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AI Security
AI Security Engineer Career Roadmap
Secure AI applications, RAG systems, model integrations, agents, data, tools, and production operations.
Intermediate to AdvancedFlexible roadmapCareer roadmap
CAREER ROADMAP VIDEOAI Security Engineer Career Roadmap | Prompt Injection, RAG & AI Red Teaming
Open on YouTube ↗ROLE OVERVIEW
What this career actually involves
AI Security Engineers apply application, cloud, identity, data, and AI-specific security controls to systems that use models, retrieval, tools, and agents.
Who this path can fit
- Application or cloud security engineers moving into AI systems.
- AI engineers who need deeper security engineering skills.
- Security professionals learning prompt injection, RAG security, model supply-chain risk, and AI incident response.
AI Security EngineerProfessionalGenAI Security EngineerProfessionalAI/ML Security EngineerProfessionalAI Red Team EngineerSpecialized
SKILLS EMPLOYERS ARE ASKING FOR
Skill demand for this career
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CORE CAPABILITIES
Skills to build for this career
✓
AI Threat Modeling
Map model, data, retrieval, tool, identity, and trust boundaries.
✓
Prompt Injection Defense
Design controls for malicious or indirect instructions.
✓
Secure RAG
Protect ingestion, retrieval, authorization, and source integrity.
✓
AI Supply Chain
Review models, datasets, dependencies, connectors, and vendors.
✓
Agent & Tool Security
Limit tool access, credentials, actions, and approvals.
✓
Red Teaming & Evaluation
Test abuse cases and verify mitigations.
✓
AI Incident Response
Contain model/tool routes, preserve traces, revoke access, and retest.
TEST YOUR SKILLS
Relevant knowledge checks
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TOOLS & PLATFORMS
Tools that support the work
OWASP LLM / GenAI guidance
AI-specific threat and control reference.
NIST AI RMF + GenAI Profile
Risk governance and generative-AI considerations.
MITRE ATLAS
Adversarial tactics, techniques, mitigations, and cases.
Cloud IAM / Secrets
Identity, credentials, and least-privilege controls.
Application Security Tools
SAST, dependency, API, container, and IaC security where applicable.
Tracing / SIEM
Correlate prompts, tool actions, application events, and security alerts.
REAL WORKFLOW
How the work typically flows
01
Map the AI System
Identify components, data flows, identities, and trust boundaries.
02
Threat Model
Combine conventional AppSec threats with AI-specific abuse paths.
03
Implement Controls
Secure retrieval, tools, outputs, secrets, supply chain, and approvals.
04
Test Adversarially
Run prompt injection, data leakage, tool misuse, poisoning, and availability tests.
05
Monitor and Respond
Observe AI behavior and connect incidents to application and business impact.
DEVELOPMENT ROADMAP
Build capability in stages
Stage 1
Application and Cloud Security Foundation
Strengthen IAM, APIs, secure coding, secrets, logging, and supply-chain security.
OutcomeApply conventional security controls to AI-enabled applications.
Stage 2
AI Threat Modeling
Map model, RAG, data, tool, agent, and user trust boundaries.
OutcomeIdentify AI-specific attack paths and business impact.
Stage 3
Secure RAG and Tools
Protect ingestion, retrieval, permissions, outputs, and tool access.
OutcomeReduce data leakage and excessive-agency risk.
Stage 4
Red Teaming and Evaluation
Design adversarial tests and verify mitigations.
OutcomeProduce repeatable evidence instead of relying on guardrail claims.
Stage 5
Operational AI Security
Monitor behavior, respond to incidents, retest, and govern changes.
OutcomeOperate AI security across the lifecycle.
WORKPLACE SCENARIO
Westbridge Mutual
Problem
An AI claims assistant uses enterprise documents and tools, creating prompt-injection, authorization, supply-chain, and operational risks.
Objective
Secure the full AI application lifecycle and produce testable evidence.
PORTFOLIO PROJECT
Westbridge Mutual AI Claims Assistant Security Assessment
Threat-model and test a simulated AI claims assistant that uses enterprise documents, retrieval, and controlled tools.
AI System Data-Flow Diagram
Model, retrieval, tools, identities, data stores, and trust boundaries.
Threat Model
Prompt injection, data leakage, poisoning, tool misuse, supply-chain, and availability scenarios.
Red-Team Test Set
Adversarial prompts and expected secure behavior.
RAG / Tool Control Matrix
Authorization, validation, source integrity, approval, and logging controls.
AI Incident Playbook
Containment, credential revocation, evidence preservation, recovery, and retesting.
PORTFOLIO EVIDENCE
What you should be able to show
AI Threat Model
Shows architecture and abuse-path reasoning.
Red-Team Evidence
Demonstrates practical validation.
Secure RAG Design
Shows retrieval and authorization controls.
Agent Permission Matrix
Shows least-privilege thinking.
Incident Playbook
Shows production readiness.
INTERVIEW PREPARATION
Turn your learning into an interview story
How would you defend against indirect prompt injection?
Explain source trust, retrieval controls, tool boundaries, output validation, and approvals.
How is AI security different from normal AppSec?
Explain what stays the same and what changes with models, retrieval, tools, and nondeterministic behavior.
How would you respond to a poisoned RAG source?
Discuss containment, source removal, index rebuild, evidence, and retest.
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