AI+ Security Level 3

Description

AI+ Security Level 3 (5 Days)

Program Detailed Curriculum

Executive Summary
The AI+ Security Level 3 course provides a comprehensive exploration of the intersection between AI and
cybersecurity, focusing on advanced topics critical to modern security engineering. It covers foundational
concepts in AI and machine learning for security, delving into areas like threat detection, response
mechanisms, and the use of deep learning for security applications. The course addresses the challenges
of adversarial AI, network and endpoint security, and secure AI system engineering, along with emerging
topics such as AI for cloud, container security, and blockchain integration. Key subjects also include AI in
identity and access management (IAM), IoT security, and physical security systems, culminating in a
hands-on capstone project that tasks learners with designing and engineering AI-driven security solutions.

Course Prerequisites
Completion of AI+ Security Level 1 and 2
Intermediate / Advanced Python Programming: Proficiency or expert in Python, including deep learning
frameworks (TensorFlow, PyTorch).
Intermediate Machine Learning Knowledge: Proficiency in understanding of deep learning, adversarial AI, and model
training.
Advanced Cybersecurity Knowledge: Proficiency in threat detection, incident response, and network/endpoint
security.
AI in Security Engineering: Knowledge of AI’s role in identity and access management (IAM), IoT security, and
physical security.
Cloud and Container Expertise: Understanding of cloud security, containerization, and blockchain technologies.
Linux/CLI Mastery: Advanced command-line skills and experience with security tools in Linux environments.

Module 1

Foundations of AI and Machine Learning for Security Engineering

1.1 Core AI and ML Concepts for Security
Mathematical Foundations for AI in Security: Examines key mathematical concepts like linear algebra, probability,
and optimization to build and analyze AI models applied in cybersecurity contexts.
Machine Learning Techniques in Cybersecurity: Introduces supervised, unsupervised, and reinforcement learning
methodologies used to enhance security systems through predictive and anomaly detection models.
Core AI Algorithms for Security: Covers key AI algorithms, including decision trees, SVMs, and neural networks,
essential for improving security defenses and automating threat detection.
NLP for Cybersecurity Applications: Focuses on natural language processing techniques for identifying phishing
emails, analyzing texts, and automating threat intelligence extraction in cybersecurity.
Deep Learning for Security Log Analysis: Explores deep learning architectures used in pattern recognition and
anomaly detection within security logs for identifying potential threats and irregular activities.

1.2 AI Use Cases in Cybersecurity
AI in Intrusion Detection and Prevention Systems: Explores the application of AI technologies in enhancing IDS
and IPS, enabling real-time threat detection and automated response mechanisms.
AI for Malware and Ransomware Detection: Examines AI-driven techniques for identifying and classifying malware
and ransomware, improving the efficiency and accuracy of cybersecurity defenses.
Network Traffic Analysis with AI: Investigates the use of AI models to analyze network traffic patterns, enhancing
visibility and facilitating early detection of security anomalies.
Behavioral Analytics for Insider Threat Detection: Focuses on employing behavioral analytics to identify potential
insider threats through user activity monitoring and anomaly detection.
Automating Threat Intelligence with Machine Learning: Discusses the automation of threat intelligence processes
using machine learning algorithms to improve the speed and accuracy of threat detection and response.

1.3 Engineering AI Pipelines for Security
Data Ingestion and Preprocessing for Cybersecurity: Covers techniques for effective data ingestion and
preprocessing tailored for cybersecurity applications, ensuring high-quality data for analysis and model training.
Feature Engineering for Anomaly Detection: Focuses on feature engineering methods used to extract meaningful
insights from data, enhancing the detection of anomalies and security threats.
Engineering AI Pipelines for Cybersecurity: Explores the design and implementation of AI pipelines, encompassing
data preprocessing, model training, and deployment for robust cybersecurity solutions.
Secure Storage and Encryption of Datasets: Examines best practices for secure storage and encryption techniques
to protect sensitive security datasets from unauthorized access and breaches.
Continuous Model Retraining for Evolving Threats: Discusses strategies for continuous model retraining and
updates to adapt to evolving cyber threats, ensuring sustained security effectiveness.

1.4 Challenges in Applying AI to Security
Complexity of Feature Extraction in Cybersecurity: Analyzes the challenges of feature extraction in cybersecurity,
highlighting the importance of relevant features for effective threat detection and analysis.
Managing Imbalanced Security Datasets: Discusses strategies for addressing imbalanced security datasets,
focusing on techniques to effectively manage rare attack types and enhance detection capabilities.
Limitations of Traditional AI Models in Security: Examines the constraints of traditional AI models when applied to
real-world security systems, emphasizing the need for advanced approaches to enhance effectiveness.
Adversarial Attacks on AI Systems: Explores the nature of adversarial attacks targeting AI systems in cybersecurity,
assessing vulnerabilities and developing countermeasures to protect against manipulation.
Mitigating False Positives and Negatives in Threat Detection: Focuses on strategies to minimize risks of false
positives and false negatives in threat detection, improving the reliability and accuracy of security systems.

Module 2

Machine Learning for Threat Detection and Response

2.1 Engineering Feature Extraction for Cybersecurity Datasets
Extracting Network-Level Features in Cybersecurity: Explores techniques for extracting crucial network-level
features such as IP addresses, protocols, and packet sizes for enhanced threat analysis and detection.
Behavioral-Based Feature Extraction from Logs: Focuses on extracting behavioral features from system logs and
user actions to identify anomalies and improve security incident detection.
NLP Applications for Phishing Detection: Examines the use of natural language processing techniques in
identifying and mitigating phishing emails, enhancing email security measures.

Real-Time Feature Extraction for Streaming Data: Discusses methodologies for real-time feature extraction in
cybersecurity systems, enabling immediate response to threats in streaming data environments.
Case Study: Feature Engineering for Spam Detection: Analyzes a case study on feature engineering techniques
specifically applied to spam detection, showcasing practical applications and outcomes.

2.2 Supervised Learning for Threat Classification
Training Supervised Models for Malware Classification: Covers the training of supervised models like SVMs and
decision trees to accurately classify malware and enhance cybersecurity defenses.
Binary Classification for Malicious Traffic Detection: Examines the development of binary classification models
focused on identifying malicious traffic, ensuring effective threat detection and mitigation.
Ensemble Learning for Detection Accuracy: Explores ensemble learning techniques, such as Random Forest and
Gradient Boosting, to improve the accuracy and robustness of cybersecurity detection systems.
Challenges in Supervised Learning for Cybersecurity: Discusses common challenges in supervised learning,
including overfitting, model drift, and maintaining model accuracy in dynamic security environments.
Cross-Validation and Hyperparameter Tuning: Focuses on the importance of cross-validation and hyperparameter
tuning techniques for optimizing cybersecurity models and improving their performance.

2.3 Unsupervised Learning for Anomaly Detection
Clustering Algorithms for Unsupervised Anomaly Detection: Explores the application of clustering algorithms,
such as k-means and DBSCAN, for detecting anomalies without labeled data in cybersecurity contexts.
Detecting Abnormal Network Behavior with Clustering: Examines techniques for identifying abnormal network
behavior using clustering methods, enhancing the ability to spot potential threats.
Dimensionality Reduction for High-Dimensional Security Data: Discusses dimensionality reduction techniques,
including PCA and t-SNE, for simplifying high-dimensional security data analysis and improving model performance.
One-Class SVM for Outlier Detection in Traffic: Focuses on using one-class SVM algorithms to effectively detect
outliers in network traffic, enhancing anomaly detection capabilities.
Engineering Pipelines for Real-Time Anomaly Detection: Covers the design and implementation of engineering
pipelines for unsupervised anomaly detection in real-time systems, ensuring swift threat identification and response.

2.4 Engineering Real-Time Threat Detection Systems
Designing Real-Time Threat Detection Systems: Explores the principles and methodologies for designing AI-
driven real-time threat detection systems to enhance cybersecurity responsiveness.
Streaming Data Pipelines for Network Traffic: Discusses the creation of streaming data pipelines to effectively
process network traffic, enabling timely analysis and threat detection.
Real-Time Anomaly Detection with Kafka and Spark: Examines the use of Apache Kafka and Apache Spark for
implementing real-time anomaly detection, enhancing the scalability and efficiency of security systems.
AI-Based Immediate Response Systems: Focuses on AI-based systems designed for immediate response to
security alerts, ensuring swift mitigation of potential threats and vulnerabilities.
Case Study: Real-Time Intrusion Detection System: Analyzes a case study on building a real-time intrusion
detection system using machine learning, showcasing practical applications and outcomes in cybersecurity.

Module 3

Deep Learning for Security Applications

3.1 Convolutional Neural Networks (CNNs) for Threat Detection
Introduction to CNNs for Security Tasks: Provides an overview of Convolutional Neural Networks (CNNs) and their
architecture, highlighting their applications in various security-related tasks.

CNNs for Network Traffic Classification: Explores the application of CNNs in classifying network traffic and
performing packet inspection to enhance cybersecurity measures.
Detecting Malware Signatures with CNNs: Discusses the use of CNNs to identify malware signatures and patterns
in binary files, improving detection rates and security protocols.
Training and Deploying CNNs for Intrusion Detection: Focuses on the processes involved in training and deploying
CNNs specifically for intrusion detection, ensuring effective real-time threat identification.
Case Study: CNNs for Encrypted Traffic Analysis: Analyzes a case study on utilizing CNNs for analyzing encrypted
traffic, showcasing innovative approaches to maintaining security in complex environments.

3.2 Recurrent Neural Networks (RNNs) and LSTMs for Security
Understanding RNNs and LSTM Networks: Introduces Recurrent Neural Networks (RNNs) and Long Short-Term
Memory (LSTM) networks, focusing on their architecture and applications for analyzing sequential data in
cybersecurity.
LSTMs for Anomaly Detection in Time-Series Data: Explores the use of LSTMs for detecting anomalies in time-
series data, such as user behavior patterns, enhancing security monitoring capabilities.
RNNs for Brute Force and DDoS Attack Detection: Examines the application of RNNs in identifying brute force
attacks and distributed denial-of-service (DDoS) attempts through sequential data analysis.
Sequence Modeling for Phishing Campaign Detection: Discusses sequence modeling techniques for detecting
phishing campaigns, leveraging historical data to identify potential threats.
Hybrid Models Combining CNNs and RNNs: Focuses on the development of hybrid models that integrate CNNs
and RNNs for advanced threat detection, improving accuracy and responsiveness in cybersecurity systems.

3.3 Autoencoders for Anomaly Detection
Basics of Autoencoders in Unsupervised Learning: Introduces the fundamentals of autoencoders, emphasizing
their role in unsupervised learning and feature extraction in cybersecurity applications.
Detecting Zero-Day Exploits with Autoencoders: Explores how autoencoders can be utilized to detect zero-day
exploits and novel attack patterns, enhancing proactive security measures.
Training Autoencoders on Clean Data: Discusses the process of training autoencoders on clean data to identify
deviations, facilitating the detection of anomalous behaviors in security contexts.
Variational Autoencoders for Enhanced Anomaly Detection: Examines the use of variational autoencoders to
improve the accuracy of anomaly detection systems in identifying security threats.
Case Study: Autoencoders for Insider Threat Detection: Analyzes a case study applying autoencoders for insider
threat detection, demonstrating practical implementations and outcomes in identifying malicious activities.

3.4 Adversarial Deep Learning in Security
Generating Adversarial Examples for Deep Learning Models: Explores methods for creating adversarial examples
designed to mislead deep learning models, highlighting vulnerabilities in AI systems.
Hardening Models Against Adversarial Inputs: Discusses techniques for strengthening AI models against
adversarial inputs, focusing on approaches like adversarial training to enhance robustness.
Detecting and Defending Against Adversarial Attacks: Examines strategies for identifying and defending against
adversarial attacks within security systems, ensuring resilience against manipulative threats.
Gradient-Based Attacks and Their Impact: Analyzes gradient-based attacks, such as Fast Gradient Sign Method
(FGSM), and their detrimental effects on the security and reliability of AI models.
Engineering Secure Deep Learning Architectures: Focuses on designing secure deep learning architectures aimed
at preventing model tampering and enhancing overall system integrity against adversarial threats.

Module 4

Adversarial AI in Security

4.1 Introduction to Adversarial AI Attacks
Types of Adversarial Attacks: Provides an overview of various adversarial attack types, including evasion, poisoning,
and inference attacks, and their implications for AI security.
Crafting Adversarial Examples with Gradient-Based Methods: Explores techniques for generating adversarial
examples using gradient-based methods such as Fast Gradient Sign Method (FGSM) and Projected Gradient
Descent (PGD).
Model Evasion Attacks on Malware Detection: Examines model evasion attacks that aim to bypass malware
detection systems, highlighting vulnerabilities and methods used by attackers.
Understanding Data Poisoning Attacks: Discusses data poisoning techniques that manipulate training data to
degrade the performance of AI models, emphasizing their impact on model integrity.
Tools for Adversarial AI Attack Simulation: Reviews tools like CleverHans and Foolbox designed for simulating
adversarial AI attacks, enabling researchers to test and strengthen model defenses.

4.2 Defense Mechanisms Against Adversarial Attacks
Adversarial Training for Model Robustness: Explores the process of adversarial training, which enhances model
robustness by incorporating adversarial examples into the training dataset.
Gradient Masking and Input Transformations: Discusses techniques such as gradient masking and input
transformations aimed at preventing adversarial attacks on AI models.
Defensive Distillation for Hardening AI Models: Examines defensive distillation as a method for strengthening AI
models against adversarial threats, focusing on its effectiveness in increasing resilience.
Ensemble Methods for Enhanced Resilience: Focuses on using ensemble methods to improve model resilience
against adversarial inputs, combining multiple models for better security.
Case Study: Defending Against Adversarial Attacks in Phishing Detection: Analyzes a case study on defending
against adversarial attacks specifically in phishing detection, showcasing successful strategies and outcomes.

4.3 Adversarial Testing and Red Teaming for AI Systems
Red Teaming Techniques for AI Models: Explores methodologies for red teaming AI models to uncover
vulnerabilities and enhance overall security posture through simulated attacks.
Automated Tools for Adversarial Input Testing: Discusses various automated tools designed to test AI models
against adversarial inputs, ensuring robust evaluation of model security.
Simulating Attacks to Improve AI Defense: Focuses on building AI systems that simulate potential attacks, thereby
improving defensive strategies and enhancing model resilience.
Using GANs to Create Adversarial Scenarios: Examines the application of Generative Adversarial Networks (GANs)
in generating adversarial scenarios to better prepare AI systems against potential threats.
Case Study: Stress-Testing a Neural Network: Analyzes a case study on stress-testing a neural network used for
intrusion detection, highlighting methodologies and outcomes in identifying weaknesses.

4.4 Engineering Robust AI Systems Against Adversarial AI
Architectural Approaches for Robust AI Systems: Explores various architectural strategies for developing robust AI
systems tailored for security applications, enhancing reliability and effectiveness.
Incorporating Redundancy and Diversity in Model Design: Discusses the importance of integrating redundancy
and diversity into AI model design to mitigate risks and prevent single points of failure.
Utilizing Secure Enclaves and Hardware Security: Examines the role of secure enclaves and hardware-based
security measures in protecting AI models from threats and unauthorized access.

Implementing AI Explainability Techniques: Focuses on techniques like LIME and SHAP to enhance AI model
transparency, improving trust and understanding of AI decisions in security contexts.
Ensuring Model Security Through Cryptographic Methods: Discusses the application of cryptographic techniques,
such as homomorphic encryption, to secure AI models and safeguard sensitive data during processing.

Module 5

AI in Network Security

5.1 AI-Powered Intrusion Detection Systems (IDS)
Architecting IDS with Deep Learning and ML Models: Explores the design and implementation of Intrusion
Detection Systems (IDS) leveraging deep learning and machine learning models to enhance threat detection.
Engineering AI Systems for Attack Detection: Discusses the engineering of AI systems capable of identifying both
known and unknown attacks within network traffic, improving overall security efficacy.
AI-Driven Rule-Based vs. Anomaly-Based IDS: Examines the advantages and disadvantages of AI-driven rule-based
versus anomaly-based IDS, aiding in the selection of appropriate detection strategies.
Integrating AI into Network Traffic Monitoring Tools: Focuses on the integration of AI into existing network traffic
monitoring tools, such as Snort and Suricata, to enhance their detection capabilities.
Case Study: AI-IDS for Enterprise Network Defense: Analyzes a case study on the implementation of an AI-based
IDS for enterprise network defense, showcasing methodologies and results in improving security posture.

5.2 AI for Distributed Denial of Service (DDoS) Detection
Analyzing Network Traffic for DDoS Pattern Detection: Examines techniques for analyzing network traffic to
enable early detection of Distributed Denial-of-Service (DDoS) attack patterns, enhancing proactive security
measures.
AI Clustering Algorithms for Traffic Distinction: Explores the application of AI clustering algorithms to effectively
differentiate between legitimate traffic and attack traffic, improving detection accuracy.
Reinforcement Learning for Adaptive DDoS Mitigation: Discusses the use of reinforcement learning to develop
adaptive strategies for mitigating DDoS attacks, allowing dynamic response to evolving threats.
Case Study: Real-Time AI System for DDoS Defense: Analyzes a case study on the construction of a real-time AI
system designed to defend against DDoS attacks, highlighting successful methodologies and outcomes.
Performance Optimization of AI Models in High-Speed Networks: Focuses on strategies for optimizing AI models
to perform efficiently in high-speed network environments, ensuring timely threat detection and response.

5.3 AI-Based Network Anomaly Detection
Engineering Anomaly Detection Models: Explores the design and development of anomaly detection models
specifically aimed at identifying abnormal network behaviors to enhance security.
Feature Extraction Techniques for Network Security: Discusses feature extraction methods tailored to network
security, focusing on techniques such as flow data analysis to improve detection capabilities.
Implementing Unsupervised Learning for Anomaly Detection: Examines the use of unsupervised learning
techniques, including Gaussian Mixture Models (GMM) and k-means, for effective real-time anomaly detection in
network environments.
Detecting Lateral Movement in Compromised Networks: Focuses on employing AI models to detect lateral
movement within compromised networks, identifying potential threats to network integrity.
Case Study: Building a Network Anomaly Detection System: Analyzes a case study on the development of a
machine learning-based network anomaly detection system, showcasing practical applications and results.

5.4 Engineering Secure Network Architectures with AI
Integrating AI into Secure Software-Defined Networking (SDN): Explores strategies for incorporating AI
technologies into secure software-defined networking architectures to enhance security and operational efficiency.
Designing AI Systems for Virtualized Network Functions: Discusses the design of AI systems aimed at monitoring
and securing virtualized network functions (VNF), ensuring robust protection in dynamic environments.
AI in Next-Generation Firewalls: Examines the application of AI in next-generation firewalls to enable automated
threat detection and response, improving overall network security.
Case Study: AI-Based Zero-Trust Network Access Control: Analyzes a case study on the implementation of AI-
based zero-trust network access control systems, highlighting innovative approaches and outcomes in enhancing
security.
Performance Tuning of AI Models for High-Throughput Networks: Focuses on techniques for optimizing the
performance of AI models in high-throughput network environments to ensure efficient and timely threat detection.

Module 6

AI in Endpoint Security

6.1 AI for Malware Detection and Classification
Using AI for Malware Identification and Classification: Explores methodologies for employing AI to identify and
classify malware based on behavior and signatures, enhancing detection capabilities.
Leveraging Dynamic and Static Analysis for Malware Detection: Discusses the integration of dynamic and static
analysis features in AI-based malware detection systems to improve accuracy and response times.
Engineering AI Systems for Fileless Malware Detection: Examines the development of AI systems specifically
designed to detect fileless malware and ransomware, addressing emerging threats in cybersecurity.
Case Study: AI-Driven Anti-Malware Solutions: Analyzes a case study on AI-driven anti-malware solutions tailored
for enterprise environments, showcasing effective strategies and outcomes in combating malware.
Optimizing AI Models for Polymorphic and Metamorphic Malware: Focuses on techniques for optimizing AI
models to effectively handle polymorphic and metamorphic malware, ensuring resilience against evolving threats.

6.2 AI for Endpoint Detection and Response (EDR)
Building AI-Driven EDR Systems: Explores the development of AI-driven Endpoint Detection and Response (EDR)
systems for real-time monitoring of endpoint activities, enhancing overall security posture.
Machine Learning for Anomalous Behavior Detection: Discusses the application of machine learning algorithms to
identify anomalous behavior on workstations and servers, improving threat detection and response.
Leveraging AI for Automated Threat Hunting: Examines how AI can be utilized for automated threat hunting
within EDR systems, enabling proactive identification and mitigation of potential threats.
Case Study: Implementing an AI-Driven Endpoint Security Platform: Analyzes a case study on the
implementation of an AI-driven endpoint security platform, highlighting methodologies and successful outcomes in
enhancing endpoint protection.
AI Integration with Existing Security Solutions: Focuses on the integration of AI with existing security solutions,
such as antivirus and firewalls, to create a more comprehensive defense against cyber threats.

6.3 AI-Driven Threat Hunting
AI-Based Automation of Threat Hunting: Explores the use of AI for automating threat hunting processes to detect
hidden attackers and enhance overall cybersecurity defenses.
Engineering UEBA Models for Threat Hunting: Discusses the development of models utilizing User and Entity
Behavior Analytics (UEBA) to improve threat hunting effectiveness by identifying abnormal behaviors.

Leveraging AI to Detect Dormant Malware and APTs: Examines how AI can be employed to uncover dormant
malware and advanced persistent threats (APTs), enhancing proactive threat detection strategies.
Reinforcement Learning for Continuous Learning in Threat Hunting: Focuses on the application of reinforcement
learning to facilitate continuous learning and adaptation in threat hunting operations, improving response
capabilities.
Case Study: AI System for Active Threat Hunting: Analyzes a case study on deploying an AI system for active threat
hunting within an enterprise network, showcasing successful methodologies and outcomes in detecting hidden
threats.

6.4 Implementing Lightweight AI Models for Resource-Constrained Devices
AI for Malware Detection on Mobile and IoT Devices: Discusses the application of AI in detecting malware and
attacks specifically targeting mobile devices and IoT networks, improving overall security.
Federated Learning for Decentralized IoT Security: Examines the use of federated learning as a decentralized
approach to enhance security solutions for IoT devices, promoting collaborative learning without compromising
data privacy.
Using AI to Identify Vulnerabilities in Connected Devices: Focuses on employing AI techniques to identify
vulnerabilities and detect anomalous behaviors in connected devices, strengthening their security posture.
Case Study: Engineering AI for Smart City IoT Protection: Analyzes a case study on engineering an AI system
designed to protect IoT devices within a smart city, highlighting innovative methodologies and outcomes.

Module 7

Secure AI System Engineering

7.1 Designing Secure AI Architectures
Architectural Best Practices for Secure AI Systems: Explores best practices for designing secure AI systems within
cybersecurity frameworks to ensure robust protection against threats and vulnerabilities.
Ensuring Data Privacy in AI-Driven Security Solutions: Discusses strategies for maintaining data privacy and
confidentiality in AI-driven security solutions, focusing on compliance and ethical considerations.
Integrating Encryption Mechanisms in AI Pipelines: Examines the integration of encryption mechanisms into AI
pipelines to safeguard sensitive security data during processing and storage.
Engineering Tamper-Proof and Hardened AI Systems: Focuses on engineering AI systems with tamper-proof and
hardened models to enhance resilience against unauthorized access and manipulation.
Case Study: Secure AI System for Financial Institutions: Analyzes a case study on architecting a secure AI system
specifically designed for financial institutions, showcasing methodologies, challenges, and successful outcomes.

7.2 Cryptography in AI for Security
Leveraging Cryptographic Techniques in AI Models: Explores the use of advanced cryptographic techniques, such
as homomorphic encryption and secure multi-party computation, to enhance the security of AI models.
Securing ML Models Against Inference Attacks: Discusses strategies for protecting machine learning models from
inference attacks through encryption, ensuring the confidentiality of sensitive data.
Implementing Federated Learning for Privacy-Preserving Security: Examines the application of federated
learning to develop distributed, privacy-preserving security models that enhance data protection across devices.
Case Study: Secure Cryptographic Techniques in Healthcare: Analyzes a case study on the application of secure
cryptographic techniques in AI for enhancing security in healthcare settings, highlighting successful outcomes.

Exploring Post-Quantum Cryptography in AI Security: Focuses on the implications of post-quantum cryptography
for AI security, discussing potential threats and the need for robust cryptographic solutions in the future.

7.3 Ensuring Model Explainability and Transparency in Security
Techniques for AI Explainability in Security Contexts: Explores AI explainability techniques, such as LIME and
SHAP, tailored for sensitive security applications, enhancing understanding of AI-driven decisions.
Auditing AI Decision-Making Processes in Cybersecurity: Discusses methods for auditing and verifying AI
decision-making processes in cybersecurity, ensuring transparency and accountability in security operations.
Ensuring Compliance with Regulatory Frameworks: Examines strategies for ensuring compliance with regulatory
frameworks like GDPR and CCPA in AI-driven security systems, emphasizing data protection and user privacy.
Case Study: AI Explainability in Fraud Detection: Analyzes a case study focusing on AI explainability in automated
fraud detection systems, highlighting effective practices and outcomes in maintaining transparency.
Engineering Transparent Models for Trust in AI Security: Focuses on the engineering of transparent AI models to
foster trust and accountability in security applications, promoting responsible AI usage.

7.4 Performance Optimization of AI Security Systems
Engineering Efficient AI Models for Real-Time Security: Explores the design of efficient AI models tailored for real-
time security systems, focusing on achieving high throughput to meet operational demands.
Optimizing AI for Scalability and Latency: Discusses strategies for optimizing AI performance in large-scale
cybersecurity environments, emphasizing scalability and minimizing latency for timely threat detection.
Hardware Acceleration of AI Security Models: Examines the use of hardware acceleration, such as GPUs and TPUs,
to enhance the performance of AI security models, improving processing speed and efficiency.
Model Pruning and Quantization Techniques: Focuses on model pruning and quantization techniques to facilitate
the deployment of AI in edge and embedded devices, optimizing resource utilization.
Case Study: Performance Tuning for Real-Time Fraud Detection: Analyzes a case study on performance tuning an
AI system designed for real-time fraud detection, showcasing effective methodologies and outcomes in enhancing
system efficiency.

Module 8

AI for Cloud and Container Security

8.1 AI for Securing Cloud Environments
Building AI Systems for Cloud Misconfiguration Detection: Explores the development of AI systems designed to
identify misconfigurations in cloud environments, enhancing security and compliance.
Using Machine Learning to Protect Cloud Infrastructure: Discusses the application of machine learning
techniques for monitoring and securing cloud infrastructure across platforms like AWS, Azure, and Google Cloud.
AI-Based Detection of Anomalies in Cloud Workloads: Examines the use of AI to detect anomalies in cloud
workloads and containers, improving threat detection capabilities and resource management.
Case Study: AI-Powered Security for Multi-Cloud Environments: Analyzes a case study on implementing AI-
powered security solutions in a multi-cloud enterprise environment, showcasing successful strategies and
outcomes.
Hardening AI Systems Against Cloud-Based Threats: Focuses on strategies for hardening AI systems against
cloud-based threats and vulnerabilities, including emerging risks like cryptojacking, ensuring robust protection.

8.2 AI-Driven Container Security
Detecting Container Runtime Anomalies Using AI Models: Explores the application of AI models to detect
anomalies during container runtime, enhancing security and operational efficiency in containerized environments.
AI-Based Vulnerability Scanning for Container Images: Discusses the use of AI for automated vulnerability
scanning of container images, improving the identification and mitigation of security risks.
Integrating AI Systems into Kubernetes for Threat Detection: Examines the integration of AI systems within
Kubernetes to enable automated threat detection and response, optimizing container orchestration security.
Case Study: Securing a Containerized Microservices Architecture: Analyzes a case study on securing a
containerized microservices architecture using AI, highlighting effective strategies and outcomes in protecting
application integrity.
Leveraging AI to Detect Unauthorized Container Access: Focuses on the use of AI to identify unauthorized access
to containers and privilege escalations, enhancing overall security posture in cloud-native environments.

8.3 AI for Securing Serverless Architectures
Detecting Threats and Vulnerabilities in Serverless Functions: Explores the use of AI to identify threats and
vulnerabilities in serverless functions, enhancing security in serverless computing environments.
AI-Based Monitoring of Serverless Application Logs: Discusses AI-driven monitoring techniques for analyzing
serverless application logs and events to improve real-time threat detection and response.
Case Study: Securing Serverless Architectures with AI: Analyzes a case study on deploying an AI system designed
to secure serverless architectures, showcasing effective strategies and successful outcomes.
Challenges and Best Practices in Securing Serverless Functions: Examines the challenges and best practices for
utilizing AI in securing serverless functions, providing insights into effective security measures.
AI Models for Detecting Misconfigurations in Serverless Applications: Focuses on the development of AI models
to detect misconfigurations in serverless applications, ensuring compliance and enhancing security resilience.

8.4 AI and DevSecOps
Integrating AI into DevSecOps Pipelines: Explores the integration of AI into DevSecOps pipelines to automate
security testing, enhancing overall software development and deployment security.
Using AI for Static and Dynamic Application Security Testing: Discusses the application of AI for both static (SAST)
and dynamic (DAST) application security testing, improving vulnerability detection and remediation.
AI Models for Secure CI/CD Practices: Examines the development of AI models to ensure secure Continuous
Integration and Continuous Deployment (CI/CD) practices in cloud and hybrid environments, mitigating risks.
Case Study: AI-Driven DevSecOps in Continuous Delivery: Analyzes a case study on the implementation of AI-
driven DevSecOps for automating security testing in a continuous delivery environment, showcasing effective
methodologies and outcomes.
AI for Identifying Security Flaws in Code: Focuses on leveraging AI to identify security flaws in code before
production, enhancing the quality and security of software applications.

Module 9

AI and Blockchain for Security

9.1 Fundamentals of Blockchain and AI Integration
Introduction to Blockchain Technology and Security Properties: Provides an overview of blockchain technology,
focusing on its fundamental security properties and potential applications in cybersecurity.
Engineering Blockchain-Based Solutions for Decentralized Security: Explores the engineering of blockchain-
based solutions aimed at enhancing decentralized security across various applications and industries.
Integrating AI with Blockchain for Secure Data Management: Discusses the integration of AI with blockchain
technology to improve secure data management and create reliable audit trails.

Case Study: AI and Blockchain for Secure Identity Management: Analyzes a case study demonstrating the
combined use of AI and blockchain for secure identity management, highlighting innovative approaches and
successful outcomes.
Cryptographic Techniques for Ensuring Blockchain Data Integrity: Focuses on cryptographic techniques
employed to ensure the integrity of data stored on the blockchain, safeguarding against tampering and
unauthorized access.

9.2 AI for Fraud Detection in Blockchain
Engineering AI Models for Real-Time Fraud Detection in Blockchain: Explores the development of AI models
specifically designed for the real-time detection of fraud within blockchain networks, enhancing security measures.
Using Machine Learning for Fraudulent Transaction Identification: Discusses the application of machine learning
techniques to identify fraudulent transactions and vulnerabilities in smart contracts, improving overall blockchain
security.
Case Study: AI-Based Fraud Detection in Cryptocurrency Exchanges: Analyzes a case study on implementing AI-
driven fraud detection systems within cryptocurrency exchanges, highlighting effective strategies and successful
outcomes.
Leveraging AI for Anomaly Detection in Blockchain Transactions: Focuses on using AI for anomaly detection in
blockchain transaction patterns, helping to identify suspicious activities and potential fraud.
Combining AI with Blockchain for Secure Supply Chain Management: Examines the integration of AI with
blockchain technology to enhance security and transparency in supply chain management, addressing
vulnerabilities and ensuring data integrity.

9.3 Smart Contracts and AI Security
Securing Smart Contracts with AI-Based Vulnerability Detection: Explores the use of AI-based tools to detect
vulnerabilities in smart contracts, enhancing security and trust in blockchain applications.
Engineering AI Systems for Malicious Smart Contract Detection: Discusses the development of AI systems
designed to identify and prevent malicious interactions with smart contracts, safeguarding blockchain transactions.
Leveraging AI for Automated Smart Contract Auditing: Examines the application of AI in automating the auditing
process of smart contracts, improving efficiency and accuracy in identifying potential security flaws.
Case Study: AI and Smart Contract Security in DeFi Applications: Analyzes a case study showcasing the
integration of AI in enhancing smart contract security within decentralized finance (DeFi) applications, highlighting
challenges and solutions.
Ensuring Robustness of AI Models in Blockchain Systems: Focuses on strategies for ensuring the robustness of AI
models utilized in blockchain-based systems, addressing challenges related to data integrity and model
performance.

9.4 AI-Enhanced Consensus Algorithms
Using AI to Optimize Consensus Algorithms: Explores the application of AI techniques to enhance consensus
algorithms like Proof of Work and Proof of Stake, improving blockchain efficiency and performance.
AI-Based Techniques for Improving Consensus Mechanisms: Discusses innovative AI-driven strategies aimed at
increasing the efficiency of blockchain consensus mechanisms, addressing scalability and resource utilization
challenges.
Case Study: AI-Enhanced Proof of Stake: Analyzes a case study on the implementation of AI-enhanced Proof of
Stake, demonstrating its effectiveness in creating energy-efficient blockchain networks.
Engineering AI Systems to Prevent Double-Spending Attacks: Focuses on the development of AI systems
designed to detect and prevent double-spending attacks, ensuring the integrity of transactions within blockchain
networks.
Combining AI and Blockchain for Secure Decision-Making Systems: Examines the integration of AI with
blockchain technology to facilitate secure and distributed decision-making systems, enhancing trust and
transparency in various applications.

Module 10

AI in Identity and Access Management (IAM)

10.1 AI for User Behavior Analytics in IAM
AI-Based Monitoring of User Behavior for Threat Detection: Explores the use of AI to monitor user behavior,
identifying anomalies and potential insider threats to enhance organizational security.
Engineering AI Systems for Automated Access Control: Discusses the development of AI systems that automate
access control decisions based on behavioral patterns, improving security and efficiency.
Case Study: AI-Driven Identity Fraud Detection in Banking: Analyzes a case study on implementing AI for identity
fraud detection in the banking sector, showcasing effective strategies and outcomes.
Using AI to Detect Privilege Escalations and Unauthorized Access: Focuses on the application of AI to identify and
mitigate privilege escalations and unauthorized access, safeguarding sensitive information.
Leveraging Reinforcement Learning for Adaptive Identity Management: Examines the use of reinforcement
learning techniques to create adaptive identity management solutions, enhancing security in dynamic
environments.

10.2 AI for Multi-Factor Authentication (MFA)
Engineering AI Models for Biometric Authentication: Explores the development of AI models for biometric
authentication methods, such as facial recognition and fingerprint scanning, to enhance security measures.
AI for Adaptive MFA Based on Risk Scoring: Discusses the application of AI in creating adaptive Multi-Factor
Authentication (MFA) systems that utilize risk scoring and user behavior for improved security.
Case Study: AI-Enhanced MFA for Securing Corporate Networks: Analyzes a case study on the implementation of
AI-enhanced MFA in corporate networks, highlighting effective strategies and outcomes.
Detecting MFA Circumvention Attempts Using AI: Focuses on leveraging AI to detect attempts to circumvent MFA
systems, enhancing the overall security of authentication processes.
Leveraging AI to Enhance Passwordless Authentication Systems: Examines the role of AI in improving
passwordless authentication systems, increasing user convenience while maintaining robust security.

10.3 AI for Zero-Trust Architecture
Implementing AI for Enforcing Zero-Trust Principles in IAM: Explores the integration of AI technologies to enforce
zero-trust principles in Identity and Access Management (IAM), enhancing security protocols.
AI-Driven Micro-Segmentation for Enhanced Security: Discusses the application of AI in implementing micro-
segmentation strategies, significantly improving security by isolating network segments.
Continuous Monitoring and AI-Based Threat Detection in Zero-Trust Environments: Focuses on continuous
monitoring techniques and AI-based threat detection methodologies designed for zero-trust security frameworks.
Case Study: AI-Powered Zero-Trust Architecture for Enterprise Networks: Analyzes a case study showcasing the
deployment of AI-powered zero-trust architecture within enterprise networks, highlighting key benefits and
outcomes.
Challenges and Best Practices in Deploying AI in Zero-Trust Environments: Examines the challenges faced when
deploying AI in zero-trust environments and outlines best practices for successful implementation.

10.4 AI for Role-Based Access Control (RBAC)
Using AI to Automatically Assign and Manage Roles: Explores the application of AI in automatically assigning and
managing user roles based on behavior and usage patterns, enhancing security and efficiency.
AI-Based Optimization of Access Policies and Privileges: Discusses how AI can optimize access policies and
privileges, ensuring users have appropriate permissions while minimizing security risks.
Leveraging AI to Detect Role Misuse or Privilege Abuse: Focuses on utilizing AI technologies to identify instances
of role misuse or privilege abuse, protecting sensitive information and resources.

Case Study: AI-Driven Role-Based Access Management in Healthcare: Analyzes a case study on implementing AI-
driven role-based access management within healthcare systems, highlighting effective strategies and outcomes.
Engineering Adaptive RBAC Systems with AI: Examines the engineering of adaptive Role-Based Access Control
(RBAC) systems that leverage AI to respond dynamically to changing environments and user needs.

Module 11

AI for Physical and IoT Security

11.1 AI for Securing Smart Cities
Engineering AI Systems for Cyber Threat Detection in Smart Cities: Explores the development of AI systems
designed to detect and mitigate cyber threats within smart city infrastructures, enhancing urban security.
Using AI to Monitor and Protect Critical Infrastructure: Discusses the application of AI in monitoring and securing
critical infrastructure, such as power grids and traffic systems, against potential cyber attacks.
AI-Based Detection of IoT Device Tampering in Smart Cities: Focuses on leveraging AI to identify and prevent
tampering with IoT devices deployed in smart city environments, safeguarding vital services.
Case Study: AI-Driven Security in Smart City Surveillance Systems: Analyzes a case study showcasing the
implementation of AI-driven security measures in smart city surveillance systems, highlighting effectiveness and
outcomes.
Leveraging AI to Ensure Privacy and Security in Public IoT Deployments: Examines strategies for using AI to
maintain privacy and security in public IoT deployments, addressing challenges in urban settings.

11.2 AI for Industrial IoT Security
Using AI to Detect Anomalies in ICS and SCADA Networks: Explores the application of AI technologies to identify
anomalies within Industrial Control Systems (ICS) and SCADA networks, enhancing operational security.
Leveraging Machine Learning to Secure IIoT Environments: Discusses how machine learning can be utilized to
protect Industrial Internet of Things (IIoT) environments from cyber-physical attacks, ensuring safety and reliability.
Case Study: AI for Protecting Manufacturing Systems: Analyzes a case study on the implementation of AI solutions
to safeguard manufacturing systems against sabotage and data theft, highlighting successful strategies.
AI-Based Solutions for Securing Operational Technology (OT) Networks: Focuses on the development of AI-based
strategies designed to secure operational technology networks, mitigating risks associated with cyber threats.
Federated Learning for Securing Decentralized Industrial IoT Systems: Examines the role of federated learning in
enhancing security for decentralized industrial IoT systems, promoting collaborative learning while preserving data
privacy.
11.3 AI for Autonomous Vehicle Security
Engineering AI Systems to Detect Cyber Threats in Autonomous Vehicles: Explores the development of AI
systems aimed at identifying and mitigating cyber threats in autonomous and connected vehicles, ensuring
operational safety.
Using AI for Securing Vehicular Communication Protocols: Discusses the application of AI in securing vehicular
communication protocols, such as Vehicle-to-Everything (V2X), to enhance the integrity of data transmission.
Case Study: AI-Driven Security for Autonomous Vehicle Systems: Analyzes a case study on implementing AI-
driven security measures to protect autonomous vehicle systems from hacking and other cyber threats.
AI-Based Solutions for Detecting Tampering in Vehicle Control Systems: Focuses on leveraging AI technologies
to identify tampering and malicious software within vehicle control systems, safeguarding their functionality.
Leveraging AI to Ensure Privacy and Security of Vehicle User Data: Examines strategies for using AI to protect the
privacy and security of user data in vehicles, addressing emerging challenges in data management.

11.4 AI for Securing Smart Homes and Consumer IoT
AI-Based Monitoring and Anomaly Detection for Smart Home Devices: Explores the use of AI for continuous
monitoring and anomaly detection in smart home devices, enhancing security and user safety.
Engineering AI Models to Prevent Unauthorized Access to Smart Home Networks: Discusses the development of
AI models designed to protect smart home networks from unauthorized access, ensuring the integrity of connected
devices.
Using AI to Secure IoT Ecosystems: Focuses on leveraging AI technologies to enhance the security of various IoT
devices, including smart thermostats, cameras, and lighting systems, within smart homes.
Case Study: AI-Driven Threat Detection in Smart Home IoT Devices: Analyzes a case study on implementing AI-
driven threat detection mechanisms in smart home IoT devices, showcasing effective security strategies.
Implementing Federated Learning for Decentralized Smart Home Security Solutions: Examines the role of
federated learning in developing decentralized security solutions for smart homes, promoting collaboration while
preserving data privacy.

Module 12

Capstone Project – Engineering AI Security Systems

12.1 Defining the Capstone Project Problem
Selecting a Real-World Security Challenge to Solve Using AI: Guides students in identifying and selecting a
pertinent security challenge that can be addressed through AI technologies, fostering critical thinking.
Defining Project Scope and Requirements: Focuses on establishing clear project goals and requirements,
including aspects of threat detection, system defense, and AI automation to ensure project alignment.
Identifying Relevant Datasets and Tools: Discusses the process of locating appropriate datasets and tools essential
for tackling the chosen security challenge, promoting effective research methodologies.
Outlining Deliverables for the Security AI System: Emphasizes the importance of defining deliverables, including a
functional security AI system accompanied by thorough documentation and testing protocols.
Assigning Roles and Tasks for Project Completion: Explores strategies for delegating responsibilities and tasks
among team members to facilitate efficient project management and successful completion.

12.2 Engineering the AI Solution
Designing the AI System Architecture to Meet Security Objectives: Explores the principles of designing robust AI
system architectures tailored to specific security objectives, ensuring effectiveness in threat detection and
mitigation.
Engineering and Training AI Models Based on the Security Challenge: Focuses on developing and training AI
models specifically for the identified security challenge, utilizing appropriate methodologies and algorithms.
Implementing Necessary Preprocessing, Feature Extraction, and Model Optimization: Discusses the importance
of preprocessing data, extracting relevant features, and optimizing models to enhance performance and accuracy in
security applications.
Integrating the AI Model with Existing Security Infrastructure: Examines strategies for seamlessly integrating AI
models into current security frameworks such as Intrusion Detection Systems (IDS), Security Information and Event
Management (SIEM), and firewalls.
Testing the AI Solution Against Real-World Attack Scenarios: Highlights the significance of rigorously testing the
AI solution using real-world attack scenarios to evaluate effectiveness and identify areas for performance
improvement.

12.3 Deploying and Monitoring the AI System
Deploying the AI Security System in a Controlled Environment: Discusses the steps and considerations for
deploying an AI security system in a controlled setting to ensure stability and reliability before broader
implementation.

Continuous Monitoring and Performance Evaluation of the AI System: Emphasizes the importance of ongoing
monitoring and evaluation of the AI system’s performance to ensure it meets security objectives and remains
effective over time.
Implementing Feedback Loops to Improve Model Accuracy and Adaptability: Explores the implementation of
feedback mechanisms that allow the AI system to learn from its performance, enhancing model accuracy and
adaptability to emerging threats.
Deploying the AI System in Real-Time Environments: Focuses on the strategies for successfully deploying the AI
system in real-time environments, emphasizing the use of appropriate monitoring tools for immediate threat
detection.
Documenting Project Implementation and Lessons Learned: Highlights the necessity of thorough documentation
of the project’s implementation process and key lessons learned to inform future projects and enhance best
practices in AI security.

12.4 Final Capstone Presentation and Evaluation
Presenting the Engineered AI Security Solution: Focuses on the effective presentation of the developed AI security
solution, emphasizing key technical decisions and design choices made throughout the project.
Demonstrating the Effectiveness of the AI Model: Discusses methods for showcasing the AI model’s effectiveness
in addressing the identified security challenge, using metrics and real-world examples to illustrate success.
Peer and Instructor Evaluation: Outlines the evaluation criteria used by peers and instructors to assess projects
based on technical complexity, innovation, and practical utility, fostering constructive feedback.
Final Grading and Feedback Session: Highlights the importance of a comprehensive feedback session where final
grades are given, and discussions on potential improvements and scalability of the solutions are held.
Completion and Awarding of AI Security Engineering Certification: Marks the conclusion of the course with the
awarding of certifications, recognizing participants’ achievements in AI security engineering and their readiness to
tackle industry challenges.
AI+ Security Level 3™ Detailed Curriculum
Date Issued: 20/01/2024
Version: 1.1