ShieldCraft AI Implementation Checklist
🧱 Foundation & Planning
Lays the groundwork for a robust, secure, and business-aligned AI system. All key risks, requirements, and architecture are defined before data prep begins. Guiding Question: Before moving to Data Prep, ask: "Do we have clarity on what data is needed to solve the defined problem, and why?" Definition of Done: Business problem articulated, core architecture designed, and initial cost/risk assessments completed.
- 🟩 Finalize business case, value proposition, and unique differentiators
- 🟩 User profiles, pain points, value proposition, and ROI articulated
- 🟩 Define project scope, MVP features, and success metrics
- 🟩 Clear, business-aligned project objective documented
- 🟩 Data sources and expected outputs specified
- 🟩 Baseline infrastructure and cloud usage estimated
- 🟩 Address ethics, safety, and compliance requirements
- 🟩 Conduct initial bias audit
- 🟩 Draft hallucination mitigation strategy
- 🟩 Obtain legal review for data privacy plan
- 🟩 Document compliance requirements (GDPR, SOC2, etc.)
- 🟩 Schedule regular compliance reviews
- 🟩 Establish Security Architecture Review Board (see Security & Governance)
- 🟩 Technical, ethical, and operational risks identified with mitigation strategies
- 🟩 Threat modeling and adversarial testing (e.g., red teaming GenAI outputs)
- 🟩 Privacy impact assessments and regular compliance reviews (GDPR, SOC2, etc.)
- 🟩 Set up project structure, version control, and Docusaurus documentation
- 🟩 Modular system layers, MLOps flow, and security/data governance designed
- 🟩 Dockerfiles and Compose hardened for security, reproducibility, and best practices
- 🟩 Noxfile and developer workflow automation in place
- 🟩 Commit script unified, automating checks, versioning, and progress
- 🟩 Deliverables: business case summary, MLOps diagram, risk log, cost model, and ADRs
- 🟩 Production-grade AWS MLOps stack architecture implemented and tested (architecture & dependency map)
- 🟩 All major AWS stacks (networking, storage, compute, data, security, monitoring) provisioned via CDK
- 🟩 Pydantic config validation, advanced tagging, and parameterization enforced
- 🟩 Cross-stack resource sharing and dependency injection established
- 🟩 Security, compliance, and monitoring integrated (CloudWatch, SNS, Config, IAM boundaries)
- 🟩 S3 lifecycle, cost controls, and budget alarms implemented
- 🟥 819+ automated tests covering happy/unhappy paths, config validation, and outputs
- 🟩 Comprehensive documentation for stack interactions and outputs (see details)
MSK + Lambda Integration To-Do List
- 🟥 Ensure Lambda execution role has least-privilege Kafka permissions, scoped to MSK cluster ARN
- 🟥 Deploy Lambda in private subnets with correct security group(s)
- 🟥 Confirm security group allows Lambda-to-MSK broker connectivity (TLS port)
- 🟥 Set up CloudWatch alarms for Lambda errors, throttles, and duration
- 🟥 Set up CloudWatch alarms for MSK broker health, under-replicated partitions, and storage usage
- 🟥 Route alarm notifications to the correct email/SNS topic
- 🟥 Implement and test the end-to-end MSK + Lambda topic creation flow
- 🟥 Update documentation for MSK + Lambda integration, including troubleshooting steps
Data Preparation
Guiding Question: Do we have the right data, in the right format, with clear lineage and privacy controls? Definition of Done: Data pipelines are operational, data is clean and indexed for RAG. Link to data_prep/ for schemas and pipelines.
- 🟩 Identify and document all required data sources (logs, threat feeds, reports, configs)
- 🟩 Data ingestion, cleaning, normalization, privacy, and versioning
- 🟩 Build data ingestion pipelines
- 🟩 Set up Amazon MSK (Kafka) cluster with topic creation
- 🟥 Integrate Airbyte for connector-based data integration
- 🟥 Implement AWS Lambda for event-driven ingestion and pre-processing
- 🟥 Configure Amazon OpenSearch Ingestion for logs, metrics, and traces
- 🟥 Build AWS Glue jobs for batch ETL and normalization
- 🟥 Store raw and processed data in Amazon S3 data lake
- 🟥 Enforce governance and privacy with AWS Lake Formation
- 🟥 Add data quality checks (Great Expectations, Deequ)
- 🟩 Implement data cleaning, normalization, and structuring
- 🟩 Ensure data privacy (masking, anonymization) and compliance (GDPR, HIPAA, etc.)
- 🟩 Establish data versioning for reproducibility
- 🟩 Design and implement data retention policies
- 🟩 Implement and document data deletion/right-to-be-forgotten workflows (GDPR)
- 🟩 Modular data flows and schemas for different data sources
- 🟩 Data lineage and audit trails for all data flows and model decisions
- 🟩 Define and test disaster recovery, backup, and restore procedures for all critical data and services
- 🟥 Text chunking strategy defined and implemented for RAG
- 🟥 Experiment with various chunking sizes and overlaps (e.g., fixed, semantic, recursive)
- 🟥 Handle metadata preservation during chunking
- 🟥 Embedding model selection and experimentation for relevant data types
- 🟥 Evaluate different embedding models (e.g., Bedrock Titan, open-source options)
- 🟥 Establish benchmarking for embedding quality
- 🟩 Vector database (or
pgvector
) setup and population - 🟩 Select appropriate vector store (e.g., Pinecone, Weaviate, pgvector)
- 🟩 Implement ingestion pipeline for creating and storing embeddings
- 🟩 Optimize vector indexing for retrieval speed
- 🟩 Implement re-ranking mechanisms for retrieved documents (e.g., Cohere Rerank, cross-encoders)
AWS Cloud Foundation and Architecture
Guiding Question: Is the AWS environment production-grade, modular, secure, and cost-optimized for MLOps and GenAI workloads? Definition of Done: All core AWS infrastructure is provisioned as code, with cross-stack integration, config-driven deployment, and robust security/compliance controls. Architecture is modular, extensible, and supports rapid iteration and rollback.
- 🟩 Multi-account, multi-environment AWS Organization structure with strict separation of dev, staging, and prod, supporting least-privilege and blast radius reduction.
- Modular AWS CDK v2 stacks for all major AWS services:
- 🟩 Networking (VPC, subnets, security groups, vault secret import)
- 🟩 EventBridge (central event bus, rules, targets)
- 🟩 Step Functions (workflow orchestration, state machines, IAM roles)
- 🟩 S3 (object storage, vault secret import)
- 🟩 Lake Formation (data governance, fine-grained access control)
- 🟩 Glue (ETL, cataloging, analytics)
- 🟩 Lambda (event-driven compute, triggers)
- 🟩 Data Quality (automated validation, Great Expectations/Deequ)
- 🟩 Airbyte (connector-based ingestion, ECS services)
- 🟩 OpenSearch (search, analytics)
- 🟩 Cloud Native Hardening (CloudWatch alarms, Config rules, IAM boundaries)
- 🟩 Attack Simulation (automated security validation, Lambda, alarms)
- 🟩 Secrets Manager (centralized secrets, cross-stack exports)
- 🟩 MSK (Kafka streaming, broker info, roles)
- 🟩 SageMaker (model training, deployment, monitoring)
- 🟩 Budget (cost guardrails, alerts, notifications)
- 🟩 Advanced cross-stack resource sharing and dependency injection (CfnOutput/Fn.import_value), enabling secure, DRY, and scalable infrastructure composition.
- 🟩 Pydantic-driven config validation and parameterization, enforcing schema correctness and preventing misconfiguration at deploy time.
- 🟩 Automated tagging and metadata propagation across all resources for cost allocation, compliance, and auditability.
- 🟩 Hardened IAM roles, policies, and boundary enforcement, with automated least-privilege checks and centralized secrets management via AWS Secrets Manager.
- 🟩 AWS Vault integration for secure credential management and developer onboarding.
- 🟩 Automated S3 lifecycle policies, encryption, and access controls for all data lake buckets.
- 🟩 End-to-end cost controls and budget alarms, with CloudWatch and SNS integration for real-time alerting.
- 🟩 Cloud-native hardening stack (GuardDuty, Security Hub, Inspector) with automated findings aggregation and remediation hooks.
- 🟩 Automated integration tests for all critical AWS resources, covering both happy and unhappy paths, and validating cross-stack outputs.
- 🟩 Comprehensive documentation for stack interactions, outputs, and architectural decisions, supporting onboarding and audit requirements.
- 🟩 GitHub Actions CI/CD pipeline for automated build, test, and deployment of all infrastructure code.
- 🟩 Automated dependency management and patching via Poetry, ensuring reproducible builds and secure supply chain.
- 🟩 Modular, environment-parameterized deployment scripts and commit automation for rapid iteration and rollback.
- 🟩 Centralized error handling, smoke tests, and post-deployment validation for infrastructure reliability.
- 🟩 Secure, reproducible Dockerfiles and Compose files for local and cloud development, with best practices enforced.
- 🟩 Continuous compliance monitoring (Config, CloudWatch, custom rules) and regular security architecture reviews.
AI Core Development and Experimentation
Guiding Question: Are our models accurately solving the problem, and is the GenAI output reliable and safe? Definition of Done: Core AI models demonstrate accuracy, reliability, and safety according to defined metrics. Link to ai_core/ for model code and experiments.
- 🟩 Selected Mistral-7B as the primary Foundation Model for ShieldCraft AI
- 🟥 Select secondary Foundation Models (FMs) from Amazon Bedrock or Hugging Face (Phase 2 - multi-agent orchestration)
- 🟥 Define core AI strategy (RAG, fine-tuning, hybrid approach)
- 🟥 LangChain integration for orchestration and prompt management
- 🟥 Prompt Engineering lifecycle implemented:
- 🟥 Prompt versioning and prompt registry
- 🟥 Prompt approval workflow
- 🟥 Prompt experimentation framework
- 🟥 Integration of human-in-the-loop (HITL) for continuous prompt refinement
- 🟥 Guardrails and safety mechanisms for GenAI outputs:
- 🟥 Establish Responsible AI governance: bias monitoring, model risk management, and audit trails
- 🟥 Implement content moderation APIs/filters
- 🟥 Define toxicity thresholds and response strategies
- 🟥 Establish mechanisms for red-teaming GenAI outputs (e.g., adversarial prompt generation and testing)
- 🟥 RAG pipeline prototyping and optimization:
- 🟥 Implement efficient retrieval from vector store
- 🟥 Context window management for LLMs
- 🟥 LLM output parsing and validation (e.g., Pydantic for structured output)
- 🟥 Address bias, fairness, and transparency in model outputs
- 🟥 Implement explainability for key AI decisions where possible
- 🟥 Automated prompt evaluation metrics and frameworks
- 🟩 Model loading, inference, and resource optimization
- 🟥 Experiment tracking and versioning (MLflow/SageMaker Experiments)
- 🟥 Model registry and rollback capabilities (SageMaker Model Registry)
- 🟥 Establish baseline metrics for model performance
- 🟥 Cost tracking and optimization for LLM inference (per token, per query)
- 🟥 LLM-specific evaluation metrics:
- 🟥 Hallucination rate (quantified)
- 🟥 Factuality score
- 🟥 Coherence and fluency metrics
- 🟥 Response latency per token
- 🟥 Relevance to query
- 🟥 Model and Prompt card generation for documentation
- 🟥 Implement canary and shadow testing for new models/prompts
Application Layer and Integration
Guiding Question: Is the AI accessible, robust, and seamlessly integrated with existing systems? Definition of Done: API functional, integrated with UI, and handles errors gracefully. Link to application for API code and documentation.
- 🟥 Define Core API endpoints for AI services
- 🟥 Build production-ready, scalable API (FastAPI, Flask, etc.)
- 🟥 Input/output validation and data serialization
- 🟥 User Interface (UI) integration for analyst dashboard
- 🟥 Implement LangChain Chains and Agents for complex workflows
- 🟥 LangChain Memory components for conversational context
- 🟥 Robust error handling and graceful fallbacks for API and LLM responses
- 🟥 API resilience and rate limiting mechanisms
- 🟥 Implement API abuse prevention (WAF, throttling, DDoS protection)
- 🟥 Secure prompt handling and sensitive data redaction at the application layer
- 🟥 Develop example clients/SDKs for API consumption
- 🟥 Implement API Gateway (AWS API Gateway) for secure access
- 🟥 Automated API documentation generation (e.g., OpenAPI/Swagger)
Evaluation and Continuous Improvement
Guiding Question: How do we continuously measure, learn, and improve the AI's effectiveness and reliability? Definition of Done: Evaluation framework established, feedback loops active, and continuous improvement process in place. Link to evaluation for metrics and dashboards.
- 🟥 Automated evaluation metrics and dashboards (e.g., RAG evaluation tools for retrieval relevance, faithfulness, answer correctness)
- 🟥 Human-in-the-loop (HITL) feedback mechanisms for all GenAI outputs
- 🟥 Implement user feedback loop for feature requests and issues
- 🟥 LLM-specific monitoring: toxicity drift, hallucination rates, contextual relevance
- 🟥 Real-time alerting for performance degradation or anomalies
- 🟥 A/B testing framework for prompts, models, and RAG configurations
- 🟥 Usage analytics and adoption tracking
- 🟥 Continuous benchmarking and optimization for performance and cost
- 🟥 Iterative prompt, model, and data retrieval refinement processes
- 🟥 Regular stakeholder feedback sessions and roadmap alignment
MLOps, Deployment and Monitoring
Guiding Question: Is the system reliable, scalable, secure, and observable in production? Definition of Done: CI/CD fully automated, system stable in production, and monitoring active. Link to mlops/ for pipeline definitions.
- 🟩 Infrastructure as Code (IaC) with AWS CDK for all cloud resources
- 🟩 CI/CD pipelines (GitHub Actions) for automated build, test, and deployment
- 🟩 Containerization (Docker)
- 🟥 Orchestration (Kubernetes/AWS EKS)
- 🟩 Pre-commit and pre-push hooks for code quality checks
- 🟩 Automated dependency and vulnerability patching
- 🟥 Secrets scanning in repositories and CI/CD pipelines
- 🟥 Build artifact signing and verification
- 🟥 Secure build environment (e.g., ephemeral runners)
- 🟥 Deployment approval gates and manual review processes
- 🟥 Automated rollback and canary deployment strategies
- 🟥 Post-deployment validation checks (smoke tests, integration tests)
- 🟥 Continuous monitoring for cost, performance, data/concept drift
- 🟥 Implement cloud cost monitoring, alerting, and FinOps best practices (AWS Cost Explorer, budgets, tagging, reporting)
- 🟥 Secure authentication, authorization, and configuration management
- 🟩 Secrets management (AWS Secrets Vault)
- 🟥 IAM roles and fine-grained access control
- 🟥 Schedule regular IAM access reviews and user lifecycle management
- 🟩 Multi-environment support (dev, staging, prod)
- 🟩 Automated artifact management (models, data, embeddings)
- 🟩 Robust error handling in automation scripts
- 🟥 Automated smoke and integration tests, triggered after build/deploy
- 🟥 Static type checks enforced in CI/CD using Mypy
- 🟥 Code coverage tracked and reported via Pytest-cov
- 🟥 Automated Jupyter notebook dependency management and validation (via Nox and Nbval)
- 🟥 Automated SageMaker training jobs launched via Nox and parameterized config
- 🟩 Streamlined local development (Nox, Docker Compose)
- 🟥 Command Line Interface (CLI) tools for common operations
- 🟥 Automate SBOM generation and review third-party dependencies for supply chain risk
- 🟥 Define release management and versioning policies for all major components
Security and Governance (Overarching)
Guiding Question: Are we proactively managing risk, compliance, and security at every layer and continuously? Definition of Done: Comprehensive security posture established, audited, and monitored across all layers. Link to security/ for policies and audit reports.
- 🟥 Establish Security Architecture Review Board (if not already in place)
- 🟥 Conduct regular Security Audits (internal and external)
- 🟥 Implement Continuous compliance monitoring (GDPR, SOC2, etc.)
- 🟥 Develop a Security Incident Response Plan and corresponding runbooks
- 🟥 Implement Centralized audit logging and access reviews
- 🟥 Develop SRE runbooks, on-call rotation, and incident management for production support
- 🟥 Document and enforce Security Policies and Procedures
- 🟥 Proactive identification and mitigation of Technical, Ethical, and Operational risks
- 🟥 Leverage AWS security services (Security Hub, GuardDuty, Config) for enterprise posture
- 🟥 Ensure data lineage and audit trails are established and maintained for all data flows and model decisions
- 🟥 Implement Automated security scanning for code, containers, and dependencies (SAST, DAST, SBOM)
- 🟥 Secure authentication, authorization, and secrets management across all services
- 🟥 Define and enforce IAM roles and fine-grained access controls
- 🟥 Regularly monitor for Infrastructure drift and automated remediation for security configurations
Documentation and Enablement
Guiding Question: Is documentation clear, actionable, and up-to-date for all stakeholders? Definition of Done: All docs up-to-date, onboarding tested, and diagrams published. Link to docs-site/ for rendered docs.
- 🟩 Maintain up-to-date Docusaurus documentation for all major components
- 🟩 Automated checklist progress bar update
- 🟥 Architecture diagrams and sequence diagrams for all major flows
- 🟥 Document onboarding, architecture, and usage for developers and analysts
- 🟩 Add “How to contribute” and “Getting started” guides
- 🟥 Automated onboarding scripts (e.g., one-liner to set up local/dev environment)
- 🟥 Pre-built Jupyter notebook templates for common workflows
- 🟥 End-to-end usage walkthroughs (from data ingestion to GenAI output)
- 🟥 Troubleshooting and FAQ section
- 🟥 Regularly update changelog and roadmap
- 🟥 Set up customer support/feedback channels and integrate feedback into roadmap
- 🟥 Changelog automation and release notes
- 🟥 Automated notebook dependency management and validation
- 🟥 Automated notebook validation in CI/CD
- 🟥 Code quality and consistent style enforced (Ruff, Poetry)
- 🟥 Contribution guidelines for prompt engineering and model adapters
- 🟥 All automation and deployment workflows parameterized for environments
- 🟥 Test coverage thresholds and enforcement
- 🟥 End-to-end tests simulating real analyst workflows
- 🟥 Fuzz testing for API and prompt inputs