AI Red Teaming, LLM Security & Private AI Security

Overview

A specialist Academy programme for learners who want to evaluate LLM applications, RAG systems, private AI deployments and AI-agent workflows inside authorised security boundaries. PentestHint Academy positions AI Red Teaming, LLM Security & Private AI Security as a practical supervised learning path, not a generic AI course. The delivery model is 3 Months, Remote, 20 Classes, 1 Hour per Class, 5 Days per Week, 40 Supervised Working Days, and programme fee INR 6,999. The first month includes 20 live practical classes, conducted one hour a day, five days a week. The following two months focus on supervised remote internship and live-project work, documentation, mentor review and final project demonstration. Certificate model: Learners who meet attendance, assignment, project, documentation and review requirements receive a Three-Month Training Certificate and a Remote Internship / Live Project Certificate from PentestHint Academy. Target audience: Cybersecurity professionals, Application-security and VAPT professionals, Developers working with AI applications, Cloud and security engineers, Learners who already understand cybersecurity fundamentals. Prerequisites: Cybersecurity fundamentals, Basic web and API security awareness, Understanding of application architecture is helpful, Commitment to ethical AI security testing. Month 1 roadmap: Week 1 LLM, GenAI and AI application foundations: LLM, GenAI and AI application fundamentals, AI application architecture, AI threat modelling, Ethical AI security testing and authorised scope, OWASP-aligned AI security concepts. Week 2 Prompt, output and agency risks: Prompt injection and jailbreak concepts, Sensitive-information disclosure, Insecure output handling, System prompt protection, Excessive agency and tool-permission risks. Week 3 RAG, private AI and supply-chain security: RAG security, Vector and embedding weaknesses, Data poisoning and knowledge-base integrity, AI supply-chain security, Private/local AI security controls. Week 4 AI security testing and reporting: AI-agent security review, AI security testing methodology, Evidence gathering and validation, AI security reporting and remediation, Final guided assessment exercise. Supervised live project examples: LLM application security review, RAG security assessment, Prompt-injection test plan, AI-agent permission review, Private AI security-control assessment, AI security findings report and remediation roadmap. Required project outputs: Authorised AI security test plan, Evidence notes and validation screenshots, RAG and prompt-risk checklist, AI-agent permission review summary, Remediation roadmap, Final project demonstration. Tools and technologies: LLM application workflows, RAG concepts, prompt-injection test planning, AI security checklists, local or approved AI tools, reporting templates, access-control review and private AI deployment review techniques. Learning outcomes: Review LLM application architecture and trust boundaries, Plan prompt-injection and jailbreak testing inside authorised scope, Assess RAG, vector, embedding and knowledge-base security risks, Document AI security findings with evidence and remediation guidance. Does this course teach unauthorised AI attacks? No. Practical work is limited to authorised labs, approved internal environments and responsible AI security practice. What AI security topics are included? The course covers LLM architecture, prompt injection, jailbreak concepts, RAG security, sensitive-data exposure, excessive agency, tool permissions and private AI security controls. Is this suitable for beginners? It is best for learners who already understand cybersecurity fundamentals, web/API basics or application-security concepts. Will I receive certificates? Learners who meet attendance, assignment, project, documentation and review requirements receive a Three-Month Training Certificate and a Remote Internship / Live Project Certificate from PentestHint Academy. Does this include real external client testing? No. Project work uses authorised labs, approved internal learning environments and supervised live-project style exercises.

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Contact PentestHint to discuss scope, business context, timelines, evidence requirements, and practical next steps for improving security posture.