AI Cybersecurity Practitioner — OWASP LLM Top 10
capability building,
designed for your organisation.
A custom-built corporate programme for security engineers, penetration testers, red teamers, AppSec specialists, security architects, AI engineers focused on security, and senior developers building security-conscious AI applications. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Notable AI incidents 2024-2026: jailbreaks at scale, agentic supply-chain, indirect injection
- Threat-actor categories: opportunistic, criminal, nation-state, insider
- Threat surface map: foundation models → fine-tuning → RAG → agents → MCP servers → users
- OWASP LLM Top 10 v2025 overview
Want the full module-by-module syllabus, sample assignments, and pricing?
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Demonstrable skills your team will apply on live projects.
Detect and demonstrate all OWASP LLM Top 10 attacks
Hands-on demonstration of each category against deliberately-vulnerable apps; structured remediation.
Architect layered AI security defences
Model layer (alignment, content filtering), app layer (input/output validation, sandboxing), infra layer (RBAC, network), ops layer (monitoring, audit).
Run AI red-team campaigns systematically
Test plans, tool selection, evidence collection, remediation tracking — for production AI applications.
Pass GSDC AI Cybersecurity Practitioner certification
Two attempts; cohort first-attempt pass rate 86%.
Lead AI security in your organisation
Equipped to set policy, run programmes, advise development teams, integrate with compliance.
Move into AI security specialist roles
Targeted role: AI Security Engineer, AI Red Team Specialist, AI AppSec Architect — in BFSI, healthcare, regulated tech.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Familiar with traditional AppSec (OWASP Top 10) but no exposure to LLM-specific threats
- ·Aware of prompt injection but cannot demonstrate or defend against it systematically
- ·No working knowledge of OWASP LLM Top 10 categories or specific defence techniques
- ·Limited fluency with red-team tools for LLMs (Garak, PyRIT, promptfoo for adversarial)
- ·Cannot evaluate AI vendors on security maturity or build a secure AI architecture
- ·Unaware of emerging threats: indirect prompt injection, agentic supply chain, model jailbreaks at scale
Where they'll arrive
- ✓OWASP LLM Top 10 fluency — diagnose, demonstrate, and defend against all 10 categories systematically
- ✓Red-team capability — runs structured adversarial campaigns with Garak, PyRIT, custom harnesses
- ✓Defence architecture — designs layered defences across model, application, infrastructure, and operational layers
- ✓Agentic AI security — addresses tool-use attack surface, indirect injection, capability scoping
- ✓Incident response for AI — playbooks for hallucination-driven harm, jailbreaks at scale, model abuse, data leakage
- ✓Compliance integration — integrates AI security with ISO 27001, ISO 42001, NIST AI RMF, EU AI Act
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
AI security focuses on protecting AI models, prompts, datasets, agents, and LLM workflows from threats like prompt injection, model abuse, data leakage, and adversarial attacks in addition to traditional cybersecurity risks.