Cancel Preloader

What You'll Learn

Explain how AI security differs from traditional application security and identify risks unique to LLMs
RAG systems
tool-calling applications
and memory,Design secure agentic AI architectures using layered controls
trust boundaries
least privilege
human approvals
and policy enforcement.,Identify
execute
and defend against prompt injection
jailbreaks
document poisoning
memory poisoning
parameter injection
and unauthorized agent actions.,Build secure AI applications that include chat interfaces
RAG pipelines
tool calling
persistent memory
and autonomous agents.,Implement prompt validation
input sanitization
risk scoring
output filtering
context isolation
trusted-source validation
and AI guardrails.,Create an integrated AI Security Gateway that protects prompts
retrieved content
tools
memory
model responses
and agent workflows.,Establish enterprise AI governance using inventories
ownership models
lifecycle controls
risk assessments
approval gates
policies
and evidence management.,Build governance dashboards for AI usage
cost
risk
model evaluation
drift
prompt quality
sensitive-data exposure
incidents
and human oversight.,Map operational AI controls to frameworks and standards including NIST AI RMF
ISO/IEC 42001
and the EU AI Act.,Communicate AI security risks
control gaps
remediation priorities
and governance recommendations to executives
engineers
risk teams
auditors and others

Requirements

No previous AI security or governance experience is required; the course begins with foundational concepts.,Basic familiarity with computers
software applications
and internet technologies will be helpful.,Beginner-level Python knowledge is recommended
but the practical exercises are explained step by step.,A computer capable of running Python
Visual Studio Code
and the course project files is required.,Students should be comfortable installing software and running commands in a terminal or command prompt.,Basic awareness of APIs
databases
cloud applications
or cybersecurity concepts is useful but not mandatory.,Docker is introduced for packaging the final secure AI application; prior Docker experience is not required.,An interest in AI security
generative AI
risk management
compliance
or enterprise governance is the most important prerequisite.

Description

This course contains the use of artificial intelligence.

Step into one of the fastest-growing career paths in technology with Agentic AI Security & LLM Governance Career Bootcamp, a comprehensive, hands-on program designed to help you secure, monitor, govern, and lead modern artificial intelligence systems.

As organizations adopt Generative AI, Large Language Models, Retrieval-Augmented Generation, and autonomous AI agents, traditional cybersecurity practices are no longer enough. AI applications introduce new risks, including prompt injection, jailbreak attacks, document poisoning, memory manipulation, unauthorized tool execution, sensitive data exposure, hallucinations, and uncontrolled agent behavior. This course gives you the practical skills required to understand these threats, attack vulnerable systems responsibly, implement effective defenses, and establish enterprise-grade governance controls.

You will begin with the core responsibilities of a Principal AI Security Engineer, including secure AI architecture, risk assessment, enterprise controls, technical leadership, and communication with executives, legal teams, auditors, and product leaders. You will then build your own AI chat assistant and progressively enhance it with RAG, tool calling, persistent memory, and autonomous agent capabilities.

Throughout the course, you will test real-world AI attack scenarios and implement practical defenses such as prompt validation, risk scoring, input filtering, output guardrails, context isolation, trusted-source validation, least-privilege tool permissions, human approval workflows, secure memory controls, and agent decision validation. You will also build a complete AI Security Gateway that integrates security across prompts, models, retrieved documents, tools, memory, and agent workflows.

The governance portion of the course takes you beyond security engineering into enterprise AI governance, responsible AI, AI risk management, and regulatory compliance. You will learn how to create an enterprise AI inventory, track usage and cost, evaluate model performance, monitor drift, govern prompts and responses, oversee agent actions, protect sensitive data, and manage governance evidence.

You will build dashboards and practical projects covering AI risk scoring, model evaluation, hallucination monitoring, prompt traceability, human oversight, RAG governance, policy management, compliance mapping, approval workflows, incident management, audit trails, and executive reporting. You will also learn how to align operational controls with major frameworks and standards, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act.

By the end of the course, you will have created a portfolio of hands-on AI security and governance projects, including an AI security gateway, risk remediation engine, model evaluation dashboard, agent activity monitor, guardrail enforcement service, incident center, and an Enterprise AI Governance Command Center.

This bootcamp is ideal for cybersecurity professionals, AI engineers, developers, architects, governance specialists, auditors, consultants, risk leaders, and career changers preparing for roles such as AI Security Engineer, LLM Security Engineer, Principal AI Security Engineer, AI Governance Specialist, Responsible AI Lead, or AI Risk Manager.

Build the technical, governance, and leadership skills required to secure the next generation of intelligent systems.

Who this course is for:

Cybersecurity professionals who want to specialize in LLM security
agent security
and generative AI risk.,AI engineers
machine-learning engineers
and developers responsible for building secure AI applications.,Application security
cloud security
product security
and security architecture professionals expanding into AI security.,Governance
risk
compliance
privacy
audit
and responsible-AI professionals who need practical technical knowledge.,Enterprise architects and solution architects designing production AI
RAG
agentic
and tool-calling platforms.,Technology leaders
engineering managers
CISOs
AI governance leaders
and risk executives responsible for enterprise AI adoption.,Consultants and forward-deployed engineers helping organizations implement secure and governed AI solutions.,Students and career changers preparing for roles such as AI Security Engineer
Principal AI Security Engineer
AI Governance Specialist
AI Risk Manager
or Responsible AI Lead.,Professionals preparing to lead AI security assessments
governance programs
red-team exercises
control implementations
or regulatory readiness initiatives.,Anyone seeking a project-based portfolio that demonstrates practical skills across AI security engineering and enterprise LLM governance.