AI Security: What Every Builder Must Know Before They Ship
Join Nahid Farady, PhD (Principal Tech Lead, AI Security · Microsoft) for a free live session.
Nahid Farady, PhD
Principal Tech Lead, AI Security · Microsoft
⭐ 4.9 / 5
You're one prompt injection away from a breach. Most AI teams don't know it yet.
Traditional security training doesn't cover LLM attack surfaces. Prompt injection, indirect RAG poisoning, jailbreaks, data leakage through model outputs — these are live threats in production AI systems right now. In 4 weeks, you'll learn to map, harden, and defend AI systems the way the top 1% of security practitioners do — and make justified ship/hold decisions backed by real risk analysis.
What You'll Learn
Map Your AI Attack Surface
Identify prompt injection, jailbreaks, indirect RAG poisoning, and data leakage vectors specific to your architecture — not generic checklists.
Harden Inputs & Prompts
Build layered defenses: input validation, system prompt hardening, and semantic classifiers. NOT single-layer string matching that's trivially bypassed.
Control Outputs with Guardrails
Implement PII detection, hallucination checks, and content filters. Evaluate real tools (Guardrails AI, NeMo) on precision vs. latency tradeoffs.
Ship/Hold Decision Framework
Run a structured AI security audit and make a justified ship/hold call with documented cost vs. risk tradeoffs your stakeholders can act on.
Who Is This For?
This sprint is designed for:
Security Engineers
Moving into AI systems who need to understand LLM-specific attack surfaces that traditional AppSec training doesn't cover.
AI Engineers & Builders
Shipping LLM apps, RAG pipelines, and agents who need to stop treating security as someone else's problem.
AI PMs & Tech Leads
Making ship/hold calls on AI features who need a rigorous security review framework — not vibes.
Sprint Outline
4 weeks · 3 sessions per week
Projects You'll Ship
Leave with real work to show, not just a certificate.
AI Threat Model
A 1-page attack surface map for a real AI system — input vectors, RAG risks, output leakage, and prioritized defense plan. Reusable as a starting template for any LLM system you build.
Prompt Hardening Implementation
A layered input defense with benchmarked results — tested against real prompt injection and jailbreak attempts. Documented and portable to any production system.
AI Security Review + Ship/Hold Decision
A full security audit of a real AI system with CI/CD gates, residual risk documentation, and a stakeholder-ready ship/hold recommendation. The artifact that justifies deployment decisions.
Your Instructors
Nahid Farady, PhD
Principal Tech Lead, AI Security · Microsoft
⭐ 4.9 / 5
Nahid Farady holds a PhD from Virginia Tech and leads AI security, privacy, and responsible AI at Microsoft Copilot. Previously at Google Cloud — where he built privacy-preserving ML and DLP systems — and Capital One CyberML — where he led threat modeling and insider threat detection for ML applications — Nahid brings 10+ years of hands-on experience securing AI systems at scale. He is an Adjunct Professor at UC Berkeley School of Information.
What Students Say
⭐⭐⭐⭐⭐
"Week 1 alone changed how I think about our entire AI stack. We had three prompt injection vulnerabilities I didn't know existed. Fixed all of them by Week 2."
Derek Thompson
AI Engineer · Cloudflare
⭐⭐⭐⭐⭐
"The guardrail tool evaluation framework is something I've never seen taught anywhere. We saved weeks of benchmarking time and made a better decision."
Ashley Morgan
Security Engineer · Okta
⭐⭐⭐⭐⭐
"The Ship/Hold framework in Week 4 is now standard in our AI release process. It's the first time our security reviews have actually influenced deployment decisions."
Nathan Brooks
Tech Lead · Databricks
Sprint Schedule
All sessions are instructor-led and live. Recordings available within 24 hours.
SUNDAY
9:00 AM PDT
Live ClassDeep dive with live red-teaming, tool demos, and adversarial exercises. Hands-on every session.
WEDNESDAY
6:00 PM PDT
Lab SessionStructured A vs B lab. Compare defenses, evaluate tools, and make real decisions with instructor guidance.
THURSDAY
6:00 PM PDT
Build & ShipBuild and test your weekly deliverable. Peer review and instructor feedback before you submit.
Frequently Asked Questions
AI Security: What Every Builder Must Know Before They Ship
with Nahid Farady, PhD · Principal Tech Lead, AI Security at Microsoft
What you'll walk away with:
🎁 Bonus for attendees:
Get "The AI Security Audit Checklist"
A 1-page threat model template + prompt hardening guide for LLM apps
Claim your free seat
Skills you can deploy on Monday morning.