Software Development Lifecycle
The end-to-end process (requirements, design, build, test, deploy, monitor) for AI applications, incorporating governance and compliance checks at each stage.
Definition
A tailored SDLC for AI that adds governance checkpoints—impact assessments at requirements, security-by-design and privacy-by-design gates in design, QA and validation tests in build, policy enforcement in deployment, and ongoing monitoring post-launch. It ensures traceability of decisions, adherence to best practices, and alignment with regulatory frameworks throughout. Documentation at each stage supports audits and continuous improvement.
Real-World Example
A financial-services firm extended its SDLC by adding: (1) a Data Privacy PIA at requirements; (2) a bias-mitigation design review; (3) automated security scans in CI; (4) policy-enforcement middleware at deployment; and (5) performance and compliance dashboards in production—ensuring every AI feature passes rigorous governance checks.
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