Secure It. Test It. Measure It. Trust It.

Building an AI application is only the beginning.

A production AI system must be protected against attacks, sensitive-data exposure and unsafe actions. It must also be continuously tested and evaluated to ensure that changes to models, prompts, retrieval, tools or agents do not silently reduce quality or reliability.

This hands-on specialization teaches you how to build the security, evaluation and observability layer around AI applications.

You will learn how to protect AI systems against realistic threats, evaluate RAG and agent quality, perform AI red-team testing, monitor production behavior and continuously improve system quality.

---

What You’ll Learn

🔐 1. AI Security & Threat Modeling

Understand the attack surface of modern AI applications.

Learn:

• AI security fundamentals
• Threat modeling
• Trust boundaries
• Data-flow analysis
• AI attack surfaces
• Sensitive information exposure
• AI abuse scenarios
• Security risk identification
• Defense-in-depth architecture

Understand where security risks can appear across:

User → LLM → RAG → Tools → APIs → Databases → Agents → External Systems

---

🧨 2. Prompt Injection & Jailbreak Defense

Learn how attackers can manipulate AI systems.

Cover:

• Direct prompt injection
• Jailbreak attempts
• Indirect prompt injection
• Malicious documents
• Untrusted web content
• Tool-based injection
• External-data injection
• Context manipulation
• Defense strategies

Learn how to design systems that do not blindly trust instructions or external content.

---

🛡️ 3. Input & Output Guardrails

Build controls around what enters and leaves your AI system.

Input Security

• Input validation
• Sanitization
• Content filtering
• PII detection
• Malicious-input detection
• Policy enforcement

Output Security

• Schema validation
• Response validation
• Content filtering
• Groundedness checks
• Policy enforcement
• Controlled outputs

---

🔑 4. Agent & Tool Security

Secure AI systems that can take actions.

Learn:

• Authentication
• Authorization
• Least-privilege access
• Tool permissions
• Access control
• Sandboxing
• Controlled execution
• Human approval
• Secure tool contracts

The goal is:

Controlled autonomy — not unrestricted autonomy.

---

📊 5. AI Application Testing

Learn how traditional software testing needs to evolve for AI applications.

Cover:

• Unit testing
• Integration testing
• API testing
• Regression testing
• End-to-end testing
• Deterministic checks
• Edge cases
• Adversarial scenarios
• Automated test suites

Apply these concepts to:

LLM → RAG → Workflows → Tools → Agents

---

🎯 6. RAG Evaluation

Learn how to measure whether your RAG system is actually producing useful and trustworthy answers.

Evaluate:

• Retrieval quality
• Retrieval relevance
• Context quality
• Context relevance
• Answer relevance
• Faithfulness
• Groundedness
• Citation quality
• End-to-end answer quality

Understand the difference between:

“The application returned an answer.”

and

“The application returned a correct, relevant and grounded answer.”

---

🤖 7. AI Agent Evaluation

Evaluate agents beyond simply checking their final answer.

Measure:

• Task success
• Tool selection
• Tool execution
• Decision quality
• Execution trajectory
• Agent reliability
• Failure patterns
• Human-intervention requirements
• Multi-step task completion

Learn how to identify where an agent failed, not just whether it failed.

---

📈 8. Evaluation-Driven Development

Build a systematic approach to AI quality.

Learn how to create:

• Evaluation datasets
• Test cases
• Quality criteria
• Benchmarks
• Automated evaluation pipelines
• Regression suites
• Quality thresholds

Then use evaluations to continuously improve:

Prompts → Models → Retrieval → Tools → Workflows → Agents

---

🔍 9. Production AI Observability

Understand what is happening inside your AI system after deployment.

Monitor:

• Logs
• Traces
• Metrics
• LLM calls
• Tool calls
• Agent execution
• Latency
• Token usage
• Error rates
• Model performance
• Retrieval behavior
• End-to-end AI telemetry

Move from:

“Something went wrong.”

to:

“We can see exactly where, why and how it went wrong.”

---

💰 10. Cost & Performance Engineering

Production AI systems must be technically efficient as well as accurate.

Learn to monitor:

• Token consumption
• Model usage
• Inference cost
• Response latency
• API usage
• Retrieval performance
• System throughput

Use telemetry to identify cost and performance optimization opportunities.

---

🔄 11. Continuous AI Quality

Build a continuous improvement loop:

Test → Evaluate → Observe → Analyze → Improve → Test Again

Detect regressions when changing:

• Models
• Prompts
• RAG pipelines
• Embeddings
• Retrieval strategies
• Tools
• Agent workflows

---

🧪 Hands-On Project

Production AI Security, Evaluation & Observability Platform

Take an AI/RAG/Agent application and build the production quality layer around it.

Security

Threat Model → Attack → Detect → Defend → Validate

Perform AI red-team testing against realistic threats such as:

• Prompt injection
• Jailbreaks
• Malicious documents
• Unsafe tool usage
• Sensitive-data exposure

Evaluation

Build automated evaluation suites for:

• RAG retrieval
• Groundedness
• Answer quality
• Agent task success
• Tool selection
• Execution reliability

Observability

Implement monitoring for:

• Logs
• Traces
• Metrics
• Latency
• Token usage
• Errors
• Model behavior
• Agent execution

Final workflow

Attack → Test → Evaluate → Observe → Analyze → Fix → Re-test

The objective is to transform an AI prototype into a system that is measurable, secure and production-ready.

---

🎯 Who This Program Is For

Software Engineers, AI Engineers, ML Engineers, QA Engineers, Architects, Tech Leads, Security professionals and experienced developers who want to build AI systems that are secure, testable, measurable and reliable in production.

It is especially valuable for people already working with LLMs, RAG, AI workflows or agents who want to understand what is required beyond the prototype stage.

---

🌟 What Makes This Specialization Different?

Most AI courses focus on:

Build the AI application.

This specialization focuses on what comes next:

Can we trust it?

You will learn to answer four critical production questions:

🔐 Is it secure?

Protect it against attacks and unsafe behavior.

🧪 Does it work?

Test its behavior systematically.

📊 How good is it?

Evaluate quality using measurable criteria.

🔍 What is happening in production?

Observe, diagnose and continuously improve it.

---

🎓 What You’ll Walk Away With

• AI security threat-modeling skills
• Understanding of prompt injection and jailbreak defenses
• Practical guardrail implementation knowledge
• Agent and tool security principles
• AI red-team testing experience
• RAG evaluation knowledge
• Agent evaluation techniques
• Automated AI testing strategies
• Evaluation-driven development practices
• Production AI observability concepts
• Cost and performance monitoring skills
• Experience building AI quality pipelines
• A practical security, evaluation and observability project

The Outcome

Move beyond “Does my AI application work?” to “Is it secure, reliable, measurable and ready for production?”