DevOps vs DevSecOps vs MLOps vs MLSecOps Lifecycle Comparison

Phase / Category	Traditional SDLC & DevOps	DevOps Tools	DevSecOps Focus	DevSecOps Tools	ML Lifecycle & MLOps	MLOps Tools	MLSecOps Focus	MLSecOps Tools
1. Plan & Design	Define user features, APIs, and software architecture.	Jira, Confluence, Miro, Trello	Threat modeling, access control, compliance scoping.	IriusRisk, Microsoft Threat Modeling Tool	Define business targets, data availability, and model metrics.	Jira, Confluence, Lucidchart	Data privacy threat modeling and AI risk assessment.	Privacy-preserving design frameworks, Microsoft EATM
2. Asset Preparation	Write application code, build UI components, and manage repositories.	VS Code, IntelliJ IDEA, Git, GitHub, GitLab	Prevent credential leaks and enforce secure coding practices.	GitGuardian, Talisman, Husky	Source datasets, perform cleaning, labeling, and feature engineering.	DVC, Feast, Labelbox, Snorkel, Airflow	Verify data provenance and detect poisoning or bias.	Great Expectations, Cleanlab, TruLens
3. Build & Train	Compile source code and package application containers.	Jenkins, GitHub Actions, GitLab CI/CD, Bitbucket Pipelines	Static Application Security Testing (SAST) and dependency scanning.	Snyk, Checkmarx, SonarQube, Veracode	Train machine learning models, tune hyperparameters, and track experiments.	MLflow, Weights & Biases, Kubeflow, Ray	Scan open-source models for malware, vulnerabilities, and backdoors.	HiddenLayer Model Scanner, Protect AI Guardian
4. Test & Verify	Execute unit tests, integration tests, and UI tests.	PyTest, JUnit, Selenium, SonarQube	Dynamic Application Security Testing (DAST) and infrastructure scanning.	OWASP ZAP, Burp Suite, Aqua Security, Trivy	Evaluate model quality, accuracy, fairness, and bias.	Evidently AI, TruEra, Fiddler, Deepchecks	Perform adversarial robustness testing and prompt fuzzing.	Counterfit, Adversarial Robustness Toolbox (ART)
5. Deploy	Deploy applications or containers into production.	Docker, Kubernetes, Terraform, Ansible	Secure networking, secrets management, and API key protection.	HashiCorp Vault, CyberArk, AWS Secrets Manager	Deploy trained models as APIs or batch inference services.	TorchServe, Triton, BentoML, Seldon Core	Protect AI endpoints from prompt injection and abuse.	Lakera Guard, LLM Guard, Langfuse
6. Monitor	Monitor infrastructure health, logs, and application performance.	Prometheus, Grafana, Datadog, New Relic	Detect security incidents and unauthorized access.	Splunk, AWS CloudTrail, Wazuh, ELK Stack	Monitor data drift, concept drift, and model degradation.	Arize AI, WhyLabs, Neptune.ai, Datadog	Detect AI attacks, model extraction, and adversarial behavior.	HiddenLayer AISPM, Protect AI Radar

Key Differences by Phase

1. Plan & Design

Discipline	Primary Goal
DevOps	Design software functionality and architecture.
DevSecOps	Integrate security and compliance requirements into the design phase.
MLOps	Define business objectives, datasets, and model success metrics.
MLSecOps	Identify AI-specific risks such as privacy leakage, bias, and misuse.

2. Asset Preparation

Discipline	Main Asset
DevOps	Source Code
DevSecOps	Secure Source Code
MLOps	Training Data and Features
MLSecOps	Trusted, Verified, and Poisoning-Free Data

3. Build & Train

Discipline	Primary Activity
DevOps	Compile and package software.
DevSecOps	Perform security scanning and vulnerability assessments.
MLOps	Train and optimize machine learning models.
MLSecOps	Validate model integrity and detect malicious artifacts.

4. Test & Verify

Discipline	Verification Focus
DevOps	Functional correctness.
DevSecOps	Security vulnerabilities and compliance issues.
MLOps	Accuracy, fairness, and model quality.
MLSecOps	Adversarial robustness and attack resistance.

5. Deploy

Discipline	Deployment Target
DevOps	Applications
DevSecOps	Secure Applications
MLOps	Machine Learning Models
MLSecOps	Protected AI Systems

6. Monitor

Discipline	Monitoring Focus
DevOps	Infrastructure and application health.
DevSecOps	Security events and threats.
MLOps	Data drift, concept drift, and model degradation.
MLSecOps	Prompt injection, model extraction, jailbreaks, and AI attacks.

Simple Relationship

If You Manage...	You Typically Use...
Traditional Software	DevOps
Traditional Software + Security	DevSecOps
Machine Learning Models	MLOps
Machine Learning Models + Security	MLSecOps

Quick Memory Formula

DevSecOps = DevOps + Security

MLOps = DevOps principles applied to Machine Learning

MLSecOps = MLOps + Security