AI Systems

Embedding store, retriever, generator. Where prompt injection hides.

Every model call goes through one door. That door is either a real control point or theater.

Layered defenses between untrusted input and a model that will follow whatever instructions it’s given.

An agent that can act needs a cage, not just good intentions.

Embeddings feel abstract until they leak the exact document they were made from.

You trust the code you review. Do you review the weights you download?

Attacking your own model on a schedule, before someone else does it off one.

Voice authentication was never meant to survive a model that can clone anyone in seconds.

It suggests. It never clicks the button. That distinction is the entire design.

A scanner that can also decide a finding is real, not just possible — for better and worse.

The model becomes whatever you feed it. This is what actually gets fed.

The unglamorous plumbing that decides whether your model answers in 200ms or times out.

A model that was accurate at launch and never checked again is a liability with a launch date on it.

Coordinator + workers, tool calls, authority delegation, blast radius.

Real-time scoring, feature store, model retraining loop.

Repo access, secrets handling, write-permission boundaries.

Streaming features, alert fatigue, model drift.

Multi-modal pipeline, human-in-the-loop, appeal flow.

Video/audio classifier, evidence chain, false-positive cost.

Embeddings, ranking, exposure auditing.

Image, text, and audio meet in one fused representation — where they disagree is where attacks live.

The model will happily suggest a hardcoded secret. Something has to catch it before a human copies it.

A chatbot doesn’t need malicious intent to leak PII — it just needs to be asked the right way.

Three independent checks on the same email, because attackers only need one to fail.

A signal embedded at generation time, checked long after the content has left your control.

Enough queries, patiently asked, can reconstruct a model you spent millions training.

A pixel-level perturbation invisible to a human can flip a model’s answer entirely.

Every model an organization actually uses, in one place, with someone accountable for each one.