Shoaibali Mir

Posted on Dec 25, 2025 • Edited on Dec 26, 2025

Building Secure AI/ML Pipelines on AWS: The AIDLC + DevSecOps Approach

#ai #aws #devops

Reading time: ~12-15 minutes

Level: Intermediate

Series: Part 1 of 4 - The AIDLC DevSecOps Approach

What you'll learn: How to architect secure, production-ready ML pipelines on AWS using the AIDLC framework with practical security implementation

The Problem

You've built an amazing ML model in a Jupyter notebook. Now what?

Deploying ML models to production isn't just about hosting an API. You need to handle:

Data privacy - Training data often contains sensitive information
Model drift - Performance degrades over time
Security - New attack vectors like model poisoning and adversarial attacks
Compliance - Audit trails and governance requirements
Reproducibility - "Works on my machine" is catastrophic with ML
Data lineage - Tracking data from source to predictions

This is where the AI Development Life Cycle (AIDLC) framework meets DevSecOps principles.

What is AIDLC?

AIDLC is a structured approach to managing the complete lifecycle of ML systems in production. Unlike traditional software development, ML systems require managing three critical assets: code, data, and models.

The AIDLC Framework

┌─────────────────────────────────────────────────────────┐
│                    AIDLC LIFECYCLE                      │
├─────────────────────────────────────────────────────────┤
│  Architecture -> Infrastructure -> Deployment ->        │
│  Learning -> Compliance                                 │
└─────────────────────────────────────────────────────────┘

Architecture Phase

Design secure ML system architecture
Define data flows and security boundaries
Select AWS services and integration patterns
Plan for scalability and compliance

Infrastructure Phase

Build encrypted data storage (S3 + KMS)
Create secure compute environments
Implement IAM least privilege policies
Set up monitoring and logging foundations

Deployment Phase

Automate ML training pipelines
Deploy models with version control
Implement CI/CD for ML workflows
Enable safe production rollouts

Learning Phase

Monitor model performance continuously
Detect and alert on data drift
Track business metrics and KPIs
Trigger automated retraining

Compliance Phase

Maintain comprehensive audit trails
Implement data governance policies
Generate compliance reports
Ensure regulatory requirements met

AIDLC vs Traditional DevSecOps

ML systems are fundamentally different from traditional software:

Traditional DevSecOps	AIDLC for ML	Why It Matters
Deploy code	Deploy code + data + models	3x the attack surface
Code versioning	Code + data + model versioning	Reproducibility requires all three
Monitor uptime	Monitor drift + performance + data quality	Models degrade silently over time
Unit tests	Unit + data validation + model tests	Bad data = bad predictions
Access control	Data lineage + model provenance	Compliance requires full tracking
Push new code	Retrain + redeploy + A/B test	Can't just push updates
Deploy once	Continuous retraining cycle	Models need regular updates

The core challenge: ML systems have data as a first-class citizen alongside code, creating unique security, compliance, and operational requirements that traditional DevOps doesn't address.

Why AIDLC + DevSecOps?

Security Throughout the Lifecycle

Architecture Phase Security:

Threat modeling for ML-specific attacks
Data flow diagrams with security boundaries
Compliance requirements identification

Infrastructure Phase Security:

Encryption at rest (S3, EBS, RDS)
Encryption in transit (TLS 1.2+)
Network isolation patterns
IAM least privilege roles

Deployment Phase Security:

Secure CI/CD pipelines
Container vulnerability scanning
Model artifact signing
Secrets management

Learning Phase Security:

Anomaly detection in predictions
Model behavior monitoring
Input validation on inference
Attack detection (adversarial inputs)

Compliance Phase Security:

Audit logging (CloudTrail)
Compliance reporting automation
Data retention policies
Incident response procedures

AWS Reference Architecture

Here's a high-level view of a secure ML pipeline following AIDLC principles:

Architecture mapped to AIDLC phases:

Phase 1: Architecture (Design)

VPC design with security groups
Data flow architecture
Service integration patterns
Disaster recovery planning

Phase 2: Infrastructure (Build)

Data Layer

Amazon S3 (encrypted storage)
AWS Glue (ETL and data catalog)
AWS Lake Formation (data governance)

ML Training

Amazon SageMaker (managed training)
SageMaker Model Registry (versioning)
Amazon ECR (container registry)

Security

AWS KMS (encryption keys)
AWS IAM (access control)
AWS Secrets Manager (credential storage)
AWS CloudTrail (audit logging)

Phase 3: Deployment (Operate)

Inference

Amazon ECS/AWS Lambda (serving)
AWS API Gateway (API management)
Application Load Balancer (routing)

CI/CD

AWS CodePipeline (orchestration)
AWS CodeBuild (build automation)
AWS CodeDeploy (deployment)

Phase 4: Learning (Monitor)

Amazon CloudWatch (metrics and logs)
SageMaker Model Monitor (drift detection)
AWS X-Ray (distributed tracing)
CloudWatch Dashboards (visualization)

Phase 5: Compliance (Govern)

AWS Config (compliance rules)
AWS Security Hub (security posture)
GuardDuty (threat detection)
Automated compliance reporting

Learning vs Production: Setting Expectations

Important: What This Series Covers

This 4-part series teaches AIDLC implementation patterns on AWS with security best practices. We focus on hands-on learning that you can progressively harden for production.

What We Build (AIDLC Learning Implementation)

Phases Fully Implemented:

Architecture Phase

Complete system design
Security architecture patterns
AWS service selection
Data flow diagrams

Infrastructure Phase

S3 encrypted storage with KMS
IAM roles with least privilege
Lambda data validation
CloudWatch monitoring setup
Infrastructure as Code (Terraform)

Deployment Phase

SageMaker training jobs
Model registry and versioning
Automated CI/CD pipeline
Deployment automation

Learning Phase

Model performance tracking
Basic drift detection
CloudWatch dashboards
Alerting on failures

Compliance Phase (Basic)

CloudTrail audit logging
Encrypted data storage
Access control policies
Basic compliance reporting

Simplified for Learning

Network Security

Public subnets with security groups (not full VPC isolation)
Development-grade security (not enterprise hardening)

Monitoring

Basic CloudWatch (not full observability stack)
Essential metrics (not comprehensive instrumentation)

Scale

Development compute (not auto-scaling production)
Single region (not multi-region DR)

Production Hardening (Beyond This Series)

To graduate from learning to production, you'll need:

Network Hardening (AWS Security Workshops)

VPC endpoints for S3, SageMaker, ECR
Private subnets for all compute
AWS WAF for API protection
Network segmentation and isolation

Advanced Security (Security Team Required)

AWS Organizations with SCPs
GuardDuty threat detection
Security Hub dashboards
Secrets Manager rotation
IAM Access Analyzer

Enterprise Operations (DevOps Maturity)

Multi-region disaster recovery
Advanced auto-scaling
Blue/green deployments with traffic shifting
Chaos engineering
Enterprise backup strategies

Compliance (Industry-Specific)

HIPAA (healthcare)
PCI DSS (payment data)
GDPR (EU personal data)
SOC 2 Type II
Industry certifications

Why This Approach?

Progressive hardening lets you:

Master AIDLC patterns without overwhelming complexity
Build working systems you can test immediately
Add production features as requirements mature
Avoid over-engineering for uncertain use cases

When production-ready, reference:

AIDLC Core Principles

1. Security by Design (Architecture Phase)

Every AIDLC phase integrates security from the start:

Defense in Depth:

┌─────────────────────────────────────────┐
│ Network Layer (VPC, Security Groups)    │
├─────────────────────────────────────────┤
│ Identity Layer (IAM, Least Privilege)   │
├─────────────────────────────────────────┤
│ Encryption Layer (KMS, TLS)             │
├─────────────────────────────────────────┤
│ Validation Layer (Schema, Quality)      │
├─────────────────────────────────────────┤
│ Monitoring Layer (CloudWatch, Alerts)   │
└─────────────────────────────────────────┘

Three Assets to Protect:

Code: Version control, code review, dependency scanning
Data: Encryption, access logging, lineage tracking
Models: Registry versioning, artifact encryption, input validation

2. Infrastructure as Code (Infrastructure Phase)

All infrastructure versioned and repeatable:

# terraform/main.tf
module "aidlc_pipeline" {
  source = "./modules/aidlc"

  project_name = "secure-ml"
  environment  = "production"

  enable_encryption = true
  enable_logging    = true
  enable_monitoring = true
}

3. Encryption Everywhere (Infrastructure Phase)

At Rest:

# S3 bucket with KMS encryption
resource "aws_s3_bucket_server_side_encryption_configuration" "training_data" {
  bucket = aws_s3_bucket.training_data.id

  rule {
    apply_server_side_encryption_by_default {
      sse_algorithm     = "aws:kms"
      kms_master_key_id = aws_kms_key.data_encryption.arn
    }
  }
}

In Transit:

# Enforce HTTPS only
resource "aws_s3_bucket_policy" "training_data" {
  bucket = aws_s3_bucket.training_data.id

  policy = jsonencode({
    Statement = [{
      Effect = "Deny"
      Principal = "*"
      Action = "s3:*"
      Resource = "${aws_s3_bucket.training_data.arn}/*"
      Condition = {
        Bool = {
          "aws:SecureTransport" = "false"
        }
      }
    }]
  })
}

4. Least Privilege (All Phases)

Each component gets minimum necessary permissions:

┌──────────────────┬──────────────────────────────────┐
│ AIDLC Component  │ AWS Permissions                  │
├──────────────────┼──────────────────────────────────┤
│ Data Validation  │ Read raw S3, Write validated S3  │
│ Training Job     │ Read training data, Write models │
│ Inference API    │ Read models, Write predictions   │
│ Monitoring       │ Read CloudWatch, Send alerts     │
│ CI/CD Pipeline   │ Deploy resources, Update code    │
└──────────────────┴──────────────────────────────────┘

Example IAM Policy (Training - Infrastructure Phase):

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["s3:GetObject", "s3:ListBucket"],
      "Resource": [
        "arn:aws:s3:::ml-training-data/*",
        "arn:aws:s3:::ml-training-data"
      ]
    },
    {
      "Effect": "Allow",
      "Action": ["s3:PutObject"],
      "Resource": ["arn:aws:s3:::ml-models/*"]
    },
    {
      "Effect": "Allow",
      "Action": ["kms:Decrypt", "kms:GenerateDataKey"],
      "Resource": ["arn:aws:kms:region:account:key/key-id"]
    }
  ]
}

5. Comprehensive Monitoring (Learning Phase)

Four Monitoring Layers:

Infrastructure Metrics

CPU, memory, disk utilization
Network throughput and errors
Instance health checks

Application Metrics

Request latency (p50, p95, p99)
Throughput (requests/sec)
Error rates by type

ML-Specific Metrics

Prediction accuracy over time
Model confidence distributions
Feature drift detection
Inference latency

Business Metrics

Prediction impact on KPIs
Cost per prediction
Model ROI
A/B test results

6. Data Quality Gates (Infrastructure Phase)

Schema Validation:

# Enforce schema before training
expected_schema = {
    'timestamp': 'datetime64',
    'feature_1': 'float64',
    'feature_2': 'int64',
    'target': 'int64'
}

if not validate_schema(data, expected_schema):
    raise ValueError("Schema validation failed")

Statistical Validation:

# Detect data drift
drift_score = calculate_drift(new_data, reference_data)
if drift_score > 0.1:
    alert("Data drift detected", severity="WARNING")

# Detect anomalies
anomalies = detect_anomalies(new_data)
if anomalies.any():
    alert("Anomalies detected", severity="CRITICAL")

AIDLC Security Checklist

Architecture Phase

Design Security

Threat model completed for ML system
Security boundaries defined
Compliance requirements documented
Data classification scheme defined
Incident response plan outlined

Infrastructure Phase

Data Security

S3 buckets with SSE-KMS encryption
S3 bucket policies deny unencrypted uploads
S3 versioning enabled for audit trail
S3 Block Public Access enabled
S3 access logging configured

Compute Security

IAM roles (no hard-coded credentials)
Security groups with minimal rules
Container vulnerability scanning
Encrypted EBS volumes
IMDSv2 required on EC2

Network Security (Learning)

HTTPS/TLS 1.2+ enforced
API Gateway throttling enabled
CloudFront for DDoS protection
Security group ingress restricted

Deployment Phase

CI/CD Security

Secrets in Secrets Manager (not code)
Code review required for merges
Automated security scanning in pipeline
Deployment requires approval
Rollback procedures documented

Model Security

Model artifacts encrypted in S3
Model registry with versioning
Model checksums verified
Input validation on inference APIs
Rate limiting on endpoints

Learning Phase

Monitoring Security

CloudWatch Logs encrypted
CloudWatch alarms for anomalies
Drift detection enabled
Performance degradation alerts
Prediction distribution monitored

Compliance Phase

Audit & Governance

CloudTrail enabled in all regions
AWS Config recording changes
Compliance rules automated (Config)
Log retention policies defined
Centralized log aggregation
Regular access reviews scheduled

Compliance Controls Mapping

How AIDLC phases address regulatory requirements:

Compliance Requirement	AIDLC Phase	AWS Implementation	Evidence
Data Encryption at Rest	Infrastructure	S3 SSE-KMS, EBS encryption	CloudTrail logs, Config rules
Data Encryption in Transit	Infrastructure	TLS 1.2+ enforcement	Bucket policies, ALB config
Access Auditing	Compliance	CloudTrail + CloudWatch	Audit logs, SIEM feeds
Data Lineage	Infrastructure	S3 metadata + tags	S3 inventory, metadata reports
Model Versioning	Deployment	SageMaker Model Registry	Registry API logs
Change Management	Deployment	Git + CodePipeline	Git history, pipeline logs
Least Privilege Access	All Phases	IAM policies + SCPs	Access Analyzer reports
Data Retention	Compliance	S3 Lifecycle policies	Compliance dashboards
Backup & Recovery	Infrastructure	S3 versioning, replication	Replication status
Incident Response	Learning	CloudWatch Alarms, SNS	Alarm history, runbooks
Model Monitoring	Learning	SageMaker Model Monitor	Drift reports, performance logs

Frameworks Supported:

GDPR: Data encryption, access controls, audit trails, deletion capabilities
HIPAA: Encryption, access logging, BAA compliance with AWS
SOC 2: Security monitoring, change management, access reviews
PCI DSS: Network isolation, encryption, logging, access controls

Tentative 8-Week AIDLC Implementation Roadmap

Weeks 1-2: Architecture & Infrastructure Foundation

Week 1: Architecture Phase

Design complete ML system architecture
Document data flows and security boundaries
Select AWS services for each AIDLC phase
Create threat model for ML system
Define compliance requirements
Establish cost budget and monitoring

Week 2: Infrastructure Phase - Foundation

Create AWS account structure
Enable CloudTrail in all regions
Configure AWS Config
Create KMS encryption keys
Set up S3 buckets (raw, validated, models)
Configure bucket policies and encryption
Initialize Terraform state management
Set up CloudWatch log groups
Create SNS topics for alerts

Deliverables:

System architecture diagram
Threat model document
Secure AWS foundation
Encrypted storage infrastructure

Weeks 3-4: Infrastructure Phase - Data Pipeline

Week 3: Data Ingestion

Build Lambda data validation function
Configure S3 event triggers
Implement schema validation
Add data quality checks
Set up CloudWatch metrics
Configure failure notifications
Create IAM roles with least privilege

Week 4: Data Processing

Implement data preprocessing logic
Create feature engineering pipelines
Add data versioning
Build data quality test suite
Implement duplicate detection
Document data lineage
Set up data drift detection baseline

Deliverables:

Automated data validation pipeline
Quality-gated data storage
Data lineage documentation

Weeks 5-6: Deployment Phase - ML Training

Week 5: Training Infrastructure

Create SageMaker execution roles
Build custom training containers
Configure training job parameters
Set up Spot instance training
Implement experiment tracking
Add training failure alerts
Create training metrics dashboard

Week 6: Model Management & Registry

Configure SageMaker Model Registry
Implement model versioning workflow
Create model approval process
Build hyperparameter tuning jobs
Document model lineage
Set up model performance baselines
Create model deployment templates

Deliverables:

Scalable training infrastructure
Model registry with governance
Experiment tracking system

Weeks 7-8: Deployment & Learning Phases

Week 7: Deployment Phase - Production Pipeline

Create inference endpoints
Build CI/CD pipeline (CodePipeline)
Implement deployment automation
Add API Gateway integration
Configure basic auto-scaling
Set up deployment rollback procedures
Create deployment runbooks

Week 8: Learning & Compliance Phases

Configure comprehensive CloudWatch dashboards
Set up drift detection monitoring
Implement performance alerting
Create incident response procedures
Schedule automated retraining
Set up compliance reporting
Document operational procedures
Conduct security review

Deliverables:

Production deployment pipeline
Comprehensive monitoring stack
Incident response procedures
Compliance documentation

Common AIDLC Pitfalls

Architecture Phase Pitfalls

Don't: Skip threat modeling

"We'll worry about security later"
-> Results in costly retrofitting

Do: Model threats upfront

1. Identify ML-specific attack vectors
2. Map data flows with trust boundaries
3. Prioritize security controls
4. Budget for security from day one

Infrastructure Phase Pitfalls

Don't: Store credentials in code

# BAD - Never do this
AWS_ACCESS_KEY = "AKIAIOSFODNN7EXAMPLE"
s3 = boto3.client('s3', 
    aws_access_key_id=AWS_ACCESS_KEY)

Do: Use IAM roles

# GOOD - Use default credential chain
import boto3
s3 = boto3.client('s3')  # Uses IAM role automatically

Don't: Public S3 buckets

# BAD
resource "aws_s3_bucket_public_access_block" "bad" {
  block_public_acls   = false  # Dangerous
  block_public_policy = false  # Dangerous
}

Do: Block all public access

# GOOD
resource "aws_s3_bucket_public_access_block" "training_data" {
  bucket = aws_s3_bucket.training_data.id

  block_public_acls       = true
  block_public_policy     = true
  ignore_public_acls      = true
  restrict_public_buckets = true
}

Deployment Phase Pitfalls

Don't: Deploy without testing

# BAD - YOLO deployment
aws sagemaker create-endpoint \
  --endpoint-config-name prod-config

Do: Staged deployment with gates

# GOOD - Progressive deployment
# Stage 1: Deploy to dev -> Integration tests
# Stage 2: Deploy to staging -> Smoke tests  
# Stage 3: Canary to prod (10%) -> Monitor
# Stage 4: Gradual rollout to 100%

Don't: Hardcode configurations

# BAD
model = RandomForest(n_estimators=100, max_depth=10)

Do: Externalize configs

# config.yaml
model:
  type: RandomForest
  hyperparameters:
    n_estimators: 100
    max_depth: 10

Learning Phase Pitfalls

Don't: Ignore model degradation

# BAD - Deploy and forget
model.predict(new_data)

Do: Continuous monitoring

# GOOD - Monitor drift and performance
from sagemaker.model_monitor import DefaultModelMonitor

monitor = DefaultModelMonitor(
    role=role,
    instance_count=1,
    instance_type='ml.m5.xlarge'
)

monitor.create_monitoring_schedule(
    monitor_schedule_name='daily-drift-check',
    endpoint_input=endpoint_name,
    schedule_cron_expression='cron(0 0 * * ? *)'
)

Don't: Train on unvalidated data

# BAD - No quality checks
df = pd.read_csv("s3://prod-data/latest.csv")
model.fit(df)

Do: Quality gates before training

# GOOD - Validate first
df = pd.read_csv("s3://prod-data/latest.csv")

# Schema validation
assert set(df.columns) == expected_columns

# Quality thresholds
assert df.isnull().sum().sum() < 0.01 * len(df)
assert not df.duplicated().any()

# Drift detection
if calculate_drift(df, baseline) > 0.1:
    raise ValueError("Drift detected - review before training")

model.fit(df)

Compliance Phase Pitfalls

Don't: Manual infrastructure changes

# BAD - Click ops in AWS Console
# 1. Click create bucket
# 2. Click add encryption
# 3. Click create role
# 4. ... (undocumented, unrepeatable)

Do: Infrastructure as Code

# GOOD - Version controlled
module "aidlc_pipeline" {
  source = "./modules/aidlc"

  environment       = "production"
  enable_encryption = true
  enable_monitoring = true
}

# terraform plan -> review
# terraform apply -> execute
# terraform destroy -> cleanup

Cost Optimization in AIDLC

Infrastructure Phase Costs

Use Spot Instances for Training

# Save 70% on training (Deployment Phase)
estimator = Estimator(
    use_spot_instances=True,
    max_wait=3600,  # 1 hour wait tolerance
    max_run=1800    # 30 min actual training
)

S3 Lifecycle Policies

# Archive old training data (Infrastructure Phase)
resource "aws_s3_bucket_lifecycle_configuration" "cleanup" {
  bucket = aws_s3_bucket.training_data.id

  rule {
    id     = "archive-old-data"
    status = "Enabled"

    transition {
      days          = 90
      storage_class = "GLACIER"
    }

    expiration {
      days = 365
    }
  }
}

Learning Phase Costs

Stop Dev Resources

# Stop notebook instances when not in use
aws sagemaker stop-notebook-instance \
  --notebook-instance-name dev-notebook

# Schedule with EventBridge + Lambda

Right-Size Monitoring

CloudWatch Logs: 30-day retention (not indefinite)
Metrics: Essential only (not every possible metric)
Alarms: Critical paths (not everything)

Monthly Cost Estimate (Learning AIDLC):

S3 storage (100GB): ~$2.30
Lambda validation (1M invocations): ~$0.20
SageMaker training (10hr/month Spot): ~$7
CloudWatch: ~$5
Total: ~$15/month

What's Next in This Series

This is Part 1 of 4: The AIDLC DevSecOps Approach

Part 2: Infrastructure Phase - Data Pipelines

Implement automated S3 data validation
Build schema and quality checks with Lambda
Create encrypted data pipelines
Set up monitoring and alerts
Complete Terraform infrastructure

AIDLC Focus: Infrastructure Phase implementation

Part 3: Deployment Phase - ML Training

Create custom SageMaker training containers
Implement experiment tracking
Use Spot instances for cost optimization
Build hyperparameter tuning
Set up model registry

AIDLC Focus: Deployment Phase implementation

Part 4: Learning & Compliance Phases - Production Operations

Deploy models with CI/CD
Implement drift detection
Set up comprehensive monitoring
Create incident response procedures
Generate compliance reports

AIDLC Focus: Learning and Compliance Phases

Each part builds on AIDLC principles with hands-on AWS implementation.

Key Takeaways

AIDLC is ML-specific - Addresses unique challenges of data + code + models
Five phases - Architecture, Infrastructure, Deployment, Learning, Compliance
Security throughout - Each phase has integrated security controls
Progressive hardening - Learn with simplified setup, harden for production
Everything as Code - Infrastructure, configs, and pipelines versioned
Defense in depth - Multiple security layers at every phase
Data as first-class - Validation, versioning, and lineage tracking
Continuous monitoring - Model performance doesn't remain static

Remember: AIDLC provides structure for managing the complexity of production ML systems with built-in security and compliance.

Additional Resources

AWS Documentation

AIDLC Implementation

Further Learning

Let's Connect!

Implementing AIDLC for your ML systems? Let's share experiences!

How do you manage ML lifecycles? Share in the comments
Follow me for Part 2
Like if AIDLC resonates with your experience
Share with your team/connects

About the Author

Shoaibali Mir

I'm an engineer with 4+ yrs of experience spanning across DevOps, Data, Cloud and AI/ML Engineering Domain. Along with full time work, I'm pursuing Masters Degree in AI/ML from BITS Pilani.

Connect with me:

Tags: #aws #machinelearning #mlops #aidlc #devsecops #security #cloud #terraform #sagemaker

Top comments (1)

Shoaibali Mir • Jan 2

Update: I published a strategic deep-dive on Medium explaining the why
behind every architecture decision in this series.

If you're wondering "why security-first instead of iterate-fast?" or
"why Spot instances despite interruptions?" — that post breaks down
the tradeoffs tutorials don't cover.

Link: shoaibalimir.medium.com/building-p...

The Problem

What is AIDLC?

The AIDLC Framework

AIDLC vs Traditional DevSecOps

Why AIDLC + DevSecOps?

Security Throughout the Lifecycle

AWS Reference Architecture

Phase 1: Architecture (Design)

Phase 2: Infrastructure (Build)

Phase 3: Deployment (Operate)

Phase 4: Learning (Monitor)

Phase 5: Compliance (Govern)

Learning vs Production: Setting Expectations

What We Build (AIDLC Learning Implementation)

Simplified for Learning

Production Hardening (Beyond This Series)

Why This Approach?

AIDLC Core Principles

1. Security by Design (Architecture Phase)

2. Infrastructure as Code (Infrastructure Phase)

3. Encryption Everywhere (Infrastructure Phase)

4. Least Privilege (All Phases)

5. Comprehensive Monitoring (Learning Phase)

6. Data Quality Gates (Infrastructure Phase)

AIDLC Security Checklist

Architecture Phase

Infrastructure Phase

Deployment Phase

Learning Phase

Compliance Phase

Compliance Controls Mapping

Tentative 8-Week AIDLC Implementation Roadmap

Weeks 1-2: Architecture & Infrastructure Foundation

Weeks 3-4: Infrastructure Phase - Data Pipeline

Weeks 5-6: Deployment Phase - ML Training

Weeks 7-8: Deployment & Learning Phases

Common AIDLC Pitfalls

Architecture Phase Pitfalls

Infrastructure Phase Pitfalls

Deployment Phase Pitfalls

Learning Phase Pitfalls

Compliance Phase Pitfalls

Cost Optimization in AIDLC

Infrastructure Phase Costs

Learning Phase Costs

What's Next in This Series

Part 2: Infrastructure Phase - Data Pipelines

Part 3: Deployment Phase - ML Training

Part 4: Learning & Compliance Phases - Production Operations

Key Takeaways

Additional Resources

AWS Documentation

AIDLC Implementation

Further Learning

Let's Connect!

About the Author

Shoaibali MirFollow

Shoaibali Mir