents may authenticate to source control, CI/CD systems, cloud APIs, and artifact registries as independent actors. Those machine identities need scoped permissions, traceability, and revocation paths. The broader lesson is that AI-DLC compresses both development and attack windows, so assurance has to be embedded earlier than in conventional delivery models. Organisational failure is usually recognised only after a bad deployment, a leaked secret, or an unauthorised code change, at which point AI-DLC becomes operationally unavoidable to secure.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-01 | AI-DLC is a lifecycle governance issue where risk must be managed continuously. |
| NIST AI RMF | AIRMF defines AI risk governance concepts relevant to AI-enabled development workflows. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance addresses tool-using coding agents that alter software delivery. | |
| OWASP Non-Human Identity Top 10 | AI-DLC often depends on machine identities used by agents in CI/CD and cloud systems. | |
| NIST SP 800-63 | AAL2 | Identity assurance matters when AI agents authenticate to development and release systems. |
Establish governance that reviews AI-assisted development risk before code reaches release.
