An AI-native SDLC with SOC 2 controls
An AI-native software development lifecycle, with AI working across every stage and SOC 2 controls governing how the platform is built and operated.
- AI · Define
Business needs, system context and acceptance criteria.
- AI · Design
Architecture options, dependencies and security decisions.
- AI · Build
Scoped implementation, coding agents and code review.
- AI · Test
Tests, security checks and model evaluations.
- AI · Release
Change risk, release evidence and controlled approval.
- AI · Operate
Monitoring, incident triage and feedback into the next brief.
AI at every stage · SOC 2 controls throughout
What the business was trying to change
The work was to establish how a financial services platform is built and run with AI throughout its software development lifecycle. Requirements, architecture and engineering sit in the same loop as testing, release and day-to-day operations.
That delivery model has to meet SOC 2 control requirements as it runs. Agent permissions, engineering review and the evidence behind a change belong inside the workflow, alongside the AI doing the work.
What needed doing
Establish an AI-native SDLC that uses AI across every stage, maintains SOC 2 control compliance and supports the demands of running AI at scale.
How AI works across the lifecycle
AI in requirements and discovery
AI turns business needs, user feedback and existing system context into draft requirements and acceptance criteria. The team reviews the assumptions and agrees what the work needs to achieve before implementation starts.
AI in architecture and design
AI examines design options and dependencies, including the implications for data access and security. Architecture decisions remain explicit, with an engineer accountable for the design that is taken forward.
AI in implementation
Coding agents implement scoped changes within defined permissions. AI reviews the code, while accountable engineers approve changes through the same controlled development process.
AI in testing and assurance
AI generates tests, analyses regressions and evaluates model and agent behaviour. Software tests, security checks and human review work together to assess a change before it reaches production.
AI in release and change control
AI assembles release summaries, checks evidence and flags change risk. Approval, access control and traceable releases maintain SOC 2 controls as the team ships. The release process includes a way to roll back a failed change.
AI in operations and improvement
AI analyses monitoring data, triages incidents and examines reliability, latency and usage cost. Operating feedback informs the next requirements and design decisions, so the lifecycle continues as the platform grows.
AI throughout delivery. SOC 2 controls throughout the work.
The delivery model combines AI across the full SDLC with SOC 2 control compliance. Defined access, review responsibilities and change records remain part of everyday engineering. Training and operating guidance help the team maintain that model as AI usage and the platform grow.
- AI embedded across all six stages of the software lifecycle.
- SOC 2 controls governing development, release and operations.
- Human accountability for design decisions and production changes.
- Operating feedback, service reliability and AI usage cost feeding the next change.
AI-native delivery depends on how the whole engineering system works. AI can contribute at every stage while the team maintains control of access, decisions and the evidence behind each release.