AI Security Engineering Roadmap From Linux Fundamentals to AI Security Engineering

A structured, instructor led program designed to build real engineering capability in Linux, cloud systems, DevOps, and AI security. The curriculum begins with core systems foundations and progresses through applied projects, automation, and AI governance.

Duration

14 Months

(Phased Roadmap)

Format

100% Live

(No Pre-recorded Video)

Schedule

Tue, Thu, Sat

(Professional Track)

Cohort Size

Limited to 40 Seats

(Fixed Start Date)

WHO THIS PROGRAM IS FOR

This program is intentionally structured for learners who want to build long term engineering capability through fundamentals, consistent practice, and applied work.

This Program Is For You If You:

This Program Is Not a Fit If You:

Program Structure and Phases

The program is organized into phases so skills are developed in the right order and reinforced through real work. Each phase builds on the previous one, ensuring that foundational knowledge supports more advanced topics rather than being skipped or rushed.

Phase 1: AI Infrastructure & Cloud Defense

(Months 1–5) The Foundation. You cannot secure AI if you cannot secure the operating system. In this phase, you stop using GUIs and start engineering. You will master the Linux kernel, architect secure AWS networks, and harden the attack surface where AI models live. Core Stack: Linux Administration Bash Scripting AWS VPC Network Security Outcome: You will be able to deploy and lock down the infrastructure that powers enterprise AI.

Apply for Phase 1 →

Phase 2: AI DevSecOps & Secure Automation

(Months 6–9) The Engineering Core. Move from manual configuration to Infrastructure as Code (IaC). You will automate defenses using Terraform, build secure CI/CD pipelines, and begin offensive operations against ML models to understand how they break. Core Stack: Terraform Docker/Kubernetes CI/CD Pipelines Adversarial ML Outcome: You will be able to automate security architecture and identify vulnerabilities in machine learning pipelines.

View Technical Curriculum →

Phase 3:AI Security Engineering Master

(Months 10–14) The Specialization. This phase focuses on securing real AI-enabled systems. You will learn how AI pipelines are deployed, where they fail, and how to defend them using secure engineering practices. You will analyze model risks, secure containerized AI workloads, and implement practical governance controls aligned with recognized frameworks like NIST AI RMF. CORE STACK Kubernetes Security · Secure MLOps · LLM Threat Modeling · OPA Policy Controls · AI Risk Mapping

See Capstone Projects →

Projects, Labs, and Applied Experience

At Gracespot Academy, experience is built through consistent practice and structured application. From the earliest weeks, students work hands on with systems, networks, and cloud environments, learning how technology behaves under real operating conditions. Learning progresses through guided labs and applied projects that reinforce fundamentals, expose common failure points, and develop practical problem solving ability over time.

Daily and Weekly Practice

Students engage with systems throughout the week through short applied exercises, interview style questions, and weekly hands on labs that reinforce core technical concepts.

Monthly Integrated Projects

Each month includes a major project that combines multiple skills across Linux, cloud, DevOps, and AI security, reflecting how real systems behave in production environments.

Review and Progression

Projects and labs are reviewed, refined, and extended as students advance through the program, ensuring skills compound rather than reset at each phase.

Representative Projects You Will Complete

Throughout the program, students complete a sequence of applied projects designed to reflect real operational and security challenges. The examples below represent the type and depth of work produced as students progress from systems fundamentals to DevOps and AI security practice.

Enterprise Linux Hardening & Audit

(Phase 1) The Challenge: Take a vulnerable default Linux server and secure it to DoD standards. The Deliverable: A fully hardened Linux environment with configured firewalls (UFW/iptables), SSH key-based authentication, and a scripted audit log report proving compliance with CIS Benchmarks.

Zero-Trust AWS Network Architecture

(Phase 1) The Challenge: Design a cloud network that assumes breach. The Deliverable: A segmented AWS VPC with public/private subnets, strict NACLs, and a Bastion Host for secure entry. You will document the topology using professional architecture diagrams.

Infrastructure as Code (IaC) Deployment

(Phase 2) The Challenge: Eliminate “ClickOps” and manual errors. The Deliverable: A modular Terraform codebase that deploys a complete web application stack (EC2, RDS, S3) with a single command, including state locking and drift detection.

Secure CI/CD Pipeline ("DevSecOps")

(Phase 2) The Challenge: Automate security checks before code hits production. The Deliverable: A Jenkins/GitHub Actions pipeline that automatically builds a Docker container, scans it for vulnerabilities (Trivy/Anchore), and blocks the deployment if critical CVEs are found.

LLM Red Teaming & Defense

(Phase 3) The Challenge: Attack and defend a Large Language Model. The Deliverable: A documented “Red Team Report” demonstrating prompt injection and jailbreak attacks against an LLM, followed by the implementation of NVIDIA NeMo Guardrails to prevent those specific attacks.

NIST AI RMF Governance Audit

(Phase 3) The Challenge: Assess a high-risk AI system for compliance. The Deliverable: A complete AI Impact Assessment and Risk Management Plan aligned with the NIST AI RMF 1.0 standard, ready to be presented to a C-Suite executive.

Time Commitment and Learning Structure

This program is designed for learners who want to build skills steadily while balancing work, family, and other responsibilities. Learning is structured to be consistent and manageable, with clear expectations around time, effort, and progression.

Live Instruction

Instructor led sessions take place three days per week, focusing on core concepts, guided walkthroughs, and applied demonstrations.

Hands On Practice

Weekly hands on labs and applied exercises reinforce each topic, helping students develop confidence through repetition and real problem solving.

Independent Study

Independent time is used for reviewing material, completing assignments, and preparing for assessments at a steady, manageable pace.

Most students should expect to dedicate approximately 12–14 hours per week, including live sessions, labs, and independent study. Time commitment may increase slightly during major project weeks or certification preparation periods.

Certification Alignment

The curriculum is designed to align with widely recognized industry certifications while prioritizing practical skill development. Certification preparation is integrated as reinforcement for hands on learning, not treated as the primary goal of the program.

AI Infrastructure & Cloud Defense

(Phase 1 • Months 1–5)

Career Goal: Entry-Level IT & SOC Analyst Qualify for: Junior SysAdmin, Network Technician, Cloud Support Associate.

CompTIA Network+
CompTIA Security+
AWS Cloud Practitioner

The Engineering Core

(Phase 2 • Months 6–9)

Career Goal: Cloud Engineer & DevOps Qualify for: Solutions Architect, DevOps Junior, Cloud Administrator.

AWS Solutions Architect – Associate (SAA-C03)
HashiCorp Certified: Terraform Associate (003)

The AI Specialization

(Phase 3 • Months 10–14)

Career Goal: AI Security Engineer Qualify for: AI Governance Lead, DevSecOps Engineer, LLM Red Teamer.

CompTIA SecAI+ (New for 2026)

Certifications are used as structured checkpoints to validate understanding. The program emphasizes applied capability and professional judgment rather than exam memorization alone.

Admissions and Expectations

Gracespot Academy is designed for learners who are ready to commit to consistent practice, structured learning, and long term skill development. Admission is selective to ensure a focused learning environment and meaningful instructor engagement.

Admission Requirements

Basic computer literacy and comfort using a computer
Willingness to learn Linux, networking, and cloud concepts from the ground up
Ability to commit approximately 12–14 hours per week
Reliable internet access and a personal computer
Completion of the application and admissions review process

Program Expectations

Attendance and active participation in live sessions
Completion of weekly labs and monthly projects
Engagement with assessments and applied exercises
Professional conduct and respect for peers
Commitment to ethical and responsible use of technology

Cohorts are intentionally limited to maintain instructional quality, peer collaboration, and individual accountability. This program is not designed for learners seeking shortcuts or minimal effort pathways.

Next Steps

If this program aligns with your goals and you are prepared for a structured, long term learning commitment, the next step is to begin the admissions process. Applications are reviewed to ensure alignment with program expectations and cohort capacity