Secure AI applications, RAG systems, model integrations, agents, data, tools, and production operations.
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Build practical analyst foundations around security operations, incidents, cloud security and Security+ concepts.
Learn governance, risk, compliance, controls, AI governance and responsible-AI practices used in modern organizations.
Learn requirements, Agile, Jira, SQL, UAT and practical AI-assisted business-analysis workflows.
AI Security Engineers apply application, cloud, identity, data, and AI-specific security controls to systems that use models, retrieval, tools, and agents.
Percentages show how often each skill appears across relevant current opportunities for this career.
Map model, data, retrieval, tool, identity, and trust boundaries.
Design controls for malicious or indirect instructions.
Protect ingestion, retrieval, authorization, and source integrity.
Review models, datasets, dependencies, connectors, and vendors.
Limit tool access, credentials, actions, and approvals.
Test abuse cases and verify mitigations.
Contain model/tool routes, preserve traces, revoke access, and retest.
Finding quizzes that match this career path...
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.
Identify components, data flows, identities, and trust boundaries.
Combine conventional AppSec threats with AI-specific abuse paths.
Secure retrieval, tools, outputs, secrets, supply chain, and approvals.
Run prompt injection, data leakage, tool misuse, poisoning, and availability tests.
Observe AI behavior and connect incidents to application and business impact.
Strengthen IAM, APIs, secure coding, secrets, logging, and supply-chain security.
Map model, RAG, data, tool, agent, and user trust boundaries.
Protect ingestion, retrieval, permissions, outputs, and tool access.
Design adversarial tests and verify mitigations.
Monitor behavior, respond to incidents, retest, and govern changes.
An 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.
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.
Shows architecture and abuse-path reasoning.
Demonstrates practical validation.
Shows retrieval and authorization controls.
Shows least-privilege thinking.
Shows production readiness.
Explain source trust, retrieval controls, tool boundaries, output validation, and approvals.
Explain what stays the same and what changes with models, retrieval, tools, and nondeterministic behavior.
Discuss containment, source removal, index rebuild, evidence, and retest.
