OSAI Certification: Is This the Most Practical AI Security Credential You Can Earn Right Now?

Let me be direct with you. If you are a penetration tester or red team operator and you have not started learning AI security yet, you are already behind.

That is not meant to create panic it is just the reality of where the job market is heading. The companies your clients are running? They are deploying AI chatbots, internal LLM tools, AI agents that make decisions, RAG-based knowledge systems. And when they ask their security vendors to include those systems in the next engagement scope, most pen testers have no idea what to do with them.

That gap is exactly what the OSAI certification trains you to close.

What Actually is OSAI?

OSAI stands for Offensive Security Artificial Intelligence. It is Securium Academy's hands-on AI red teaming certification a practical credential built for security professionals who already understand traditional penetration testing and want to extend those skills into AI systems.

Unlike a lot of AI security certifications that are mostly theory and multiple-choice questions, OSAI ends with a 24-hour practical exam. You are given access to a simulated enterprise environment that includes deployed LLM applications, RAG pipelines, multi-agent workflows, and cloud-hosted AI services. Your job is to find and exploit real vulnerabilities in that environment, then write a professional report on what you found. You either demonstrate the skill or you do not. There is no way to pass by guessing.

That format is what sets it apart. We built it this way deliberately, because when a client hires you to test their AI systems, they need to know you can actually do it not just that you watched a few videos and passed a quiz.

Why AI Red Teaming Is Different From What You Already Know

Here is something that trips up experienced pen testers when they first start working with AI systems.

With a web application, you can look at the code. You can trace what happens when a specific input hits a specific function. The system behaves predictably. You build a mental model of it and you find the holes.

An LLM does not work like that. The same prompt can produce ten different outputs on ten different attempts. The attack surface is not just the code it is the instructions the model was given (which are often hidden), the data it was trained on (which you cannot read), the tools it has permission to call (which might include sending emails or writing to databases), and the way it retrieves information from external sources. Every one of those dimensions is an attack vector, and none of them look like anything in the traditional pen testing playbook.

Prompt injection alone just one of the attack categories in the OSAI curriculum has already been used to extract confidential system prompts from enterprise chatbots, to make AI agents take actions their owners never intended, and to leak documents from RAG pipelines that were supposed to be access-controlled. And most organisations deploying AI right now have no one qualified to test for these vulnerabilities.

What the OSAI Course Covers — Module by Module

The course runs across 8 modules and 80+ hours of content. Here is what you actually learn:

Module 1 — AI Systems from an Attacker's Perspective

Before you can attack something effectively, you need to understand how it works. This module covers LLM architecture, how inference pipelines operate, and how enterprise AI systems are typically structured all from the perspective of someone looking for weaknesses. You also learn the MITRE ATLAS framework here, which is the AI equivalent of MITRE ATT&CK. This gives you a structured vocabulary for documenting what you find in client reports.

Module 2 — Prompt Injection: All the Ways It Works

This is the big one. Prompt injection is the OWASP LLM Top 10's number one vulnerability and it is vastly more nuanced than most people realise. Direct injection is the obvious version you manipulate the conversation to override the model's instructions. But indirect injection is where it gets interesting: you poison content that the AI will later retrieve (a web page, a document, a database entry), and when the model processes that content, your malicious instructions execute. We cover both in the labs, along with multi-turn attacks that persist across conversation sessions and techniques for reconstructing hidden system prompts.

Module 3 — RAG Pipelines: The Most Underestimated Attack Surface

RAG Retrieval-Augmented Generation is how enterprise AI applications give models access to company knowledge. You query the model, it retrieves relevant documents from a vector database, and includes them in its response. This architecture has been deployed at scale in banking, legal, healthcare, and government systems, often with sensitive information behind it. In this module you learn how to probe RAG configurations, enumerate vector databases, craft adversarial queries that surface documents you were never supposed to see, and exploit the cross-user data leakage that poorly configured RAG systems allow.

Modules 4 through 8 — From LLM Data Extraction to the Exam

The remaining modules cover adversarial machine learning attacks on model training data, multi-agent system exploitation (this is the most complex and most exciting content in the entire course attacking AI agents that can call tools, write code, and make decisions autonomously), AI model API security testing on AWS Bedrock and Azure OpenAI, professional engagement methodology, and a full mock exam with trainer feedback on your report. The feedback session alone is worth it knowing exactly what a professional AI red team report should look like before the real exam makes a significant difference.

The 24-Hour Exam: What to Expect

The exam is accessed via VPN. You get 24 hours of active testing time against a realistic simulated enterprise environment, then 24 hours to submit your report. The environment has been designed to reflect how real organisations actually deploy AI: it is not a collection of obvious training exercises, it is a system that a reasonably security-conscious engineering team built without specifically thinking about offensive AI testing.

The report is assessed as seriously as the technical findings. We look at how you classified the risk of each vulnerability, whether your remediation recommendations are actionable, and whether a non-technical stakeholder could read your executive summary and understand the business impact. These report writing skills are what turn a good security tester into a trusted security consultant and they are what clients pay premium rates for.

Who Should Actually Take This Course?

Be honest with yourself here. This is not a course for someone who has never done a penetration test. The labs assume you already know what a reverse shell is, that you can navigate a Linux terminal comfortably, and that you have some experience writing professional security reports.

If you hold a CEH or OSCP and want a specialisation that opens doors traditional certs cannot this is it. If you run a red team and your clients are asking about AI security testing  this is the credential that lets you say yes with confidence. If you are a security engineer who wants to understand AI attack surfaces from the attacker's side  the defensive insight you gain from this training is genuinely irreplaceable.

Salary data for AI red teaming roles in India is still forming, but the directional trend is clear: ₹12-30 LPA at mid-senior level for AI security specialists in 2026, with consulting rates running significantly higher. More importantly, the supply of certified AI red teamers is tiny right now. That balance shifts as more people train which is exactly why the timing matters.

Connect with Securium Academy today:  🌐 Visit: www.securiumacademy.com
www.securiumacademy.com  Max 15 students per batch