Introducing a Harmonized MLSecOps Loop for Securing the AI Lifecycle
As AI systems become more deeply integrated into our products and services, ensuring their security is no longer optional — it's a foundational requirement. While frameworks like DevSecOps have matured for traditional software, AI introduces a whole new layer of complexity: sensitive training data, dynamic models, unpredictable outputs, and agentic behavior.
To help organizations navigate this landscape, I’ve analyzed key sources like MITRE ATLAS, the OWASP AI Security Solution Guide, and OWASP AI Data Security Best Practices. Based on this research, I developed a harmonized MLOps infinity loop — along with a set of MLSecOps activities, which are a structured set of activities that span the full AI/ML lifecycle, from scoping and data collection to model operation and governance.
MLOps Stages
Here’s a snapshot of the MLOps stages:
MLSecOps Activities
And here are the List of sample Security activities for each stage of MLOps, what I called MLSecOps activities
🔍 1) Scope & Collect Data
Align with compliance and regulatory requirements
Identify sensitive data early
Conduct third-party risk assessments
Perform threat modeling for data pipelines and model usage
📥 2) Prepare & Ingest Data
Secure data sources and pipelines
Ensure output handling meets security standards
Validate model integrity
Assess vulnerabilities at the data and preprocessing stages
🧠 3) Model Engineering & App Development
Address how models interact with applications
Run SAST, DAST, and SCA scans
Ensure secure model and code repository practices
🧪 4) Test & Evaluate
Conduct adversarial robustness testing
Evaluate for bias and fairness
Perform final security audits and incident response dry-runs
Scan for available agents or external model dependencies
🚀 5) Release
Generate AI/ML BOMs and sign artifacts
Evaluate security posture of released models
Use secure CI/CD pipelines
Validate supply chain and access controls
🔗 6) Deploy & Integrate with Applications
Verify compliance and artifact integrity
Enforce strong encryption and key management
Implement WAFs for LLMs and secure APIs/networks
Ensure runtime observability
🛡️ 7) Operate, Monitor, and Respond
Deploy guardrails and runtime protections
Monitor for adversarial behavior or policy violations
Detect anomalies in agent chains
Manage patching and update cycles
📊 8) Governance & Feedback
Ensure ongoing compliance and governance
Maintain oversight over bias/fairness issues
Manage AI data posture
Audit agent actions and user/machine access regularly
This framework is designed to be flexible yet comprehensive — a living structure you can adapt to your organization’s AI maturity and risk profile.
The following should capture the same in a visual form.
There are more steps in each phase that are not covered here for brevity and for the sake of general audience. In future posts, I’ll dive into each stage in more detail and share lessons from real-world engagements.
If you need more detailed evaluation of your AI deployment and would like to tap into our expertise on AI security or simply want to compare notes — please reach out.