A Step-by-Step Plan To Master AI Security in 2025

How to Become an AI Security Expert by the End of 2025

If you work in Cybersecurity then AI Security could be one of the most important skills to acquire in 2025 and beyond

Whether you believe the hype or not .. you cannot deny just how big of an impact AI is having on nearly every industry across the world

Gartner predicts that more than 80% of enterprises will be using GenAI apps in one way or another by 2026

As AI gets closer and closer to corporate data .. new risks are being introduced that most companies are not ready for

Not to mention new regulations like the EI AI Act that require companies to put in security controls for higher risk AI systems

Professionals who are able to learn and practice this skill will be in hot demand

But where to start ??

In this article I want to go over five easy steps you can use to learn AI security and get a running start on this upcoming field

Step 1 : Understand Machine Learning

To secure AI .. you must first understand how it works

Machine Learning (ML) is the engine that drives most AI implementations and it is essential as a starting point

Understand the core concepts of ML and how it differs from normal applications.

I would suggest getting a firm understanding of the below topics:

• Supervised and unsupervised learning

• Neural networks and deep learning

• Reinforcement learning

• Feature engineering

• Model evaluation and validation

You do not have to become an expert or get into the nitty gritties of the different types of machine learning algorithms ..

BUT you should have a firm understanding of the concepts that ML is based around

The reason being that a lot of the attacks on AI seek to exploit these very concepts !

Step 2: Learn About Biases in AI systems

One of most dangerous risks in AI systems is for biases to get introduced leading to unfair or discriminatory decisions.

Think about an AI system being used in law enforcement or the medical field that is biased towards a particular ethnicity or race ?!

To become an expert in AI security, you must know how to identify and mitigate these biases.

Here’s what to study:

• Types of biases (e.g., sampling bias, measurement bias, and algorithmic bias)

• Ethical implications of biased AI systems

• Techniques for mitigating biases (e.g., re-sampling, re-weighting, and adversarial training)

Step 3: Learn About AI Specific Attacks

The third step is learning about AI-specific attacks.

As AI systems become more prevalent, they become targets for malicious actors.

To protect these systems, you need to understand the various attacks and their implications.

Some common attacks include:

• Adversarial examples

• Data poisoning

• Model inversion and extraction

• Membership inference attacks

Step 4: Learn AI Risk Management

AI security does not exist in a vacuum and needs a proper framework to be implemented to function properly.

Find out about the new regulations which are being introduced to regulate AI and what sort of controls they require.

Learn these topics:

• AI Governance frameworks and best practices

• AI Risk assessment methodologies

• Threat Modeling AI systems

• Compliance with relevant laws and regulations

• Incident response and recovery plans for AI systems

I would recommend the following frameworks to get a more comprehensive knowledge about this topic

1 — NIST AI Risk Management Framework

The NIST Cybersecurity Framework has become an industry benchmark companies use to assess their security posture against best practices.

The NIST AI Risk Management Framework (RMF) is going to be the equivalent for AI Risks.

It is a tech-agnostic guidance developed to help companies design, develop, deploy, and use AI technologies responsibly.

NIST frameworks are well-trusted within the industry due to the rigorous validation they undergo from experts all across the globe

This framework is an excellent starting point for people, regardless of their technical background.

NIST AI RMF

2 — AWS GenAI Security Scoping Matrix

If securing GenAI applications is your interest then I cannot recommend this framework enough

I admit to being a bit biased as I currently work in AWS but I honestly believe the AWS GenAI Security Scoping Matrix is one of the best resources around

This three-part series helps you understand the different ways of assessing Generative AI risk and how they change depending on the model your company chooses

The concepts are not just restricted to AWS and can be applied to any provider

Highly recommended for those wanting to deep-dive into GenAI risks !

Step 5: Learn about AI specific security controls

Now that you have a firm understanding of the basics of AI, you can start to learn about AI specific security controls

Most cybersecurity professionals make the mistake of jumping to this step directly instead of creating a solid foundation first !

A few of the key topics to learn are:

• Data protection (e.g., encryption, anonymization, and access controls)

• Secure model training and deployment

• Robustness testing and validation

• Monitoring and auditing of AI systems

Two great resources on this are the MITRE ATLAS framework and the OWASP Top 10

1 — MITRE ATLAS Framework

The previous frameworks I highlighted are great, but they can be too high-level for someone who likes to dive deep into the technicalities of AI attacks.

This is where ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) comes in.

As per their website

ATLAS is a globally accessible, living knowledge base of adversary tactics and techniques against Al-enabled systems based on real-world attack observations and realistic demonstrations from Al red teams and security groups.

As the diagram below shows, ATLAS demonstrates how attackers can compromise AI at each stage and what techniques are used.

An excellent resource if you want to become an AI pent-tester!

MITRE ATLAS Framework

2 — OWASP Top 10 For Large Language Models

The OWASP Top 10 is another industry benchmark for web application security.

So it was no surprise when they released their top 10, this time focusing on Large Language Model Applications

As per OWASP

The OWASP Top 10 for Large Language Model Applications project aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing Large Language Models (LLMs).

Similar to their previous top 10s, this document lists the most critical vulnerabilities found in LLM applications.

It shows their impacts, how easy it is to exploit, and real-world examples

If you are a CISO or have security leadership responsibilities, it also comes with a great companion piece, the LLM Security & Governance Checklist.

The checklist helps you understand how to assess AI risks and implement an oversight program to mitigate them

There you have it .. follow these steps and you will have an intermediate to expert understanding of AI Security in 2025.

If you want to dive deeper then check our my courses on AI Security and Risk Management on Udemy.

Good luck on your journey ! Check out my video on this also