Introduction
Last week, when I was analyzing an AI agent deployed on the server, I noticed that it was connecting to a MCP server using the credentials defined in the environment file. Since the environment file had credentials stored in it, it was not protected from unauthorized access and this misconfiguration/lack of security would have compromised the AI agent. So, I felt there is a huge gap in the market for AI Security Engineers and AI security skills to understand these nuances of AI related infrastructure and configurations.
That’s why AI is no longer just a productivity tool — it is now critical infrastructure. Banks, hospitals, and governments are deploying AI systems that make real decisions. And yet, most security teams have never been trained to secure them.
The good news? This skill gap is your opportunity. Companies are urgently hiring people who understand both AI systems and security methods — and they are paying a premium for it. In this article I will walk you through the 10 AI security skills that are genuinely in demand right now — the ones I cover in depth in my AI Security Engineer course on Udemy, and the ones you need to stand out in 2026
Why AI Security Is the Skill of 2026
Consider these numbers:
• 40% of all cyberattacks now use AI to find hidden weaknesses
• The average US salary for an AI Security Engineer is $152,000/year
• There are 3.5 million unfilled cybersecurity positions globally — and AI security roles are the hardest to fill
In my DevSecOps and AI Security courses on Udemy, I am asked this question, why shall we learn AI security and what is the need of AI security?
The problem is that traditional security training does not cover AI-specific threats like prompt injection, model poisoning, or adversarial inputs. That is the gap we are filling today.
The 10 Skills
1. Understanding LLM Architecture and Attack Surfaces
Before you can defend an AI system, you need to understand how it works. Large Language Models (LLMs) process input in ways that are fundamentally different from traditional software — and that creates attack surfaces traditional security training never covered.
Key concepts to learn: tokenisation, context windows, system prompts, RAG (Retrieval-Augmented Generation) pipelines, and how models are fine-tuned.
The first module of my AI Security Engineer course spends time here because I have seen too many security engineers try to protect an AI system they do not fully understand. It is like trying to patch an application without knowing what language it is written in.
2. Prompt Injection Defence
Prompt injection is the #1 AI-specific vulnerability right now. It is the OWASP LLM Top 10’s most critical issue — and most developers have never heard of it.
An attacker crafts an input that overrides the system prompt or hijacks the model’s behaviour. The consequences range from data leakage to full system compromise.
• Direct prompt injection: user manipulates the model directly
• Indirect prompt injection: malicious instructions hidden in external content the model reads
We will see a case study in AI Security Engineer course that explains how a chatbot in Bing browser was exploited using Prompt Injection in real life.
3. OWASP LLM Top 10
OWASP released a dedicated Top 10 for LLM applications, and it is quickly becoming the industry standard for AI security assessments. Every AI security engineer needs to know it cold.
The list covers: Prompt Injection, Insecure Output Handling, Training Data Poisoning, Model Denial of Service, Supply Chain Vulnerabilities, Sensitive Information Disclosure, Insecure Plugin Design, Excessive Agency, Overreliance, and Model Theft.
4. Adversarial Machine Learning
Adversarial ML is the discipline of understanding how AI models can be fooled, manipulated, or stolen. It is the AI equivalent of penetration testing.
Core attack types to understand:
• Evasion attacks: crafting inputs that fool a model into misclassifying
• Poisoning attacks: corrupting the training data to influence model behaviour
• Model extraction: reverse-engineering a model’s behaviour through queries
• Membership inference: determining whether specific data was in the training set
We will see a case study in AI Security Engineer course that explains Adversarial attacks in AI
5. MITRE ATLAS Framework
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is the AI equivalent of MITRE ATT&CK. It documents real-world adversarial ML techniques, tactics, and case studies.
If you know ATT&CK, ATLAS will feel familiar — but it introduces AI-specific tactics like Model Evasion, ML Supply Chain Compromise, and Craft Adversarial Data that have no equivalent in traditional cybersecurity.
We will learn about MITRE ATLAS framework in AI Security Engineer course that explains how this framework is used in real life for AI systems.
6. Securing RAG Systems and Vector Databases
RAG (Retrieval-Augmented Generation) is how most enterprise AI applications work — the model queries an external knowledge base to answer questions. This introduces a whole new attack surface that most security teams are completely blind to.
Attack scenarios to understand: data poisoning in the vector store, indirect prompt injection via retrieved documents, sensitive data leakage through context injection, and unauthorised knowledge base access.
7. AI Application Security Testing
Traditional DAST and SAST tools do not understand AI-specific vulnerabilities. AI security engineers need a new toolkit for testing LLM applications.
Key skills: manual prompt injection testing, automated fuzzing of LLM inputs, output validation testing, and using tools like Garak (LLM vulnerability scanner) and PyRIT (Python Risk Identification Toolkit from Microsoft).
We will learn about AI AST in detail in AI Security Engineer course.
8. AI Supply Chain Security
Most AI applications are built on top of pre-trained models, open-source libraries, and third-party datasets — any of which could be compromised. This is the AI equivalent of the SolarWinds problem.
Key risks: malicious models on Hugging Face or similar platforms, compromised Python packages in the AI/ML ecosystem, poisoned training datasets, and insecure model serving infrastructure.
We will learn about AI Supply chain security with case study in AI Security Engineer course. ‘The 2023 PyTorch supply chain incident — where a malicious package was uploaded to PyPI — was a wake-up call. Most teams building AI applications have never audited their model dependencies the way they audit code dependencies.’ For latest CVEs, checkout https://asecurityguru.com/tools/cve-tracker.html
9. AI Governance and Compliance Basics
Even if you are a deeply technical engineer, you need to understand the regulatory landscape. The EU AI Act, NIST AI RMF, and ISO 42001 are shaping how organisations must document, test, and govern their AI systems.
You do not need to be a compliance expert — but you need to speak the language, understand what high-risk AI systems require, and know how to feed your security findings into a governance framework.
We will learn about Governance frameworks that help us to build a safe AI system.
10. Secure AI Development Lifecycle (Secure AI SDLC)
The final skill is putting it all together. A Secure AI SDLC means integrating security at every stage of an AI system’s life — from data collection and model training to deployment and monitoring.
Key practices: threat modelling for AI systems, security reviews at model selection stage, adversarial testing before deployment, continuous monitoring for model drift and attacks, and incident response planning for AI failures.
After years in DevSecOps, I have learned that security bolted on at the end never works — for code or for AI. The engineers who get this right are the ones who shift left, and this module shows them exactly how.
How to Learn These Skills
There are a few paths depending on where you are starting from:
If you are a complete beginner:
Start with the fundamentals of how LLMs work, then layer in security concepts. You do not need a formal security background — you need curiosity and hands-on practice.
If you are an existing security or IT professional:
Your threat modelling and risk thinking transfers directly. Focus on learning the AI-specific attack surface — OWASP LLM Top 10 and MITRE ATLAS are your best starting points.
If you are a developer or DevSecOps engineer:
You already understand pipelines and deployment. Learn to threat model your AI components and add security testing to your existing workflows.
I built my AI Security Engineer course on Udemy to work for all three of these starting points. The early modules build the foundation, and the course progressively moves into hands-on labs and real-world scenarios regardless of your background.
🎓 Want to master all 10 of these skills in one place? My Udemy course AI Security Engineer covers every skill on this list with hands-on labs, real-world attack scenarios, and practical defence techniques — from complete beginners to DevSecOps engineers.
Final Thoughts
The 10 skills above are not a wish list — they are what the market is actively hiring for right now. AI security is at the same inflection point that cloud security was in 2015: the window to get in early is open, but it will not stay open forever.
I have spent 20 years in security and the pace of change I am seeing around AI is unlike anything before it. The engineers who take this seriously today will be the ones leading security teams in three years.
Pick one skill from the list above and go deep on it this week. Then come back for the next one. Consistency beats intensity every time.
If you found this useful, share it with a fellow engineer — and drop your questions in the comments. I read every one.
About the Author
Raghu the Security Expert has 20 years of experience in Security, DevSecOps, AI Security, and Penetration Testing. He has helped 80,000+ students upskill themselves in DevSecOps, Application Security, AI Security. Currently, he is running A Security Guru and creates security content on social media platforms. Follow and review his work on linkedin, Youtube and Udemy.
