---
url: "https://xcademia.com/courses/securing-ai-pipelines-mlops"
title: "Securing AI Pipelines & MLOps"
description: "Learn AI pipeline security, MLOps protection, supply chain risks and monitoring in this mentor-led advanced training programme."
publishedAt: "2026-03-20T12:11:35.256591+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "AID-0077"
level: Professional
duration_days: "3"
track: AI Security
category: "Cybersecurity & Ethical Hacking"
credential_tier: tier1
price_gbp: "1999"
---

# Securing AI Pipelines & MLOps

> Learn how to secure AI pipelines including training, deployment and monitoring across MLOps environments. This mentor-led programme uses practical scenarios to address supply chain risks, controls and model protection.

## Overview

As organisations scale AI adoption, securing the full AI lifecycle becomes critical. From data ingestion and model training to deployment and monitoring, each stage introduces unique risks that can impact model integrity, reliability, and trust.

This programme focuses on securing AI pipelines and MLOps environments, including model training security, AI supply chain risks, software bill of materials (SBOM) for AI, deployment hardening, and monitoring for model abuse. Participants gain a structured understanding of how to protect AI systems end-to-end.

Delivered as a mentor-led programme using practical scenarios, this course equips participants to design secure AI pipelines, implement controls, and ensure resilience against emerging threats in production environments.

## Prerequisites

- Experience with AI, ML, or data systems.
- Basic understanding of cybersecurity principles.
- Familiarity with cloud or DevOps environments.

## What you will learn

- Analyse risks across AI pipelines
- Design secure MLOps architectures
- Implement controls for training and deployment
- Evaluate AI supply chain vulnerabilities
- Communicate security strategies and risks
- Lead secure AI system implementation

## Skills you will gain

- AI pipeline security
- MLOps risk management
- Model training protection
- AI supply chain security
- Deployment hardening techniques
- Monitoring and abuse detection

## Career progression

- AI Security Engineer
- ML Engineer
- DevSecOps Engineer
- Security Architect
- AI Platform Engineer

## Curriculum

1. **Module 1: Getting Ready**
   - Introduction to AI pipeline security
   - Programme scope and setup
2. **Module 2: AI Lifecycle and Threat Landscape**
   - Stages of AI pipelines
   - Key risks across lifecycle
   - Threat modelling for AI systems
3. **Module 3: Securing Model Training**
   - Data integrity and poisoning risks
   - Secure training environments
   - Access control and governance
4. **Module 4: AI Supply Chain Security**
   - Dependencies and third-party risks
   - Software Bill of Materials (SBOM) for AI
   - Managing model and data provenance
5. **Module 5: Deployment Hardening**
   - Securing model deployment environments
   - API security and access controls
   - Infrastructure and runtime protection
6. **Module 6: Monitoring and Abuse Detection**
   - Detecting model misuse and abuse
   - Logging, alerts, and observability
   - Response and mitigation strategies
7. **Module 7: Governance and Compliance**
   - Policies and controls for AI systems
   - Risk management frameworks
   - Documentation and audit readiness
8. **Module 8: Practical Security Scenarios**
   - End-to-end secure pipeline design
   - Case studies and threat mitigation
   - Optimisation and continuous improvement

## Exam & certification

You will receive an Xcademia certificate of completion based on participation and successful completion of labs and scenario simulations.

## Delivery options

- **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more.
- **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning.
- **Blended** — Combine online and in-person learning for maximum flexibility and impact.

## Frequently asked questions

**Who should attend this course?**

AI engineers, security professionals, and DevOps practitioners working with AI systems.



**Do I need MLOps experience?**

Basic familiarity is recommended, but key concepts are covered.



**Is supply chain security included?**

Yes, including SBOM concepts and third-party risk management.



**Will I learn deployment security?**

Yes, including hardening, monitoring, and runtime protection.



**What certificate will I receive?**

Participants receive an Xcademia Advanced Certificate upon completion.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | AID-0077 |
| Duration | 3 days |
| Level | Professional |
| Track | AI Security |
| Category | Cybersecurity & Ethical Hacking |
| Credential tier | tier1 |
| Price (GBP) | £1999 |

---

## About this content

This Markdown course profile is the citation-grade twin of [Securing AI Pipelines & MLOps](https://xcademia.com/courses/securing-ai-pipelines-mlops). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite.

When citing or quoting, please attribute *Xcademia* and link back to the source URL above.

- Source: https://xcademia.com/courses/securing-ai-pipelines-mlops
- Publisher: Xcademia — https://xcademia.com
- Catalogue index: https://xcademia.com/llms-full.txt