AI Security Program for LLMs, RAG, AI Agents, and AI Governance

Certificate Program in AI Security

Application closes on 3rd Nov 2026

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Certificate Program in AI Security

Application closes 3rd Nov 2026

Why should you join this program?

Flexible Learning Paths

This program offers learners the flexibility to learn with or without code, featuring dynamic variations of activities suited for technology professionals and strategy leaders alike.
Learn from JHU, a Leading US Research University

Ranked #2 in Computer Information Technology, #9 National University, and #17 Best Global University, reflecting JHU's leadership in research and innovation. (2027 Rankings)

LEARNING OUTCOMES

What will you learn to build and apply?

Through a structured learning journey, you will build the capability to:

Analyze the AI threat landscape and design secure architectures to resist misuse and multimodal attacks
Understand AI security frameworks and address prompt injection, jailbreaks, adversarial inputs and model abuse
Understand AI data pipeline security through data lineage, privacy controls, integrity, and attack mitigation
Understand alignment techniques, trust boundaries, tool abuse, and multi-agent risks in agentic AI systems
Assess AI models for safety, fairness, and robustness, and explore runtime guardrails and jailbreak testing
Explore AI governance for secure delivery, compliance monitoring, human oversight, and regulatory requirements

Earn a Certificate of Completion from Johns Hopkins University

#9 National University Rankings

U.S. News & World Report, 2027
#2 Computer Information Technology

U.S. News & World Report, 2027
#11 Most Innovative Schools

US News and World Report, 2027
#17 Best Global University

U.S. News & World Report, 2027
#1 Biomedical Engineering Program

US News and World Report, 2027

KEY PROGRAM HIGHLIGHTS

Why choose this program?

Learn from Johns Hopkins University Faculty

Learn through recorded lectures and attend monthly faculty-led masterclasses to secure AI systems across their entire lifecycle.
Interactive Mentorship by Industry Experts

Learn from experts through weekly live mentorship sessions focused on industry best practices to secure AI and Agentic AI systems.
Comprehensive Curriculum

Learn from an in-depth curriculum that covers security across the entire AI development lifecycle, from data and model design through deployment and monitoring.
Dynamic Hands-On Activities

Learn to secure modern AI agents and LLM-based architectures through dynamic code, no-code, and low-code projects, lab activities, tools, and technologies.
Earn a Recognized Credential from Johns Hopkins University

Earn a Certificate of Completion and 6.3 CEUs from Johns Hopkins University upon successful completion of the program.
Personalized Program Support

Get personalized assistance from a dedicated Program Support Team and AI-assisted learning support through Great Learning.

Skills you will learn

Agentic AI Security

Runtime Guardrail Integration

Tool Abuse Testing

Guardrail Implementation

AI Threat Modeling

Automated Agentic Workflow

AI Governance & Compliance

Model Card Analysis

AI Security Forensics

Multi-Agent Trust Boundary & Message Verification

Agentic AI Security

Runtime Guardrail Integration

Tool Abuse Testing

Guardrail Implementation

AI Threat Modeling

Automated Agentic Workflow

AI Governance & Compliance

Model Card Analysis

AI Security Forensics

Multi-Agent Trust Boundary & Message Verification

Who is the program for?

Delivered in code and no-code formats, this program is suited to technology professionals and strategy leaders

AI and Data Professionals

Who build, implement, or deploy AI solutions and want to develop system-level risk awareness and practical skills to secure AI across the lifecycle.
Cybersecurity and Tech Professionals

Who want to expand their security expertise to LLMs, RAG architectures, AI agents, model supply chains, and AI workflows.
Leaders, CXOs, and Governance Professionals

Who need to govern risk, ensure compliance, and evaluate secure architectures without writing code.

How is the program learning experience?

Our pedagogy is designed to ensure a holistic learning experience

Learn from Experts

Learn from JHU faculty and industry experts to secure AI and Agentic AI systems
Learn By Doing

Apply Agentic AI concepts through hands-on projects and real-world case studies
Earn a University Credential

Earn a certificate of completion and 6.3 CEUs from Johns Hopkins University
Get Support Throughout the Learning Journey

Program managers will help you stay on track, navigate key milestones & complete the program

Comprehensive curriculum

The curriculum for the Certificate Program in AI Security, crafted by expert faculty at Johns Hopkins University and leading industry experts, covers security across the AI development lifecycle, from data and model design through deployment and monitoring, equipping you with the skills to secure AI systems at every stage.

Code & No-Code

Tools and Techniques
Dynamic

Projects and Activities
Monthly

Faculty-Led Masterclasses

PREPARATORY MODULE

This preparatory module ensures that learners have the technical foundations required to begin the program. They will build proficiency in Python programming, understand the fundamentals of AI systems, and gain foundational knowledge of cybersecurity principles.

MODULE 01: SECURE AI DESIGN AND THREAT LANDSCAPE

In this module, learners will build a strong foundation in the different types of attacks that target AI systems. Learners will be introduced to the CIA Triad, which is the DNA of cybersecurity. They will explore different approaches to building secure architecture for AI applications, and examine why strict trust boundaries and airlocks are essential. Learners will also shift from reactive patching to privacy by design, containing the blast radius early in the AI lifecycle.

WEEK 01: AI SECURITY BY DESIGN

In this week, learners will explore the core threat landscape, from foundational CIA Triad principles to complex multi-model and agent architectures. They will examine how to identify architectural flaws before deployment, map security boundaries for RAG, and structure multi-model workflows. Applying threat modeling and security boundaries ensures that lower-cost and frontier models collaborate safely without expanding the attack surface. Topics Covered: - Introduction to AI Threats and Vulnerabilities - Confidentiality, Integrity, and Availability of the Model Design; Detecting and Preventing Model Misuse and Abuse - Threat Modeling - Secure RAG Architecture - Agent Security Architecture - Secure LLM Architecture - Multi-Model AI Systems: Security Risks in Multi-Model AI Systems, Multimodality, Cost Optimization, and Security Frameworks for Combining Lower and Frontier Models

MODULE 02: SECURING THE AI BUILD LIFECYCLE

In this module, learners will secure the data pipeline, protecting the crown jewels of an AI system against hidden backdoors and compromised training inputs. They will implement these controls to build systemic resilience across every stage of development and deployment, moving step-by-step through the hands-on engineering phase of the AI lifecycle. Learners will also verify data sources, precisely align model behavior, and deploy runtime guardrails to keep AI applications safe, compliant, and under control.

WEEK 02: DATA INTEGRITY AND SUPPLY CHAIN SECURITY

In this week, learners will explore how data pipelines feed the core reasoning engine, making them a crown jewel of AI systems. They will practice how to spot poisoned training data, prevent hidden backdoor vulnerabilities, trace data lineage, and audit third-party AI supply chains so the foundation remains trustworthy. Topics Covered: - Data Processing and Preparation - Advanced Data Storage and Retrieval - Securing the Data Pipeline - Data Privacy and Safety Controls - Data-Specific Attacks: Data Poisoning - Backdoor Attacks via Training Data - Data Lineage - ML Supply Chain

WEEK 03: MODEL EDITING – ABLATION, ALIGNMENT, AND SUPERVISED FINE-TUNING

In this week, learners will explore different ways of implementing mechanisms of model misuse and preventing alignment failures. They will be equipped with the skills to debug models, correct built-in biases, and apply fine-tuning techniques like RLHF (Reinforcement Learning from Human Feedback). Model editing through ablation and alignment gives defenders the precision to remove embedded biases and enforce safety guardrails, without sacrificing performance or erasing what the model has already learned. Topics Covered: - Ablation: From Neuron Lesioning to Component Isolation - Ablation for Security Debugging and Bias Correction - Recovery and Verification: Distillation and Before/After Evaluation (Ablation or De-biasing) - Alignment by Design: Specs, Refusal Behavior, and the Helpful/Harmless Frontier - De-Alignment, Cornering, and Sovereignty - RLHF and Supervised Fine-Tuning in Practice: Curation, Mechanics, and Regression Evaluations

WEEK 04: LEARNING BREAK

WEEK 05: SECURE DEVELOPMENT OF MODELS AND AGENTS

In this week, learners will focus on pre-release verification to ensure system components interact safely before reaching production users. They will explore AI evaluations across safety, quality, and performance dimensions, including responsible AI assessments for bias, fairness, and harmful content. The week also covers securing model training and operating environments as part of a rigorous build lifecycle. By the end, learners will be equipped to validate agent interactions, measure model robustness against adversarial prompts, and ensure reliable system behavior under real-world conditions. Topics Covered: - Securing Model Training and Operating Environments - AI Evaluations Across Safety, Quality, and Performance Dimensions; AI Engineering - Responsible AI Assessments for Bias, Fairness, and Harmful Content - Model Robustness Testing Against Hallucinations and Reasoning Failures

WEEK 06: MODEL RESILIENCE AND GUARDRAILS

In this week, learners will explore how placing an AI model into production exposes it to live adversarial traffic, where static security filters quickly fall short. Implementing dynamic runtime guardrails protects the system in production, helping prioritize the specific real-world ways attackers are exploiting these models. Learners will apply these guardrails, from jailbreak assessments and rate limiting to automated output verifiers, which intercept live prompt injections, block unauthorized agent actions, and keep AI systems in production from being exploited or misused. Topics Covered: - Comprehensive Security Testing for AI Agents, RAG Systems, and LLMs - Jailbreak and Prompt Injection Assessments - Model Security Reviews - Defenses Against Model Theft and Extraction Attacks - Secure Deployment With Policy Engines and Schema Constraints - Runtime Guardrails: Output Validation and Response Verifiers - Security Controls and Guardrails for AI Systems - Defensive Measures: Rate Limits, Canaries, Watermarking, Query Auditing

WEEK 07: LEARNING BREAK

MODULE 03: DEPLOYMENT, OPERATIONS, AND GOVERNANCE

In this module, learners will unpack what it truly means to own a model in production, where performance can drift, integrations can crack, and compliance rules shift without warning. Continuous governance transforms compliance from a static checklist into an active, scalable defense against performance drift and operational risk. From monitoring real-time model behavior to managing third-party risk and building human oversight that scales, this module prepares learners to keep enterprise AI trustworthy long after it goes live.

WEEK 08: INFRASTRUCTURE SECURITY AND AI SECURITY OPERATIONS

In this week, learners will explore how operating AI in production demands purpose-built pipelines that go beyond traditional DevOps. They will implement Artificial Intelligence Security Operations, integrating cryptographic verification, secure model registries, AI Software Bill of Materials (SBOM), secret management, and agent sandboxing into every deployment cycle. Learners will also gain the skills to automate safety gates, enable instant rollback capabilities, and isolate autonomous tools so vulnerabilities are contained before they escalate into operational risk. Topics Covered: - AISecOps: CI/CD for AI Systems, Deployment Gates, Rollback - Securing the Model Registry: Versioning, Signoff, Approvals - Artifact Integrity: Signing, Attestation - Secret Management for AI Systems - AI Software Supply Chain Security - AI SBOM (Software Bill of Materials) - Vector Database Security (Operational View) - Agent Sandboxing and Tool-Abuse Prevention - Comprehensive Security Testing on AI Agents, RAG, LLMs

WEEK 09: AI GOVERNANCE AND MONITORING

In this week, learners will examine how slow, hidden threats can quietly erode AI system integrity over time. Implementing continuous observability frameworks ensures autonomous machine behavior is audited at scale before incidents escalate. Learners will also develop the judgment to know when to step in, building real-time monitoring habits, protecting sensitive data, and responding to incidents with the confidence and clarity that keep AI systems running safely. Topics Covered: - AI Governance - Governance for AI Agents - Model Monitoring (Drift, Data Quality) - AI Governance Maturity Metrics - Observability for Agentic Systems - Incident Response for AI Systems - Data Leak Prevention - Human-in-the-Loop Design: Where, When, How

SELF-PACED MODULE: CLAUDE-BASED AI WORKFLOWS

This module is designed to build practical capability in applying Generative AI and Agentic AI using the Claude ecosystem in real-world contexts. Participants build the ability to design, execute, and evaluate AI-driven workflows for real-world applications, supported by structured learning. *Disclaimer: Access to tools within the Claude ecosystem is not included as part of the program. Participants may choose to explore advanced capabilities independently.

Design and Execute AI Workflows

- Model selection and prompt engineering using Claude Chat - Agentic workflow design and orchestration using Claude CoWork - Plan → Approve → Execute → Iterate framework - Designing workflows with reasoning, tools, and multi-step execution - Applying concepts through real-world case studies

Build and Deploy AI Systems at Scale

- API integration and model usage using Claude Code - Tool integration using the Model Context Protocol - Designing agentic systems with memory, tools, and orchestration - Performance optimization, cost considerations, and system reliability - Responsible AI principles, including alignment approaches such as Constitutional AI

MASTERCLASSES

Learn through monthly live masterclasses from Johns Hopkins University faculty

ATTACK ON AI

In this masterclass, learners will be placed at the front lines of real-world AI attacks, studying how today's most damaging breaches unfolded, what warning signs were missed, and what it cost. Through MITRE ATLAS and OWASP frameworks, learners will dissect live threat patterns to ensure history does not repeat itself. By the end, learners will be able to evaluate emerging threats and weigh AI risk against business value. Topics Covered: - Industry Case Studies (Data Breaches, Statistics) - MITRE ATLAS - OWASP Top 10 for LLMs - Strategic Value vs. Risk Mapping for AI Use Cases - Why AI Security Is Different From Traditional InfoSec - Tech Leadership Considerations: Where AI Security Investment Pays Off - Privacy by Design

SECURE ARCHITECTURE AND DEPLOYMENT OF AI AGENTS

In this masterclass, learners will explore why giving autonomous agents direct execution power introduces risks that standard security controls simply were not built to handle, especially as the stakes change entirely once AI agents can act on their own. They will discover how separating what an agent thinks from what it can do is the critical design choice that stops failures from cascading across multi-agent systems. Topics Covered: - Agentic AI Types - Agentic AI Workflow: Orchestrated Workflow → Single Agent → Multi-Agent → Autonomous - Model Context Protocol (MCP): Trust Boundaries, Client-Host-Tool Chain - MCP Attack Vectors - Security Use Cases of Agentic AI - Incident Response for Agentic Systems - Monthly Frontier Briefing: Current Incidents, Current Mitigations

ANTHROPIC

In this masterclass, learners will explore the Anthropic AI landscape, covering Claude models, Constitutional AI, and key safety and alignment principles. They will apply effective prompt engineering techniques, use the Claude API for tasks and integrations, generate structured outputs, build simple applications, compare Claude with other AI models, and evaluate ethical considerations for deploying AI systems.

SAMPLE CODE-BASED PROJECTS

Note: This is an indicative list of projects and is subject to change.

BACKDOOR IMPLANTATION VIA DATA POISONING: DETECTION, REMOVAL, AND INCIDENT RESPONSE

Domain: AI Security / Adversarial ML / Incident Response Objective: Implant a backdoor into a machine learning model through data poisoning, then apply detection and removal techniques to cleanse the model. Document the full lifecycle in an incident report covering root cause, indicators of compromise, and recommended pipeline mitigations. Skills You Will Learn: Data Poisoning, Backdoor Detection, Model Cleansing, Incident Response, Adversarial ML Defense, AI Supply Chain Security

ABLATION AND DISTILLATION ON A TABULAR FRAUD-DETECTION MODEL FOR SECURITY AND BIAS REMEDIATION

Domain: AI Security / Model Robustness / Responsible AI Objective: Apply targeted ablations to a fraud-detection model to address a security vulnerability and a bias issue, then use knowledge distillation to recover lost performance. Validate results through before-and-after evaluation of accuracy, robustness, and fairness. Skills You Will Learn: Ablation Techniques, Tabular ML Security, Bias and Fairness Analysis, Knowledge Distillation, Responsible AI Assessment

EXPLORING TOOL ABUSE SCENARIOS IN AGENTIC AI AND RECORDING RESULTS

Domain: AI Security / Agentic AI Security / Red Teaming Objective: Test and evaluate how an agentic AI system's tools and capabilities can be manipulated through adversarial inputs to perform unintended actions. Record findings in a structured log and propose guardrails and least-privilege controls to mitigate identified risks. Skills You Will Learn: Agentic AI Red Teaming, Prompt Injection, Tool Abuse Testing, Trust Boundary Analysis, Security Logging, Guardrail Design

SAMPLE NO-CODE / LOW-CODE PROJECTS

Note: This is an indicative list of projects and is subject to change.

BUILDING AND SECURING AN AI WORKFLOW USING A LOW-CODE PLATFORM

Domain: AI Security / Workflow Automation Security Objective: Design and implement an automated AI workflow using a low-code visual platform, connecting AI models with external tools and data sources. Identify and address common security risks in automated workflows such as insecure connections, over-permissive integrations, and unvalidated inputs. Skills You Will Learn: Workflow Automation, AI Integration, Secure Configuration, API Security Basics, Credential Management

GENERATING AN AI BILL OF MATERIALS (AI-BOM) USING NO-CODE TOOLS

Domain: AI Supply Chain Security Objective: Use no-code tooling to catalog the key components of an AI-enabled application, including models, datasets, libraries, and third-party dependencies. Produce a structured inventory that supports transparency, license tracking, and supply chain risk management. Skills You Will Learn: AI-BOM Generation, Component Inventory, Supply Chain Security, Provenance Tracking, License Management

MANAGING SUPPLY CHAIN RISKS BY ANALYZING MODEL CARDS

Domain: AI Supply Chain Security / AI Governance Objective: Evaluate publicly available model cards to assess risks related to training data, intended use, bias, and licensing before integrating third-party models into a production environment. Deliver a structured risk assessment and governance checklist based on findings. Skills You Will Learn: Model Card Analysis, Supply Chain Risk Assessment, Model Provenance Evaluation, Third-Party Model Governance

Note: The curriculum listed above is indicative and subject to updates as technology evolves.

What activities will you learn from?

Build hands-on AI security skills through immersive labs, and real-world case studies.

SAMPLE LAB ACTIVITY

Threat Modeling and Attack Testing on an LLM Application

Description

Build a threat model for a provided LLM-based application by mapping its attack surface and data flows. Design and execute a set of prompt injection and jailbreak attacks to validate identified risks, referencing established LLM security frameworks.

Skills you will learn

LLM Threat Modeling
Prompt Injection Testing
Attack Surface Mapping
Risk Assessment

SAMPLE LAB ACTIVITY

Implementing Runtime Guardrails on an LLM Application

Description

Apply runtime guardrails to a baseline LLM application to enforce topic boundaries, block adversarial inputs, and prevent data leakage. Run a simulated incident to test detection and response capabilities.

Skills you will learn

NVIDIA NeMo Guardrails
Guardrail Implementation
Input/Output Validation
AI Monitoring
Incident Simulation

SAMPLE LAB ACTIVITY

Building an AI Compliance Mapping Matrix

Description

Construct a comparative matrix mapping major AI regulations and frameworks against common control areas such as risk, transparency, and accountability. Use the matrix to identify compliance gaps and overlaps relevant to real-world AI deployments.

Skills you will learn

AI Governance and Compliance (GRC)
Regulatory Analysis
Control Mapping
Compliance Assessment

SAMPLE LAB ACTIVITY

Prompt Airlines: AI Security CTF Challenge

Description

Participate in a guided Capture the Flag challenge focused on interacting with and exploiting LLM-based applications. Practice identifying vulnerabilities and applying adversarial thinking in a safe, gamified environment.

Skills you will learn

Prompt Injection
Jailbreak Techniques
LLM Red Teaming
Guardrail Bypass Analysis

This is an indicative list of lab activities and case studies and is subject to change.

What no-code and code tools will you explore?

Work with a comprehensive suite of modern code-based or no-code tools

Claude
OpenAI
Gemini Notebook
Teachable Machine
n8n
KNIME
Zapier
Google Colab
Python
VS Code (Visual Studio Code)
Jupyter Notebook
OWASP Threat Dragon
Hugging Face
Vector Databases
DSPy
LangChain
LlamaIndex
Amazon CloudWatch
Databricks

This is an indicative list, and relevant tools are reviewed for inclusion periodically. Access to certain tools may require additional subscriptions or fees.

Earn a Certificate of Completion from Johns Hopkins University

Earn a globally recognized credential from a top U.S. university and showcase it to your network.

* Image for illustration only. Certificate subject to change.

Who are the faculty for the program?

Learn from renowned JHU faculty and build the intuition and strategic judgment to secure modern AI systems

Ian McCulloh, Ph.D.

Director, AI Executive & Professional Education, Johns Hopkins University

Served as Chief Data Scientist & MD of AI, Accenture Federal Services

Author of three books and over 100 peer-reviewed papers

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Kiran Chittargi

Adjunct Faculty - Computer Science, Johns Hopkins University

Program Director at Microsoft

Global Expansion Lead for Azure

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Dr. Pedro Rodriguez

Faculty, Johns Hopkins University AI Program

Leads the Information Science Branch at the JHU's Applied Physics Lab

20+ years of expertise in AI/ML algorithms

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Dr. Art Sedighi

Adjunct Faculty, Johns Hopkins University

Ph.D. in Systems Engineering and in Cybersecurity/Cryptography

AI and security expert focused on LLM security, & cloud architectures

Know More

Who are the mentors for weekly live sessions?

Learn from seasoned AI industry mentors to apply concepts and build practical skills

Aniket Amdekar

General Manager - Cyber Defence Education Great Learning
Ivan Marchany

SOAR Engineer

Note: This is an indicative list and is subject to change based on the availability of mentors.

Program Fee

The program fee is USD 1,800

Invest in your career

Build hands-on expertise in operationalizing responsible AI practices across the enterprise
Dedicate 8-9 hours weekly to faculty-led learning, industry mentorship, projects, and case studies
Learn from AI experts in weekly live online sessions focused on real-world case studies
Receive a Certificate of Completion and 6.3 CEUs from Johns Hopkins University

Take the next step

00 : 00 : 00

Apply to the program now or schedule a call with a program advisor

Learn more about the program

Application closes: 3rd Nov 2026

Talk to our advisor for offers and course details

Application process

Applications close once the required number of participants enroll. Apply early to secure your spot

Fill application form

Apply by filling out a simple online application form.
Review process

A panel from Great Learning will review your application to determine your fit for the program.
Join program

Receive an offer for a seat in the upcoming cohort of the program post a final review.

Limited seats available

Build AI Systems That Resist Attack

Delivered in Collaboration with:

Johns Hopkins University is collaborating with online education provider Great Learning to offer the Certificate Program in AI Security. Great Learning is a professional learning company with a global footprint in 170+ countries. Its mission is to make professionals around the globe proficient and future-ready. This program leverages JHU's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning manages the enrollments and provides industry experts, student counselors, course support and guidance to ensure students get hands-on training and live personalized mentorship on the application of concepts taught by the JHU faculty.

Got more questions? Talk to us

Connect with our advisors and get your queries resolved

Speak with our expert +14109365774 or email to jhu-aisecurity@mygreatlearning.com

AI Security Program for LLMs, RAG, AI Agents, and AI Governance

Certificate Program in AI Security

Trusted by millions of learners

Certificate Program in AI Security

Why should you join this program?

Flexible Learning Paths

Learn from JHU, a Leading US Research University

What will you learn to build and apply?

Earn a Certificate of Completion from Johns Hopkins University

Why choose this program?

Learn from Johns Hopkins University Faculty

Interactive Mentorship by Industry Experts

Comprehensive Curriculum

Dynamic Hands-On Activities

Earn a Recognized Credential from Johns Hopkins University

Personalized Program Support

Agentic AI Security

Runtime Guardrail Integration

Tool Abuse Testing

Guardrail Implementation

AI Threat Modeling

Automated Agentic Workflow

AI Governance & Compliance

Model Card Analysis

AI Security Forensics

Multi-Agent Trust Boundary & Message Verification

Agentic AI Security

Runtime Guardrail Integration

Tool Abuse Testing

Guardrail Implementation

AI Threat Modeling

Automated Agentic Workflow

AI Governance & Compliance

Model Card Analysis

AI Security Forensics

Multi-Agent Trust Boundary & Message Verification

Who is the program for?

How is the program learning experience?

Learn from Experts

Learn By Doing

Earn a University Credential

Get Support Throughout the Learning Journey

Comprehensive curriculum

PREPARATORY MODULE

MODULE 01: SECURE AI DESIGN AND THREAT LANDSCAPE

WEEK 01: AI SECURITY BY DESIGN

MODULE 02: SECURING THE AI BUILD LIFECYCLE

WEEK 02: DATA INTEGRITY AND SUPPLY CHAIN SECURITY

WEEK 03: MODEL EDITING – ABLATION, ALIGNMENT, AND SUPERVISED FINE-TUNING

WEEK 04: LEARNING BREAK

WEEK 05: SECURE DEVELOPMENT OF MODELS AND AGENTS

WEEK 06: MODEL RESILIENCE AND GUARDRAILS

WEEK 07: LEARNING BREAK

MODULE 03: DEPLOYMENT, OPERATIONS, AND GOVERNANCE

WEEK 08: INFRASTRUCTURE SECURITY AND AI SECURITY OPERATIONS

WEEK 09: AI GOVERNANCE AND MONITORING

SELF-PACED MODULE: CLAUDE-BASED AI WORKFLOWS

Design and Execute AI Workflows

Build and Deploy AI Systems at Scale

MASTERCLASSES

ATTACK ON AI

SECURE ARCHITECTURE AND DEPLOYMENT OF AI AGENTS

ANTHROPIC

SAMPLE CODE-BASED PROJECTS

BACKDOOR IMPLANTATION VIA DATA POISONING: DETECTION, REMOVAL, AND INCIDENT RESPONSE

ABLATION AND DISTILLATION ON A TABULAR FRAUD-DETECTION MODEL FOR SECURITY AND BIAS REMEDIATION

EXPLORING TOOL ABUSE SCENARIOS IN AGENTIC AI AND RECORDING RESULTS

SAMPLE NO-CODE / LOW-CODE PROJECTS

BUILDING AND SECURING AN AI WORKFLOW USING A LOW-CODE PLATFORM

GENERATING AN AI BILL OF MATERIALS (AI-BOM) USING NO-CODE TOOLS

MANAGING SUPPLY CHAIN RISKS BY ANALYZING MODEL CARDS

What activities will you learn from?

Threat Modeling and Attack Testing on an LLM Application

Implementing Runtime Guardrails on an LLM Application

Building an AI Compliance Mapping Matrix

Prompt Airlines: AI Security CTF Challenge

Claude Mythos: When AI Becomes the World's Best Hacker

Inside OpenClaw: Real Attacks on Autonomous AI Agents

First Fully Autonomous AI Attack

Camoleak and Echoleak: Zero-Click Exploits on RAG and Agents