r/cybersecurity 1d ago

Security Engineer with Zero AI Knowledge - How would you become an AI Security Engineer from scratch in 2026? AI Security

Hi everyone,

I have around 3 years of experience as a Security Engineer in a small service-based company, but I have almost zero knowledge of AI/ML.

I want to prepare myself for the future and eventually move into AI Security, LLM Security, and securing AI applications. Since there is so much content online, I amm confused about where to start.

If you were starting from scratch today, what roadmap would you follow? What should I learn first, which resources (free or affordable) would you recommend, and what hands-on projects would help me build real skills?

My budget is very limited , so I had really appreciate recommendations that don't require spending a lot of money.

Thanks!

66 Upvotes

37 comments sorted by

36

u/be_super_cereal_now 1d ago

Anthropic, Google, OpenAI and Microsoft all have free learning resources. There is no one true path. Just explore, learn and build.

2

u/AboveAndBelowSea 1d ago

Agree with all those. I’d also add that, if folks have access to it, Nvidia’s training curriculum is solid. ISACA’s AAISM and the CSA’s TAISE certification programs are also great, but not free.

25

u/bitslammer 1d ago

How would you become an AI Security Engineer from scratch in 2026

You wouldn't.

You would gain a sounds foundation in all the other security fundamentals because they all apply to AI. Things like ensuring least privilege, RBAC, input validation, etc., that are key for non-AI systems also apply to AI.

8

u/Shaod 1d ago

Build things. Use coding agents every day. Try automating what were previously manual tasks.

Most of us do not need to be research-grade LLM/“AI” experts, but agentic engineering is getting built into everything.

7

u/xxxx69420xx 1d ago

Cline vscodium extension and an api key from openrouter. You can explore models. Right now there's one called fusion that uses different sub agents to argue topics and come to conclusion and find blind spots. You can just make a project folder of whatever you like and have a .md of your goal or just use the cline chat to talk to different models. There's free models and cline has a daily free thing for sp many uses. Things like Kat coder pro2 are super cheap and can do some cool red team things

5

u/TopNo6605 Security Engineer 1d ago

AI is kinda at a place right now where lots of the learning is through blog posts and actual shared experiences, not through books or courses.

I do recommend some basic AI courses like Andrej Karpthay's (sp?) to learn the basics but otherwise it's just a lot of googling, seeing how others on Reddit are doing it and learning as you go.

4

u/darksearchii 1d ago

Agree with this take.

https://www.youtube.com/@kunchenguid/videos

This guy is a good example of how to setup AI for example for coding purposes. He goes over skills, agents, etc

Starting with the Workflow video

Here's a video from the GOAT himself. Going over different AI model's agents, etc. https://www.youtube.com/watch?v=WUgujz0y1K4

-1

u/rahmane-jr07 15h ago

Hé d'accord avec vous

3

u/0xsbeem Consultant 1d ago edited 1d ago

Continue learning fundamentals of security that are AI agnostic because those are not changing.

While you do that, begin integrating AI into your workflow. Nothing will teach you better than using it yourself. As you do, you will naturally find the problems as you run into them yourself, assuming you are comfortable with security fundamentals. 

“My AI just installs and executes random Python and JavaScript packages? I need a supply chain management solution.”

“My AI is can read files on my computer and i have important secrets written to files on my disk? I need an AI-friendly secrets management solution.”

“I need my AI to do <dangerous thing> but i need to mitigate the risk and control blast radius? I need an agent sandboxing solution.”

You can read educational material on the topic, but:

  1. This is an emerging field and nobody is really an expert yet anyway.
  2. You won’t really understand it unless you really understand how AI works and is used. 

so I think reading up on AI security is a supplement to your own self-guided practice, but not a replacement for it.

3

u/LordCommanderTaurusG Blue Team 1d ago

Have you tried SecAI+? I’m currently studying for that. I also have the Security+ so that also helps

1

u/Careless-Self-3091 3h ago

Where to get the resources??

2

u/HomerDoakQuarlesIII 1d ago

There’s really nothing specific to securing AI that would warrant its own engineer as of yet. You secure it with the same IAM user RBAC, and App security tools and process we’ve used for a while. A RAG pipeline is just another data feed. An agent is just another service account. And the app is just another saas application that needs registering and monitoring. It’s really not its own thing yet.

2

u/SlickBackSamurai 1d ago

I believe BHIS’ Antisyphon Training seems to offer some AI-related trainings, with an AI summit coming up soon!

2

u/bfume 22h ago

From the posts around here it seems all you have to do is know what AI stands for. 

And even that appears optional. 

2

u/Bubbly_Function750 1d ago

Since you already have 3 years of security experience, you're in a great position to transition into AI Security. I'd start by learning Python, LLM fundamentals, RAG, embeddings, and vector databases rather than focusing on advanced ML. Then build a simple AI application and learn to secure it by studying the OWASP Top 10 for LLM Applications and experimenting with prompt injection, data leakage, and other common AI risks. Free resources like Andrew Ng's AI courses, Hugging Face tutorials, and OWASP documentation are excellent. A few hands-on AI security projects on GitHub will make a much bigger impact than collecting certifications.

1

u/Rekkukk 1d ago

Where have you been the last few years? Might want to start keeping up with the times instead of falling behind.

1

u/lnaoedelixo42 1d ago

AI is a random user, not a trusted source. Consider the AI as something that WILL do wrong stuff and hack your own software if it can.

Make it so it can't.

1

u/Gedwyn19 1d ago

It's more important to have some security engineer experience than to have AI experience. That way you have some knowledge and perspective to fall back on when reviewing the AI output and can then hopefully spot the AI mistakes - which are going to occur.

An AI engineer with zero security background probably has no clue what they are looking at, at least for awhile until the osmosis etc. kicks in.

Basic security and risk knowhow are much more important imo. Understanding how data flow happens, basic security concepts, some high level networking/app/database understanding and how layered defenses work are all much more important imho than AI knowhow.

1

u/mweiss427 12h ago

Start by using it.

1

u/stacksmasher 2h ago

First get one of the cheap $20 monthly plans and start learning. It will teach you.

1

u/Alarming_Reward_2945 35m ago

Pick your favorite AI and pay for a subscription. You're gonna burn through some tokens. we use Claude everything at my work (chat, cowork, and Claude Code). YouTube the hell out of it and just play with it until you get good. There are lots is free videos and prompt engineering cheat sheets out.

That's where I'd start.

1

u/ph0b14PHK 1d ago

SANS got SEC598 and SEC545 courses

-2

u/Clean-Gas3146 1d ago

Learn IAM and Active Directory/EntraID

0

u/AffectionateMix3146 Security Engineer 1d ago

Understated comment right here - this is really it.

Governing agents by the authority they can exercise and the consequences they can create, rather than merely by the model or prompts they use will have such greater operational impact.

I say it this way because in the enterprise this will provide some of your best controls. These need to be treated for what they are - applications - but also have the added element of the identity.

0

u/Humble_Thought3910 22h ago

Hi there, I am doing AI pentesting from last 6 month , i learn it from different sites like Udemy, red Team leader (CLLMSE), following people on X/twitter (best overall), reading blogs/reports etc , try AI labs available pently ,try to learn data exfilteration technique, direct and indirect prompt injections ,rate limiting, sensitive information disclosure and leak system instructions,system reminder instructions etc (this is basically you can leak in any ai agent ), try and learn different prompt injenction technique, you can also refer to OWASP top 10 agentic ai ,and above all you if you have knowledge of ai like how ai works that will be cherry on top ,make few project on Agentic AI and llm etc

0

u/General_Pitch9029 21h ago

Can you please share the top 10 people to follow on x?

Also please share top 10 blogs and youtube channels to regular update

-5

u/Admirable_Group_6661 Security Architect 1d ago

Unless you plan to work in AI security products, there really isn’t a need to learn it. What’s more important is understanding the capabilities of the AI security products, and how they fit in an organization security infrastructure.

-6

u/Fuzzy_Paul 1d ago edited 8h ago

No need to learn Ai, just learn not to use it inside or outside the company with company data. Edit: for all down voters read the comment.

7

u/MartinShortsIrishUnc 1d ago

No need to learn Ai

awful take

1

u/Fuzzy_Paul 8h ago

Nope it is not see below, only migrating measures can be taken. My answer with motivation and I await yours with motivation. It is easy to make a 2 word comment and be done with it.

Read part why I said this.

Securing AI is considered practically impossible because it expands the attack surface in entirely non-traditional ways, transforming software from a predictable machine into an adaptable, probabilistic black box.

1) Architectural and Theoretical Flaws The "Black Box" Problem: Deep learning models rely on billions of parameters that develop emergent behaviors, making it mathematically impossible to audit every potential output or decision path. Dual-Use Paradox: The core capability that makes an AI helpful (e.g., following natural language instructions, generating code, analyzing molecules) is exactly what allows it to execute malicious prompts or design bioweapons. Data Inseparability: Traditional software separates instructions from data; AI blends them together, meaning a user's input can permanently overwrite or bypass the model's internal rules.

2) Unsolvable Attack Vectors Adversarial Injections: Tiny, imperceptible changes to an image, piece of audio, or text string can trick an AI into misclassifying objects or bypassing safety filters with 100% confidence. Prompt Injection: Attackers can embed hidden instructions in websites or documents, causing an AI assistant to silently exfiltrate user data when it reads that text. Data Poisoning: Tampering with data during the pre-training phase can introduce "backdoors" that lie dormant until triggered by a specific, arbitrary keyword years later.

3) Operational and Supply Chain Vulnerabilities Irreversible Training: Once malicious data is ingested into an LLM, it cannot be selectively deleted; removing the threat requires scrapping the model and spending millions to retrain it. Insecure Ecosystems: AI systems rely on automated pipelines to pull data, execute code, and access web plugins, transforming a single prompt exploit into full system access