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How to become an AI Security Engineer

Prompt injection defense, model supply chain, data leakage, and access control. Secure AI systems end to end.

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The AI Security Engineer roadmap

Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.

Phase 1AI security foundations

The LLM threat model

AI security starts from one fact traditional web security misses: a model follows instructions in its input, so any untrusted text - user, document, or web page - can act as code.

OWASP LLM Top 10

The OWASP Top 10 for LLM Applications is the field's shared map of what goes wrong: prompt injection, insecure output handling, sensitive data disclosure, excessive agency, supply chain, and more.

Trust boundaries and untrusted input

Every AI system has a boundary between trusted instructions and untrusted content - user input, retrieved documents, tool output.

Phase 2Prompt and input attacks

Direct prompt injection

The most common LLM attack: a user embeds instructions that override your system prompt.

Indirect (second-order) injection

The dangerous cousin of direct injection: the payload hides in content the model later reads - a wiki page, an email, a web result - and fires when retrieved.

Jailbreaks and guardrail bypass

Attackers rephrase endlessly to slip past a forbidden-content rule.

Phase 3Tool and execution security

Least-privilege tool scoping

Excessive agency turns one injection into total compromise.

Sandboxing execution

When an agent runs generated code or calls powerful tools, isolation is the control.

Multi-tenant data isolation

In a shared system, one tenant's data must never reach another's answers.

Phase 4AI security data protection

Sensitive data in prompts and logs

Prompts and their logs are a data store.

Output filtering and exfiltration

A model can be induced to smuggle secrets into a URL or image that phones home when rendered.

Model and plugin supply chain

Third-party models and plugins run with your app's trust and access, and public registries have shipped compromised components before.

Phase 5Detection and response

Red-teaming AI systems

Functional tests check intended behavior; they say nothing about resistance to an attacker.

Monitoring and abuse detection

You cannot respond to what you cannot see.

Incident response for AI

When a flaw is actively leaking, the first move is containment - disable the vulnerable capability to stop the bleeding - then investigate scope and remediate.

Reading for this path

The primary sources behind the topics above, all free to read.

Coming from another job?

Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.

AI Solutions Architect

System design, model selection, build-vs-buy, and scaling and cost. Architect enterprise AI solutions that hold up in production.

AI Strategist

Opportunity sizing, build-vs-buy, and adoption strategy. Decide where AI creates real business value.

AI Systems Engineer

GPUs, distributed training, kernels, and memory and throughput optimization. Make large models train and serve fast.

AI UX Designer

Human-AI interaction patterns, trust, uncertainty, and feedback loops. Design interfaces where people and models work well together.

Analytics Engineer

Data modeling, transformation pipelines, metrics, and data quality. Turn raw data into trusted datasets teams can build on.

Applied Scientist

Model adaptation, evaluation, and prototyping against real product problems. Take research from paper to production.

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