Protecting machine learning models from attacks

An advanced, hands-on course dedicated to key aspects of machine learning model security.

Duration

16h · 2 days

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Who it's for

Ideal for teams that…

1 AI engineers and data scientists

2 ML solution architects

3 Professionals responsible for deploying AI solutions in organizations

4 Cybersecurity specialists

5 Developers working on advanced ML models

Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

✓

Identification of advanced attack vectors targeting ML models

✓

Methods to prevent manipulation of training data

✓

Practical techniques for securing training and inference processes

✓

Tools and strategies for protecting sensitive models against cyber threats

Requirements

What you should know before we start

Basic knowledge of Python and ML libraries (NumPy, scikit-learn, TensorFlow/PyTorch)

Program · 6 modules

What we actually do

Day 1: Fundamentals of ML Model Security

M01

Module 1: Introduction to ML ecosystem threats

· Characteristics of modern AI model attacks
· Consequences of successful breaches
· Case studies of intrusions and manipulations in real-world projects

M02

Module 2: Types of attacks on ML models

· Adversarial attacks: methods of generating adversarial samples
· Attacks on training data privacy
· Information leakage from trained models
· Vulnerability analysis of different ML architectures
· Attacks targeting ML infrastructure

M03

Module 3: Workshop – Threat identification

· Simulating attacks on sample classification and regression models
· Analyzing traces and penetration mechanisms of ML models

Day 2: Advanced Protection Techniques

M04

Module 4: Methods for securing ML models

· Adversarial training techniques
· Federated learning for enhanced privacy
· Implementing obfuscation and data privacy mechanisms
· Strategies for risk reduction in ML workflows

M05

Module 5: Workshop – Practical model protection

· Designing resilient ML architectures
· Implementing advanced defense techniques
· Security testing of ML models
· Developing security policies for ML teams

M06

Module 6: Security tools and frameworks

· Overview of open-source tools for model protection
· Analysis of specialized ML cybersecurity libraries
· Automating security verification processes
· Integrating security tools with ML pipelines

Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.

How we work

From brief to retro in 30 days.

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

✓Quote within 48h of the brief

✓First session within 30 days

✓Pilot before the full decision

✓VAT invoice, payment in instalments possible

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