AI cybersecurity careers: two paths into a growing overlap

These areas overlap, but they are not interchangeable. A security operations analyst may need strong incident reasoning; an AI application security engineer may need code review and systems knowledge. Start with the responsibilities that match your existing background.

OWASP’s LLM application guidance includes prompt injection, sensitive-information disclosure, improper output handling and excessive agency. Learn how each risk appears in an application’s data and action flow rather than memorising a list.

For example, a document assistant needs to retrieve only material the user is allowed to access. A tool-using system needs the application to enforce permissions. Model instructions alone should not be treated as an access-control system. NIST’s AI risk framework provides broader context for documenting risks and responsibility.

HTTP, authentication, authorisation, secrets management, logging and incident response remain useful. A new model does not replace the need to understand which component can read or change which resource.

If you already work in application security, learn how prompts, retrieved documents, model outputs and tools connect. If you work in security operations, focus on the quality of evidence and how an AI-generated summary could omit or distort it. If you are new to security, build the fundamentals before claiming specialist expertise.

Use a local practice application with fictional documents assigned to two separate users. Map the flow from request to retrieval, model response and any available action. Identify where identity and permissions must be enforced.

Create benign test cases for cross-user document access, misleading instructions inside retrieved text and malformed model output. Define the expected safe behaviour before running the tests. Keep the exercise within systems you own or have permission to test.

Implement a focused control, such as permission filtering before retrieval, server-side tool allowlists or output validation. Record the original failure, the change and the regression result. State what the control does not solve. This is more informative than declaring the application “secure” after a handful of tests.

Build a synthetic incident timeline from a small set of fictional alerts and logs. Ask an AI-assisted workflow to draft a summary that references the underlying events. Evaluate whether it preserves timestamps, distinguishes observations from hypotheses and identifies missing evidence.

Compare the assisted summary with a manually reviewed reference. Track omitted events and unsupported claims separately. Keep containment and account changes under explicit human control in the exercise. The goal is to demonstrate careful assistance, not to automate every decision.

Search for AI application security, product security, AI risk, security engineering and security automation responsibilities. A role need not contain “AI” in its title to involve these systems. Explain your actual security experience and the additional AI work you have done.