If you're new to AI security, this is the page to start on. It covers what actually makes AI systems different to secure, the handful of attack types worth understanding first, and the core defenses — each with a direct link to the deeper guide once you're ready to go further.
Traditional cybersecurity was built for deterministic software: fixed logic, predictable workflows, the same input producing the same output every time. AI systems break that assumption. They're probabilistic, they adapt to context, and increasingly they're autonomous — capable of taking real actions rather than just generating text.
| Traditional Cybersecurity | AI Security |
|---|---|
| Code-based attacks | Language-based attacks |
| Deterministic systems | Probabilistic systems |
| Static behavior | Adaptive, context-dependent behavior |
| Fixed workflows | Dynamic reasoning |
An autonomous agent is an AI system that can plan, decide, and act — not just answer questions. Unlike a standard chatbot, an agent might have access to APIs, the ability to query a database, send an email, or coordinate with other agents. This is what turns a bad output from embarrassing into operational: a compromised chatbot says something wrong, a compromised agent does something wrong.
Manipulating an AI system's behavior through crafted input rather than a code exploit. This is the foundational AI-native attack — most other categories below build on the same underlying mechanism.
Planting malicious instructions in documents or data sources an AI system will later retrieve, so the system treats attacker content as trusted context.
Manipulating an agent into misusing the real capabilities it was granted — sending unauthorized emails, modifying records, calling APIs it shouldn't.
The single biggest amplifier of real-world damage. An agent can only do as much harm as its permissions allow, regardless of how it was manipulated.
Apply least-privilege access by default — scope every API and tool grant to exactly what the task requires, nothing more.
Treat any document or data source an AI system reads from as untrusted until it's been checked, the same way you'd treat external user input.
Capture inputs, outputs, and tool calls from day one — you can't investigate an incident you never recorded.
Anything that sends money, deletes data, or modifies records outside a sandbox should have a human checkpoint until the system has earned trust.
Run basic adversarial testing before launch — you don't need a full red team on day one, but you do need to know the obvious injection attempts fail.
The HexTyx AI Security Assessment gives you a clear baseline read on your system, whether you're just getting started or already in production.
Once the fundamentals above feel solid, the natural next step depends on what you're building. If you're working with retrieval or a knowledge base, go deeper on RAG security. If your system can take real actions, go deeper on agent attack simulation and runtime governance. If you're preparing for a launch, the pre-deployment audit framework and production checklist are the right places to go from here.