AI Cybersecurity Practitioner OWASP LLM Top 10 Corporate Training
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?
One PDF sent to your inbox in under a minute.
Enterprise learning solutions built for corporate teams.
Go beyond standard classroom delivery with enterprise-ready learning infrastructure, managed execution, capability insights, and production-like practice environments designed for corporate scale.
Enterprise Command Center (LMS+)
Managed Batches (End-to-End Execution)
Capability Audits (Pre-Training Intel)
Custom Chaos Sandboxes
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 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.
Every OWASP LLM category covered with labs
This OWASP LLM Top 10 training goes beyond theory every threat category has a dedicated red-team lab where learners exploit and defend against real vulnerabilities in deliberately-built target applications.
Built for enterprise security teams
AI cybersecurity training for enterprises requires more than awareness. The programme delivers structured red-team methodology, evidence-based reporting, and compliance integration that security teams can apply from day one.
Production-grade red-team tooling
Learners operate Garak, Microsoft PyRIT, and promptfoo in structured campaigns the same tooling used in enterprise AI red-team engagements. This is LLM security training corporate teams need to run systematic assessments, not ad-hoc testing.
Agentic AI security depth
Agentic AI security training is embedded across Modules 7, 9, and the capstone covering tool-use attack surface, indirect prompt injection, capability scoping, and human-in-the-loop design patterns for production agentic systems.
Certification and career pathway
The programme prepares learners for the AI Cybersecurity Practitioner credential the leading AI security certification course India's enterprise sector recognises for security engineer, red team, and AppSec architect roles.
AI AppSec depth for engineering teams
AI AppSec training for security engineers covers output injection, supply-chain compromise, vector store poisoning, and unbounded consumption attacks threats that traditional AppSec programmes do not address.
A four-milestone path from skill gap to client-ready.
AI threat landscape and OWASP foundations
Establish a working threat model for AI systems mapping the full attack surface from foundation models through fine-tuning, RAG pipelines, agentic systems, and MCP servers, with an introduction to the OWASP LLM Top 10 v2025 framework.
OWASP LLM Top 10 mastery attack and defend
Learners work through all ten OWASP LLM categories prompt injection, sensitive disclosure, supply chain, data poisoning, output handling, excessive agency, system-prompt leakage, vector weaknesses, misinformation, and unbounded consumption with hands-on lab exploitation and structured defence for each.
Red-team tooling and campaign execution
Each learner runs a structured red-team campaign using Garak, Microsoft PyRIT, and promptfoo against a target AI application, producing a formal red-team report with demonstrated exploits and remediation recommendations.
Capstone production AI security assessment
Learner teams conduct a complete security assessment of a production-grade AI application, covering all 10 OWASP categories, with panel review by the NovelVista security practice and an invited industry CISO.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Trusted by Industry Leaders for Enterprise AI Upskilling
See why CEOs, CTOs, and business leaders collaborate with NovelVista
to discuss the future of AI, digital transformation, and workforce readiness.
- Exclusive AI leadership summits featuring enterprise decision-makers and technology experts
- Recognized corporate training partner for AI, Agile, DevOps, ITSM, and cybersecurity programs
- Trusted by organizations to build future-ready teams with practical, industry-focused learning
- Real conversations, real business challenges, and actionable AI transformation insights from industry leaders
Learn from domain experts with 15+ years of experience.
"AI transformation is not just about adopting new tools it’s about helping organizations build intelligent systems, scalable workflows, and future-ready teams that can innovate with confidence."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Security engineers and AI AppSec specialists building or assessing AI systems the core audience for this AI AppSec training for security engineers
- ·Penetration testers and red teamers extending their adversarial capability into LLM and agentic AI attack surfaces
- ·Security architects responsible for designing layered defences across model, application, infrastructure, and operational layers for AI systems
- ·AI engineers and senior developers building security-conscious AI applications who need structured OWASP LLM Top 10 training to harden their systems
- ·Enterprise security teams seeking structured AI cybersecurity training for enterprises operating AI in regulated sectors such as BFSI, healthcare, and technology
Pre-requisites
- ·A working background in application security, penetration testing, software engineering, or information security is strongly recommended
- ·Familiarity with traditional OWASP Top 10 concepts will accelerate learning in Modules 2 through 11
- ·Learners should be comfortable working in lab environments command-line tooling, API interaction, and basic scripting will be used throughout red-team labs
- ·Enterprise cohorts should identify representative AI applications or use cases in scope before the programme begins this is the leading AI security certification course India's enterprise security teams use for hands-on red-team capability building
Trusted by L&D leaders across the world.
"The lab-per-module structure made the difference. Our red team went from theoretical OWASP LLM awareness to hands-on exploitation and documented remediation across all 10 categories. This is the most practical OWASP LLM Top 10 training we have found."
"The agentic AI security modules were ahead of everything else available. Our architects came back with concrete capability scoping patterns, human-in-the-loop designs, and a formal assessment methodology for our agentic deployment pipeline."
"As an AppSec team lead, the compliance integration module gave us exactly what we needed a clear mapping between OWASP LLM controls and our ISO 27001 and ISO 42001 obligations. The capstone report became our internal AI security assessment template."
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.