Cybersecurity
Your comprehensive resource for AI news, tutorials, and cybersecurity insights. Master AI tools and concepts with a focus on security applications.
Security, explained for people shipping AI
Not abstract theory. The failure modes that actually show up once a model touches production data, customers, or money.
Prompt injection
Why untrusted text can hijack an agent, and why "just tell the model to ignore it" is not a defense. Practical containment patterns.
Data leakage
How context windows, embeddings, and logs quietly exfiltrate what you never meant to share — and where to put the boundary.
Model supply chain
Weights, adapters, and packages are dependencies too. Provenance, pinning, and what to verify before you load someone's checkpoint.
Access & identity
Scoping an agent's credentials to the blast radius you can live with. Least privilege, short-lived tokens, and human-in-the-loop gates.
Detection & response
What to log when the actor is a model, how to reconstruct a session after the fact, and what a useful AI incident postmortem contains.
Red teaming
Adversarial testing that goes past a jailbreak checklist. Building evals that fail loudly before your users find the hole.
Ship the model without widening the attack surface
Most AI incidents aren't exotic. They're an over-permissioned integration, an unreviewed tool call, or a log file holding something it shouldn't. The fixes are unglamorous and they work.
- ✓ Treat every model output as untrusted input to the next system
- ✓ Scope tool credentials to the smallest workable blast radius
- ✓ Keep a human gate on irreversible and outward-facing actions
- ✓ Log the full decision trail, redact the payload
- ✓ Test adversarially before launch, not after the disclosure email
Start where you actually are
Four tracks, ordered. Each assumes only what came before it.
AI fundamentals
Tokens, context windows, embeddings, and what a model can and cannot know about your data.
Tooling and agents
Function calling, retrieval, and the moment your model stops answering and starts acting.
Threat modeling for AI systems
Map trust boundaries, enumerate what an attacker controls, and decide what you refuse to automate.
Adversarial evaluation
Build a red-team suite that runs on every change, and read its failures honestly.
Adopt AI without inheriting its risks
Tell us what you're building and where the data flows. We'll tell you what we'd harden first.