AI Agent Security Training Course
AI Agent Security Training Course is designed to equip cybersecurity professionals, IT teams, developers, and business leaders with the skills required to secure autonomous AI agents, intelligent automation systems, and agentic AI ecosystems.
Course Overview
AI Agent Security Training Course
Introduction
AI Agent Security Training Course is designed to equip cybersecurity professionals, IT teams, developers, and business leaders with the skills required to secure autonomous AI agents, intelligent automation systems, and agentic AI ecosystems. As organizations rapidly adopt Generative AI, Large Language Models (LLMs), AI-powered workflows, and autonomous decision-making systems, securing AI agents has become a critical priority. This course explores AI security architecture, prompt injection defense, adversarial machine learning, AI governance, identity management, data protection, model security, and responsible AI deployment to help organizations mitigate emerging cyber threats.
Participants will gain practical expertise in designing and implementing secure AI agent frameworks, protecting AI-driven applications from data poisoning, model manipulation, unauthorized access, AI hallucination risks, and agent exploitation attacks. Through real-world case studies, hands-on exercises, and industry best practices, learners will understand how to build trustworthy, resilient, and compliant AI systems aligned with modern cybersecurity frameworks such as Zero Trust Security, Secure AI Lifecycle Management, NIST AI Risk Management Framework, and Responsible AI principles.
Course Duration
5 days
Course Objectives
By the end of this course, participants will be able to:
- Understand AI Agent Security fundamentals and emerging cybersecurity challenges.
- Implement secure AI agent architecture and threat modeling techniques.
- Identify and mitigate prompt injection and jailbreak attacks.
- Apply LLM security best practices for enterprise AI applications.
- Design Zero Trust security models for autonomous AI agents.
- Protect AI systems against adversarial machine learning attacks.
- Implement AI identity, authentication, and authorization controls.
- Secure AI agent communication through API security and encryption strategies.
- Apply AI governance, compliance, and risk management frameworks.
- Detect and respond to AI-driven cyber threats using security monitoring tools.
- Develop secure AI agent workflows and automation pipelines.
- Apply Responsible AI and ethical security principles.
- Build organizational strategies for AI Security Operations (AI SecOps).
Target Audience
- Cybersecurity professionals and analysts
- Security Operations Center (SOC) teams
- AI engineers and machine learning developers
- Software developers building AI applications
- Cloud security architects
- IT managers and technology leaders
- Risk, compliance, and governance professionals
- Business leaders adopting enterprise AI solutions
Course Modules
Module 1: Foundations of AI Agent Security
- Understanding agentic AI systems and autonomous workflows
- AI agent attack surfaces and security challenges
- Differences between traditional cybersecurity and AI security
- AI threat landscape and emerging attack techniques
- Security principles for trustworthy AI deployment
- Case Study: Enterprise AI Assistant Security Incident
Module 2: AI Agent Threat Modeling and Risk Assessment
- Identifying AI agent vulnerabilities and attack pathways
- AI-specific threat modeling methodologies
- Risk assessment for autonomous decision systems
- Mapping AI threats using security frameworks
- Building AI security risk registers
- Case Study: Financial Services AI Risk Assessment
Module 3: Large Language Model (LLM) Security
- Understanding LLM vulnerabilities and limitations
- Preventing prompt injection attacks
- Managing AI hallucination and misinformation risks
- Securing retrieval-augmented generation (RAG) systems
- Protecting enterprise AI knowledge bases
- Case Study: Healthcare AI Chatbot Attack Prevention
Module 4: Identity, Access Management, and Zero Trust for AI Agents
- AI agent authentication and authorization strategies
- Implementing Zero Trust AI security architecture
- Managing agent permissions and privileges
- Securing AI-to-system interactions
- Identity lifecycle management for autonomous agents
- Case Study: Banking AI Automation Platform
Module 5: AI Application and API Security
- Securing AI APIs and integrations
- Protecting agent communication channels
- Encryption strategies for AI workloads
- API gateway security controls
- Preventing unauthorized AI agent actions
- Case Study: Cloud-Based AI Enterprise Platform
Module 6: Adversarial AI and Machine Learning Security
- Understanding adversarial machine learning attacks
- Preventing data poisoning and model manipulation
- Securing AI training pipelines
- Model validation and integrity protection
- Detecting malicious AI behavior
- Case Study: Autonomous Vehicle AI System Protection
Module 7: AI Governance, Compliance, and Responsible AI Security
- AI governance frameworks and policies
- Regulatory requirements for AI security
- Responsible AI implementation practices
- AI audit and compliance management
- Managing AI risks across organizations
- Case Study: Global Enterprise AI Governance Program
Module 8: AI Security Operations and Future Defense Strategies
- Building AI Security Operations (AI SecOps)
- Monitoring AI agents using security analytics
- Incident response for AI-driven attacks
- Automating cybersecurity with defensive AI agents
- Future trends in AI cyber defense
- Case Study: Security Operations Center AI Integration
Training Methodology
- Interactive lectures and presentations.
- Group discussions and brainstorming sessions.
- Hands-on exercises using real-world datasets.
- Role-playing and scenario-based simulations.
- Analysis of case studies to bridge theory and practice.
- Peer-to-peer learning and networking.
- Expert-led Q&A sessions.
- Continuous feedback and personalized guidance.
Register as a group from 3 participants for a Discount
Send us an email: info@datastatresearch.org or call +254724527104
Certification
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.