ai-deployment-checklist

name	ai-deployment-checklist
description	Pre-deployment privacy compliance checklist for AI/ML systems covering DPIA completion, lawful basis verification, transparency notices, human oversight mechanisms, bias testing, and post-deployment monitoring setup. Keywords: AI deployment, privacy checklist, go-live, model deployment, compliance gate.
license	Apache-2.0
metadata	{"author":"mukul975","version":"1.0","domain":"privacy","subdomain":"ai-privacy-governance","tags":"ai-deployment, privacy-checklist, compliance-gate, model-release, pre-deployment"}

AI System Pre-Deployment Privacy Checklist

Overview

Deploying an AI system that processes personal data requires verification of privacy compliance across multiple dimensions before the system goes live. This checklist serves as a compliance gate in the Cerebrum AI Labs ML deployment pipeline. No AI system may be deployed to production until all mandatory items are verified and signed off by the Data Protection Officer (DPO). The checklist is structured around GDPR requirements, the EU AI Act obligations (for high-risk systems), and internal governance standards.

Pre-Deployment Compliance Gate

Gate 1: Legal Basis and DPIA

Check	Requirement	Status	Evidence
Lawful basis documented	Art. 6(1) basis identified and recorded for all personal data processing	Required	LIA or consent records
Special categories assessed	Art. 9 data identified; explicit consent or Art. 9(2) exception documented	Required	Data classification report
DPIA completed	Art. 35 DPIA completed for high-risk processing (profiling, systematic monitoring, large-scale special categories)	Required if applicable	DPIA document signed by DPO
DPIA risks mitigated	All high/critical risks from DPIA have documented mitigations	Required	Risk treatment plan
Prior consultation	Art. 36 consultation with supervisory authority if residual risk remains high	Required if applicable	Consultation record
Legitimate interest assessment	If relying on Art. 6(1)(f), LIA balancing test completed	Required if LI basis	LIA document

Gate 2: Transparency and Information

Check	Requirement	Status	Evidence
Privacy notice updated	Art. 13-14 information includes AI processing details	Required	Updated privacy notice
Logic described	"Meaningful information about the logic involved" documented for data subjects	Required for automated decisions	Explanation document
Significance disclosed	Envisaged consequences of AI processing disclosed	Required for automated decisions	Privacy notice section
Profiling disclosed	If system profiles individuals, this is disclosed in privacy notice	Required if profiling	Privacy notice section
AI Act transparency	Art. 52 transparency obligations met (if applicable): inform that they are interacting with AI	Required for AI Act	User interface disclosure

Gate 3: Data Subject Rights

Check	Requirement	Status	Evidence
Access process defined	Process for responding to access requests for AI data (inputs, outputs, profiles)	Required	Documented SOP
Explanation mechanism	Individual explanations can be generated on request	Required for Art. 22	Technical capability verified
Human intervention available	Art. 22(3) human review process established	Required for solely automated decisions	Process document + trained staff
Contestation channel	Data subjects can contest AI decisions and have them reviewed	Required for Art. 22	Appeal process document
Rectification process	Process for correcting AI input data and regenerating outputs	Required	Documented SOP
Erasure process	Process for deleting data from training sets, inference logs, embeddings	Required	Documented SOP

Gate 4: Data Quality and Bias

Check	Requirement	Status	Evidence
Training data documented	Data sources, size, collection method, preprocessing documented	Required	Data card / dataset documentation
Bias testing completed	Model tested for bias across protected attributes (gender, race, age, disability)	Required	Bias test report
Fairness metrics acceptable	Disparate impact ratio >0.8 (four-fifths rule) or equivalent metric within acceptable range	Required	Fairness metrics report
Data quality verified	Training data completeness, accuracy, representativeness verified	Required	Data quality report
Art. 9 data removed or justified	Special category data either removed or lawful basis documented	Required	Data classification report

Gate 5: Security and Technical Controls

Check	Requirement	Status	Evidence
Data encryption at rest	Training data and model weights encrypted (AES-256 or equivalent)	Required	Security configuration
Data encryption in transit	All API endpoints use TLS 1.2+	Required	SSL certificate
Access controls	Role-based access to model, training data, and inference logs	Required	IAM policy
Audit logging	All model invocations logged with timestamp, input hash, output, user	Required	Logging configuration
Adversarial robustness	Model tested against common adversarial attacks relevant to its domain	Recommended	Security test report
Model versioning	Model versioned in registry with rollback capability	Required	MLflow / model registry

Gate 6: Monitoring and Governance

Check	Requirement	Status	Evidence
Performance monitoring	Dashboard tracking accuracy, latency, error rates in production	Required	Monitoring setup
Drift detection	Data drift and concept drift detection implemented	Required	Drift monitoring configuration
Bias monitoring	Post-deployment bias metrics tracked continuously	Required	Fairness monitoring dashboard
Incident response	Process for handling AI-related privacy incidents (e.g., discriminatory output, data leak)	Required	Incident response plan
Retraining schedule	Defined schedule for model retraining with fresh data	Required	Retraining plan
Retention enforcement	Automated deletion of inference logs and training data per retention schedule	Required	Retention policy + automation

Gate 7: EU AI Act (High-Risk Systems Only)

Check	Requirement	Status	Evidence
Risk classification	System classified per Annex III	Required	Classification document
Technical documentation	Annex IV documentation complete	Required	Tech doc package
Risk management system	Art. 9 continuous risk management implemented	Required	Risk register + process
Conformity assessment	Internal or third-party conformity assessment completed	Required	Assessment report
EU Declaration of Conformity	Art. 47 declaration prepared	Required	Signed declaration
EU database registration	Art. 49 registration completed	Required	Registration confirmation

Sign-Off

Role	Name	Approval	Date
ML Engineering Lead		[ ] Approved / [ ] Blocked
Data Protection Officer		[ ] Approved / [ ] Blocked
Information Security Officer		[ ] Approved / [ ] Blocked
Product Owner		[ ] Approved / [ ] Blocked
Legal Counsel		[ ] Approved / [ ] Blocked (high-risk only)

Key Legal References

• GDPR Articles 5, 6, 9 — Data processing principles, lawful basis, special categories
• GDPR Article 22 — Automated individual decision-making safeguards
• GDPR Article 25 — Data protection by design and by default
• GDPR Article 35 — Data Protection Impact Assessment
• EU AI Act Articles 9-15 — High-risk AI system requirements
• EU AI Act Article 52 — Transparency obligations for certain AI systems
• EDPB Guidelines on Automated Decision-Making (WP 251 rev.01) — Art. 22 interpretation
• ISO/IEC 42001:2023 — AI management system standard