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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThe AI software development lifecycle is a software delivery lifecycle with AI-assisted work and additional checks for AI-related risks. Teams still plan, develop, test, release, deploy, and operate software; they also need to assess data, intended use, model behavior, and impacts across those stages. There is no single universally mandated AI SDLC. A practical approach combines a delivery workflow with AI-specific evaluation, governance, and monitoring.
How the lifecycle models fit together
NIST’s notional DevSecOps model uses seven delivery phases: Plan, Develop, Build, Test, Release, Deploy, and Operate. It is an end-to-end reference model, not a universal standard. NIST’s AI Risk Management Framework (AI RMF) describes AI-related work through tasks such as establishing design and context, handling data and inputs, developing models, deploying systems, and operating and monitoring them. It also emphasizes testing, evaluation, verification, and validation (TEVV) throughout the lifecycle.
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These views complement one another. The delivery phases help organize how software moves from an idea into production; the AI RMF view highlights the additional decisions and evidence an AI system may require. The stages below group related delivery phases for readability, while retaining their distinct purposes. Governance, human accountability, and security apply across every stage rather than appearing only at the end.
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Start by establishing what problem the system is meant to solve, for whom, and under what conditions. Define intended users, objectives, requirements, operating context, constraints, and risks before deciding that AI is the right approach. A clear problem statement also gives reviewers a basis for judging whether a proposed system or change is useful.
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- Specify intended use and important uses the system is not meant to support.
- Identify stakeholders who can set priorities and approve requirements.
- Record operational, security, and other relevant constraints and assumptions.
- Decide what evidence will be needed to accept the system and who is accountable for that decision.
AI tools can help turn source material into draft requirements, user stories, work items, task decompositions, or threat-model prompts. Treat these as proposals: authorized stakeholders must confirm priorities, resolve ambiguity, and approve acceptance criteria.
2. Design the system and its data
Design decisions establish how the system is expected to work in its context and what data or model approach is appropriate. Record assumptions about data and inputs, and consider how the system will fit into surrounding software, user workflows, and operational processes. Plan evaluation for the intended deployment setting; a result on an abstract benchmark alone does not establish that a system is suitable for its actual use.
- Describe the intended system behavior and relevant boundaries.
- Document data and input characteristics that matter to the design.
- Choose evaluation methods and criteria tied to intended use and foreseeable risks.
- Consider human factors and how people will interpret, rely on, or challenge system outputs.
These design records give later development and evaluation work a traceable reference. If the context, data assumptions, or intended use changes, teams may need to revisit the design and its evaluation criteria.
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3. Develop and review changes
During development, teams may use AI assistants to propose code, configuration, documentation, tests, or security analysis. AI can accelerate drafting, but a plausible output is not evidence that a change is correct, secure, or appropriate for the project. Generated artifacts should follow the same established review and approval gates as other work.
- Keep the relevant source context and requirements available to reviewers, and trace proposed changes back to them.
- Review generated code and configuration for correctness, security, maintainability, and unintended behavior.
- Check security recommendations against the code and environment rather than accepting them solely because an assistant produced them.
- Record who reviewed and approved changes, including relevant AI-generated artifacts and their provenance.
NIST’s DevSecOps project describes a human-directed implementation using generative AI for requirements generation, task decomposition, ticketing, code generation and modification, infrastructure configuration, and test-case creation, alongside code scanning, vulnerability detection, and remediation summaries. Users reviewed and validated its generated outputs. This is an implementation example, not a measured productivity study or proof that every use case improves outcomes in every organization.
4. Build and test the software
Build work integrates code and dependencies into artifacts that can be evaluated and released. Testing should cover the behavior relevant to the system, including functionality, integration, security, and operational behavior. NIST’s DevSecOps reference model describes CI checks and scanning for code, dependencies, secrets, infrastructure as code, and container images before deployment.
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AI can help draft test cases or analyze results, but generating tests does not establish adequate coverage. Teams need to evaluate whether tests address the stated requirements, realistic inputs, important failure modes, and security concerns. A passing test suite is evidence about the cases it covers, not a guarantee that all defects or risks have been found.
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- Review whether generated tests exercise meaningful requirements and edge cases.
- Track findings through correction and retesting rather than treating a scan as a release decision by itself.
- Preserve traceability between requirements, changes, test evidence, and approvals.
5. Release and deploy with appropriate controls
Release only reviewed artifacts with the required approvals and evidence. Deployment is also where the system meets its actual use context, so AI systems need validation of system integration, user experience, and applicable requirements—not just model or code checks in isolation. A pilot or controlled rollout can be appropriate when the consequences of failure warrant a more cautious introduction.
Before release, confirm that the artifact being deployed is the one that passed review and testing, and that its origin and approvals can be traced. The degree of control should reflect the system’s potential impact and the autonomy it receives. In particular, an agent able to act across tools needs explicit authorization and oversight rather than broad access by default.
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6. Operate, monitor, and improve
Deployment begins operational learning; it does not end the lifecycle. Monitor application behavior, vulnerabilities, performance, incidents, and AI outputs or impacts relevant to intended use. NIST’s AI RMF lifecycle description includes periodic testing, incident tracking, and response during operation.
Use operational evidence to investigate problems, correct defects, update the system, and revisit planning when assumptions or conditions have changed. Monitoring should connect to a defined response: teams need to know who assesses an incident, who can authorize a change or intervention, and how findings feed into future evaluation and development.
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AI-generated code is a proposed change, not a separate class of change that can bypass normal engineering controls. Validation should establish that it meets requirements, behaves safely in context, and has passed accountable review. A practical release gate is:
- Check scope and traceability. Confirm the change answers an approved requirement and reviewers can see the source context behind it.
- Review the implementation. Examine logic, dependencies, configuration, and security implications; do not infer correctness from fluent explanations or comments.
- Run relevant tests and scans. Use the project’s build, functional, integration, and security checks, including applicable scans of code, dependencies, secrets, infrastructure configuration, and container images.
- Evaluate the evidence. Check test coverage and results against requirements and foreseeable failure modes. Review AI-generated tests themselves rather than treating their existence as proof of coverage.
- Require approval and preserve provenance. Have an accountable reviewer approve the artifact and retain the information needed to trace what was generated, changed, tested, and accepted.
- Monitor after deployment. Watch for defects, vulnerabilities, or unexpected behavior and route findings into incident response and corrective work.
No single passing scan or test suite proves generated code is safe in every environment. The release decision belongs to accountable people using evidence appropriate to the change and its potential impact.
Security and governance belong throughout the SDLC
NIST’s Secure Software Development Framework (SSDF), SP 800-218 version 1.1, supplies secure development practices that can be integrated into different lifecycle models; security is not a final checklist. NIST SP 800-218A, finalized in July 2024, extends SSDF 1.1 with AI-specific secure development practices for AI model producers, AI system producers, and acquirers.
Risks identified by NIST’s DevSecOps project include inaccurate outputs, insecure generated code, unauthorized actions, limited explainability, hallucinated security recommendations, data leakage, excessive agent privileges, context tampering, and AI-generated supply-chain artifacts without provenance or approval. Controls should address those risks where they arise:
- Limit tool and data permissions, especially for agents that can take actions.
- Validate outputs and security recommendations rather than accepting them automatically.
- Require accountable human approval at meaningful control gates.
- Preserve provenance, traceability, and relevant action logs.
- Monitor deployed behavior and maintain a response path for incidents.
Scale controls to the system’s impact and degree of autonomy. More consequential systems and agents with broader permissions call for stronger authorization, auditability, governance, and human oversight.
What to take away
Use the familiar software delivery lifecycle as the operational backbone, then make AI-specific design, data, evaluation, governance, and monitoring explicit. AI can assist with planning, implementation, testing, configuration, and security analysis, but the organization remains responsible for review, approval, and outcomes. The lifecycle is complete only when operational feedback can change the system or send the work back to planning.
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