HIGAET AI Security
Learn defensive security for AI systems including prompt safeguards, data protection, model access controls, and evaluation of LLM behavior in labs.
Duration
8 weeks · 6-8 hours/week
Level
Advanced
Delivery
Online
Status
Open for enrollment
Why this technology matters.
AI security is the defensive practice of keeping AI systems — especially LLM-powered apps — safe, private, and well-behaved. It matters now because teams are connecting models to real data and tools, where prompt injection, data leakage, and overbroad access can cause real harm. This course teaches safeguards and evaluation habits through guided lab apps.
You will use these controls wherever AI features ship: safeguard patterns for model inputs and outputs, data handling controls for training and retrieval pipelines, and access controls with logging for model endpoints. This solves the everyday risks of careless prompts, exposed data, and unmonitored model use. It does not solve everything: filters do not fix missing evaluation data, access controls do not fix a poorly chosen use case, and logging does not fix alerts nobody reviews.
By the end you will be able to build an LLM input-output safeguard pattern set for a lab app, a data handling control plan for training and retrieval pipelines, and a model endpoint setup with access controls, logging, and a prompt injection and leakage evaluation checklist.
Why this course exists
The gap is between a chatbot demo and an AI feature that is safe to run for real users and real data. This course closes it with an arc from safeguards to data controls to evaluation to operations: design prompt safeguards, protect training and retrieval data, evaluate injection and leakage risks, then lock down model endpoints with access control and logging.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Basic familiarity with LLM applications
- Comfort with Python and REST APIs
- Understanding of data pipelines and access controls
Technologies & tools
Skills you'll gain
A 8 weeks arc, module by module.
- Module 01
Module 01 — Foundations: AI system components and trust boundaries
- Module 02
Module 02 — Data Defense: Dataset provenance and privacy controls
- Module 03
Module 03 — Prompt Safeguards: Injection awareness and defensive design
- Module 04
Module 04 — Core: Model access control and endpoint logging
- Module 05
Module 05 — Engineering: RAG security and retrieval validation
- Module 06
Module 06 — Evaluation: Safety testing and guardrail measurement
- Module 07
Module 07 — Advanced: Controlled red-teaming methods and reporting
- Module 08
Module 08 — Capstone: Guardrailed lab AI app with safety evaluation
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build safeguard patterns for LLM inputs and outputs in lab apps
- Design data handling controls for training and retrieval pipelines
- Develop evaluation checklists for prompt injection and leakage risks
- Deploy access controls and logging for model endpoints
- Integrate content filters and human review gates
- Evaluate model theft, poisoning, and misuse risks defensively
- Secure RAG pipelines with source validation and redaction
- Automate safety regression checks for AI application updates
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
LLM input and output safeguard kit
- Project 02
Secure training and retrieval data controls
- Project 03
Prompt risk evaluation checklist
- Project 04
Model endpoint access and logging setup
- Capstone
Secured lab AI app with safeguards, data controls, and endpoint logging
Speak the language first.
- Prompt safeguards
- Input and output checks around an LLM app that keep interactions within intended and safe behavior.
- Prompt injection
- A trick where hidden instructions in user input or retrieved text try to make the model act against its intended rules.
- Data leakage
- Unintended exposure of private or sensitive information through model outputs, logs, or stored prompts.
- Retrieval pipeline security
- Protecting the documents and search steps that feed an AI app so only allowed content reaches the model.
- Model access controls
- Authentication and permission rules that decide which users and services may call an AI endpoint.
- Output filtering
- Reviewing generated text for disallowed or sensitive content before showing it to users.
- Evaluation checklist
- A repeatable set of test prompts and pass criteria used to check AI behavior for safety risks.
- Endpoint logging
- Recording who called a model, with what inputs and outputs, to support review and incident analysis.
- Data minimization
- Keeping only the training and log data truly needed, with retention limits and access restrictions.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Lab chatbot follows instructions hidden in pasted content
Separate system instructions from untrusted input, add an output review step, and retest with the same injection examples to confirm refusal or safe handling.
Model output reveals sensitive data from retrieval documents
Restrict the retrieval index to approved documents, redact sensitive fields, and add output checks that block responses containing protected patterns.
Evaluation results vary between runs on the same prompts
Fix the test settings and dataset version, run each prompt multiple times, and record pass criteria so results are comparable.
Legitimate prompts are blocked by overly strict safeguards
Review blocked examples to find the triggering rule, narrow its pattern, and add the cases to a regression set for future tuning.
Model endpoint logs miss key request details
Confirm logging captures caller identity, timestamps, and redacted prompts and responses, then emit a test call and trace it end to end.
Retrieval step returns irrelevant or unauthorized documents
Check index permissions and chunk metadata, tighten access filters, and re-evaluate with a fixed question set.
Before you move on, you should be able to
- Explain defensive security risks unique to AI and LLM applications
- Build safeguard patterns for LLM inputs and outputs in lab apps
- Design data handling controls for training and retrieval pipelines
- Evaluate LLM behavior with checklists for injection and leakage risks
- Deploy access controls and logging for model endpoints
- Design response steps for unsafe AI outputs in lab scenarios
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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