AI Security Engineer Career Roadmap
Secure AI applications, RAG systems, model integrations, agents, data, tools, and production operations.
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 is for
- 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.
Skill demand for this career
Percentages show how often each skill appears across relevant current opportunities for this career.
Core capabilities
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.
Relevant knowledge checks
Finding quizzes that match this career path...
Tools that support the work
AI-specific threat and control reference.
Risk governance and generative-AI considerations.
Adversarial tactics, techniques, mitigations, and cases.
Identity, credentials, and least-privilege controls.
SAST, dependency, API, container, and IaC security where applicable.
Correlate prompts, tool actions, application events, and security alerts.
How the work typically flows
Map the AI System
Identify components, data flows, identities, and trust boundaries.
Threat Model
Combine conventional AppSec threats with AI-specific abuse paths.
Implement Controls
Secure retrieval, tools, outputs, secrets, supply chain, and approvals.
Test Adversarially
Run prompt injection, data leakage, tool misuse, poisoning, and availability tests.
Monitor and Respond
Observe AI behavior and connect incidents to application and business impact.
Build capability in stages
Application and Cloud Security Foundation
Strengthen IAM, APIs, secure coding, secrets, logging, and supply-chain security.
AI Threat Modeling
Map model, RAG, data, tool, agent, and user trust boundaries.
Secure RAG and Tools
Protect ingestion, retrieval, permissions, outputs, and tool access.
Red Teaming and Evaluation
Design adversarial tests and verify mitigations.
Operational AI Security
Monitor behavior, respond to incidents, retest, and govern changes.
Westbridge Mutual
Fictional workplace scenarioAn AI claims assistant uses enterprise documents and tools, creating prompt-injection, authorization, supply-chain, and operational risks.
Secure the full AI application lifecycle and produce testable evidence.
Westbridge Mutual AI Claims Assistant Security Assessment
Threat-model and test a simulated AI claims assistant that uses enterprise documents, retrieval, and controlled tools.
Model, retrieval, tools, identities, data stores, and trust boundaries.
Prompt injection, data leakage, poisoning, tool misuse, supply-chain, and availability scenarios.
Adversarial prompts and expected secure behavior.
Authorization, validation, source integrity, approval, and logging controls.
Containment, credential revocation, evidence preservation, recovery, and retesting.
What you should be able to show
Shows architecture and abuse-path reasoning.
Demonstrates practical validation.
Shows retrieval and authorization controls.
Shows least-privilege thinking.
Shows production readiness.
Translate 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.