AI Security Engineer
Protect AI systems from attacks, data leaks and misuse.
Threat modeling, prompt injection, data leakage, agent and tool security, supply chain risk, red teaming, guardrails and monitoring, and incident response. Ends with interview practice.
10 lessons · about 3 hours
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Write the policy a deterministic guardrail layer will enforce.
Plan the eval gate, canary and rollback for a prompt or model change.
Interview-style review of an agent against the eight ops pillars.
Find the biggest savings in an agent's usage without hurting quality.
A blameless postmortem with timeline, root cause and action items.
Generate attack cases for your agent and the checks that detect success.
A calibrated rubric with anchors so judge scores are consistent.
Create diverse test cases including edge, adversarial and policy-sensitive inputs.
Find the first wrong step in a long agent trace and classify the root cause.
Pick the few service-level indicators and targets that matter for your agent.