The AI Security Engineer's Handbook One-Pager

AI Security LLC

Author / AI Security Engineer / Product Security Architect

Context

Created a practitioner-oriented AI Security Engineering Handbook that translates AI risk, governance, product-security, and agentic-system concerns into concrete security engineering tasks, workflows, controls, templates, checklists, evaluation patterns, and implementation guidance for teams building or governing AI-enabled products.

Problem

AI security guidance is often too abstract for builders and too fragmented for security teams. Practitioners face overlapping concerns across model behavior, prompt injection, tool calling, agent permissions, data exposure, RAG authorization, privacy, logging, supply chain, evaluation, and governance evidence. Teams need a handbook that explains what AI security engineers actually do, what artifacts they produce, and how those artifacts connect to product delivery.

What I did

• - Defined AI security engineering as a concrete operating discipline rather than a loose mixture of model safety, compliance, and traditional AppSec.
• - Mapped the recurring tasks an AI security engineer must perform across discovery, architecture review, threat modeling, control design, testing, evaluation, monitoring, and evidence generation.
• - Translated AI product-security concerns into practical workflows that security and engineering teams can execute.
• - Connected prompt injection, excessive agency, RAG authorization, tool-calling risk, data leakage, model behavior, and supply-chain issues to specific engineering controls.
• - Organized governance requirements into artifacts that can be created, reviewed, and maintained rather than policy statements that sit outside delivery.
• - Defined reusable templates for assessment, risk review, control mapping, workflow review, model evaluation, and evidence packaging.
• - Framed AI security work around actual product surfaces: prompts, tools, context stores, APIs, agents, automation workflows, datasets, logs, connectors, and user-facing actions.
• - Emphasized continuous verification and validation because AI behavior changes with prompts, context, tools, model versions, retrieval sources, and workflow design.
• - Created a bridge between executive-level AI risk management and backlog-level security tasks.
• - Positioned the handbook as a durable reference asset for consulting, training, portfolio storytelling, and client delivery.

Outcome

• - Produced a practical AI security engineering reference asset that can support advisory work, portfolio positioning, training, and client delivery.
• - Created a clearer explanation of what AI security engineers do across product, platform, application, governance, and operational surfaces.
• - Converted AI security concepts into usable workflows, templates, control categories, and evidence patterns.
• - Strengthened the user's professional positioning around AI product security, agentic systems, governance implementation, and security engineering leadership.
• - Created reusable material for proposals, interviews, executive briefings, webinars, courses, workshops, and consulting engagements.
• - Established a common vocabulary that connects AI security engineering to product-security program buildout, secure SDLC, governance evidence, and agentic control-plane design.
• - Supported the larger AI Security LLC content ecosystem by giving the Mythos framework and AI security report a practitioner-facing operational companion.

Evidence

• - Practitioner-oriented handbook covering AI security engineering tasks, controls, workflows, templates, and evidence artifacts.
• - Designed to bridge product security, AI governance, agentic workflow security, prompt testing, RAG security, privacy, and control evidence.
• - Reusable across portfolio pages, consulting offers, executive briefings, workshops, training material, and advisory engagements.
• - Part of a broader AI security research platform including the Mythos framework and the State of AI Security Engineering Report.
• - Public-safe metric language; this is an independent research and practitioner enablement asset rather than a confidential client delivery.