People responsible for informed decisions
- AI product owners and Builders
- Governance, security, privacy, and assurance practitioners
- Operators responsible for AI incidents and change
- Reviewers responsible for release and lifecycle evidence
Batoi Learn Lab
Trustworthy AI SDLC Lab
Practise an evidence-driven AI lifecycle from use-case impact through evaluation, provenance, agent authorization, incidents, recourse, revalidation, readiness, and retirement.
Individual developers can begin in a free, non-production Developer workspace. Team assignments, teaching, and governance features depend on plan and role.
Bring a real, bounded context
- Choose a Platform-owned synthetic Assist, Embed, Operate, or combined reference application
- Identify the decision owner, affected people, intended outcome, and prohibited uses
- Do not enter production data, credentials, secrets, customer records, or private evidence
Learning Outcomes
Develop reasoning that can withstand review
The Lab combines method, guided practice, evidence, assessment, reflection, and an applicable output.
Classify intent and impact
Define purpose, mode, affected groups, consequences, ownership, risk, prohibited uses, and stop conditions.
Build versioned assurance evidence
Design data, policy, evaluation, threat, AI-BOM, SBOM, provenance, and agent-authorization records for exact versions.
Operate the lifecycle
Connect incidents, complaints, appeals, material and supplier change, rollback, emergency stop, revalidation, and retirement.
Make a bounded readiness decision
Preserve blockers, adverse evidence, limitations, remediation, monitoring, recourse, and recovery without claiming certification.
Guided Learning Path
Learn → Try → Assess → Reflect → Apply
Each stage strengthens the evidence and preserves the distinction between learning and operational authority.
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1
Learn
Review the full evidence-driven lifecycle and its decision boundaries.
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2
Try
Apply the method to one of four synthetic application patterns.
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3
Assess
Test adverse cases, missing authority, stale evidence, protected mutation, and recovery.
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4
Reflect
Identify the evidence that most changed the lifecycle decision.
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5
Apply
Prepare a Trustworthy AI SDLC evidence and readiness pack for authorized review.
Evidence produced through the Lab
A versioned evidence pack covering intent, impact, data, engineering policy, evaluation, threats, AI-BOM, SBOM, provenance, agent authority, incidents, recourse, revalidation, transparency, scorecards, readiness, and lifecycle decisions.
Transparent criteria support improvement
- Use-case and impact boundary
- Data quality and authority
- Evaluation and material-change control
- Threat, inventory, and provenance evidence
- Agent authorization, operations, and readiness decision
Feedback and reflection support retry and mastery; they do not replace destination review or approval.
Responsible Boundaries
Practice remains bounded and reviewable
A completed Lab demonstrates learning evidence. It does not grant production authority, certify compliance, accept risk, or bypass human decisions.
The Lab performs no provider call, production read, external write, deployment, or monitoring action.
Synthetic fixtures do not prove factual correctness, security, compliance, certification, or production readiness.
Any application requires current evidence and named accountable approval in the owning product workflow.
Frequently Asked Questions
About this Lab
No. It produces learning evidence and remediation guidance, not certification, compliance, or production approval.
No. It uses deterministic Platform-owned synthetic fixtures and does not connect production data, credentials, repositories, models, or tools.
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