TTAI2800: AI Security Deep Dive
About this Course
AI and machine learning systems introduce unprecedented security challenges that
traditional cybersecurity practices cannot adequately address. AI Security Deep Dive
delivers the specialized knowledge and hands-on experience needed to secure AI/ML
systems against sophisticated attacks, protect sensitive training data, and implement
robust defenses for AI-integrated applications. This intensive course is designed for
programmers building AI-enabled applications, security analysts responsible for
protecting AI systems, cybersecurity professionals expanding into AI security, and
technical managers overseeing AI implementation projects.
Hands-On Format: – Days 1 and 2 feature interactive labs delivered via Jupyter
notebooks, allowing participants to experiment directly with code, attacks, and defenses
in a guided environment. – Day 3 focuses on real-world integration, exposing local models via a Flask API and integrating with a Large Language Model (LLM) using the
Hugging Face Inference API (free tier, requires registration).
Integration labs offer multiple language options: Python/Flask, Java/Spring, ASP.Net,
and Node.js, so participants can choose the stack most relevant to their work.
All labs and exercises are designed to be accessible with minimal setup, and detailed
instructions are provided for each environment.
Throughout three intensive days, you will master the fundamentals of machine learning
from a security perspective, identify and exploit vulnerabilities in AI systems through
hands-on exercises, and implement practical defenses against data poisoning,
adversarial attacks, and privacy breaches. You will gain critical experience securing
traditional applications that integrate AI models, including LLM-powered features, and
learn to validate inputs and outputs to prevent prompt injection and other AI-specific
attacks. The course combines essential AI/ML concepts with real-world security
scenarios, ensuring you understand both the technical foundations and practical
implementation challenges.
With a 50 percent hands-on approach, this course provides extensive practical
exercises where you will simulate adversarial attacks, implement data poisoning
defenses, conduct membership inference attacks, secure API integrations with AI
models, and build comprehensive security strategies for AI-powered applications.
Whether you are developing AI systems, securing existing implementations, or
preparing for the next wave of AI-driven threats, you will leave with the expertise to
protect machine learning applications, implement security-first AI development
practices, and respond effectively to emerging AI security challenges.
Objectives
By the end of this course, you will be able to:
Master AI/ML security fundamentals from the ground up. Understand how machine
learning works, identify attack vectors unique to AI systems, and assess security
implications of different ML model types and deployment patterns.
Identify and exploit AI-specific vulnerabilities through hands-on exercises.
Conduct data poisoning attacks, implement adversarial examples, perform model
inversion and membership inference attacks, and understand the mechanics of AI
system compromise.
Implement comprehensive defenses against AI security threats. Design and deploy
robust input validation, output filtering, differential privacy mechanisms, and secure
training pipelines to protect against known attack vectors.
Secure traditional applications integrating AI models and APIs. Build secure
interfaces to LLM APIs, implement prompt injection defenses, validate AI-generated
content, and establish secure authentication and authorization patterns.
Protect sensitive information in AI training and inference. Apply privacy-preserving
techniques, detect and prevent data leakage through model behavior, and implement
secure data handling practices for AI systems.
Establish enterprise-grade AI security governance and incident response. Develop
AI security policies, create monitoring and detection capabilities, design incident
response procedures for AI breaches, and build security-first AI development workflows.
If your team requires different topics, additional skills or a custom approach, our team
will collaborate with you to adjust the course to focus on your specific learning
objectives and goals.
Audience Profile
This intermediate-level course is designed for programmers and developers building
AI-enabled applications, security analysts and cybersecurity professionals expanding
into AI security, and technical leads responsible for securing AI implementations.
Software engineers integrating machine learning models, security architects designing
AI system defenses, and incident response teams preparing for AI-related threats will
gain essential skills to identify vulnerabilities, implement robust security measures, and
respond to sophisticated AI attacks.
Technical managers, DevSecOps professionals, and compliance officers overseeing AI
security initiatives will also benefit from this course by gaining insights into AI-specific
risk management, security governance frameworks, and regulatory compliance
considerations. Whether you are directly developing AI systems, securing existing AI
implementations, or establishing organizational AI security practices, this course
provides the technical depth and practical experience needed to protect against
emerging AI threats and build resilient AI-powered solutions
At Course Completion
Outline
Please note that this list of topics is based on our standard course offering, evolved
from typical industry uses and trends. We will collaborate with you to tune this course
and level of coverage to target the skills you need most. Topics, agenda and labs are
subject to change, and may adjust during live delivery based on audience skill level,
interests and participation.
Day 1: AI/ML Foundations and Attack Fundamentals
AI/ML Security Foundations
Understanding artificial intelligence and machine learning from a security perspective -
establishing the essential knowledge base for identifying and defending against AI-
specific threats.
Overview of the OWASP Top 10 Application Security Vulnerabilities. Since AI models
are frequently embedded within traditional web or enterprise applications, they inherit
many of the same security risks identified by the OWASP Top 10. Understanding these
common vulnerabilities is essential for developers and security professionals to protect
both traditional and AI-powered applications from cyber threats.
Essential AI/ML concepts for security professionals: supervised vs unsupervised
learning, neural networks, deep learning fundamentals
AI system architecture and deployment patterns: training vs inference, model serving,
API endpoints
The AI threat landscape: why traditional security approaches fail with AI systems
Understanding the AI attack surface: training data, models, inference endpoints, and
integration points
Hands-on Lab (Jupyter Notebook): Setting up an AI security testing environment and
exploring vulnerable ML models
Data Poisoning and Training Attacks
Deep dive into attacks targeting the AI training process, including practical
implementation of data poisoning techniques and defense strategies.
Data poisoning fundamentals: targeted vs untargeted attacks, clean-label attacks
Training data vulnerabilities: data sources, collection pipelines, and validation gaps
Backdoor attacks in machine learning models: trigger insertion and activation
Supply chain security for AI: malicious datasets, compromised pre-trained models
Hands-on Lab (Jupyter Notebook): Implementing data poisoning attacks against
image classifiers and text models
Hands-on Lab (Jupyter Notebook): Building data validation pipelines and poisoning
detection systems
Day 2: Adversarial Attacks and Model Security
Adversarial Examples and Model Manipulation
Comprehensive exploration of adversarial attacks against deployed AI models, including
hands-on generation of adversarial examples and evasion techniques.
Adversarial examples: perturbation-based attacks, gradient-based methods (FGSM,
PGD)
Model evasion techniques: black-box vs white-box attacks, query-based optimization
Physical world adversarial attacks: adversarial patches, real-world evasion
Transferability of adversarial examples across different models and architectures
Hands-on Lab (Jupyter Notebook): Generating adversarial examples using popular
attack frameworks
Hands-on Lab (Jupyter Notebook): Testing adversarial robustness of production AI
systems
Privacy Attacks and Information Extraction
Understanding how attackers can extract sensitive information from AI models,
including membership inference and model inversion attacks.
Membership inference attacks: determining if specific data was used in training
Model inversion attacks: reconstructing training data from model parameters
Property inference: extracting global properties about training datasets
Model extraction and stealing: replicating proprietary models through queries
Hands-on Lab (Jupyter Notebook): Conducting membership inference attacks against
machine learning models
Hands-on Lab (Jupyter Notebook): Implementing model inversion techniques to
extract sensitive information
Differential privacy fundamentals and implementation strategies for AI systems
Day 3: Secure AI Integration and Enterprise Defense
Securing AI-Integrated Applications
Practical security implementation for traditional applications that leverage AI models and
services, including LLM integration patterns.
Secure API integration patterns for AI services: authentication, rate limiting, input
validation
LLM integration security: prompt injection attacks, output validation, context isolation
Building secure AI microservices: containerization, network isolation, monitoring
Input sanitization for AI systems: handling untrusted data, format validation
Hands-on Lab: Implementing secure LLM integration using the Hugging Face Inference
API (Python/Flask, Java/Spring, ASP.Net, Node.js options)
Hands-on Lab: Building input validation pipelines for AI-powered web applications in
your chosen language
Enterprise AI Security Strategy
Comprehensive approach to building organizational AI security capabilities, including
governance, monitoring, and incident response.
AI security governance frameworks: risk assessment, policy development, compliance
Continuous monitoring for AI systems: model drift detection, anomaly identification
AI security testing and red teaming: automated testing, adversarial validation
Incident response for AI breaches: containment strategies, forensic analysis
Hands-on Lab: Setting up AI security monitoring dashboards and alerting systems
Hands-on Lab: Conducting AI security assessments and building remediation plans
Advanced Topics and Emerging Threats
Exploration of cutting-edge AI security challenges and future threat vectors.
Large Language Model (LLM) specific attacks: jailbreaking, instruction following exploits
Multi-modal AI security challenges: vision-language models, cross-modal attacks
AI supply chain security: model provenance, dependency management
Regulatory compliance for AI systems: GDPR, algorithmic auditing requirements
Course Wrap-up and Resources
Next steps in your AI security journey
Essential tools and frameworks for ongoing AI security work
Building and maintaining AI security expertise within your organization
Community resources and continued learning opportunities
Prerequisites
To ensure a smooth learning experience and maximize the benefits of attending this course,
you should have the following prerequisite skills:
Read code and understand basic programming concepts. The course provides
hands-on opportunities using interactive Python and optionally other platforms. Successful students will need to setup a basic development environment,
read and follow program logic and make minor modifications to code.
Awareness of traditional cybersecurity issues. The successful student will have
some prior knowledge of security issues in an IT environment.
Basic understanding of web applications. Students should have some experience
and exposure to basic HTTP based web technology.
Familiarity with data handling and basic statistical concepts. Understanding of data
formats, databases, and basic data analysis principles.
Experience with software development lifecycle and security practices. Knowledge
of testing, deployment, and security integration in development processes.
TTAI2141 AI Security Essentials for Non-Technical Professionals