Practical lesson

How to develop AI Security

Turn the skill into repeatable behaviour with a staged practice plan.

The idea in one minute

AI security applies security engineering and threat reasoning to systems whose behavior depends partly on machine-learning or generative models. It covers conventional application and cloud security plus AI-specific attack surfaces such as direct and indirect prompt injection, malicious retrieved content, model or data poisoning, sensitive-information disclosure, insecure output handling, vector and embedding weaknesses, system-prompt leakage, excessive agency, model theft, and attacks on agent goals, memory, tools, identities, or inter-agent communication. Strong practitioners avoid treating the model as a trusted security boundary. They design least privilege around tools and data, validate actions outside the model, isolate untrusted content, protect credentials, monitor behavior, test adversarial cases, and build containment and recovery for failures that cannot be completely prevented.

This capability connects directly with AI Governance, AI Risk Management, Cybersecurity Awareness. Open those concepts when the lesson depends on them rather than treating AI Security as an isolated ability.

Start here

  1. 1.Draw the trust boundaries around one AI application
  2. 2.Identify which inputs are untrusted and which actions could cause harm
  3. 3.Learn why a system prompt is not an authorization control
  4. 4.Map one AI application against relevant OWASP GenAI risks

Build working proficiency

  1. 1.Threat-model direct and indirect prompt injection in a RAG workflow
  2. 2.Move authorization checks outside the model and test bypass attempts
  3. 3.Reduce an agent's tool and credential permissions to least privilege
  4. 4.Create adversarial test cases for data leakage and unsafe actions

Stretch toward advanced practice

  1. 1.Lead an AI red-team exercise across model, retrieval, tools, identity, and infrastructure
  2. 2.Design defense-in-depth for an agent with multiple operational tools
  3. 3.Map findings to OWASP, MITRE ATLAS, and organizational controls
  4. 4.Create detection, containment, investigation, and recovery procedures for AI-specific incidents

Build the surrounding skill cluster

AI GovernanceThe ability to establish decision rights, policies, evidence, oversight, and accountability so AI is selected, built, used, monitored, and retired in ways consistent with organizational goals and acceptable risk.AI Risk ManagementThe ability to identify, analyze, prioritize, treat, monitor, and communicate risks created or amplified by AI across its technical, human, business, and societal context.Cybersecurity AwarenessUnderstanding cybersecurity threats and best practices for digital safety.Retrieval-Augmented Generation (RAG)The ability to design and evaluate AI systems that retrieve relevant external knowledge at query time and supply it as grounding context for generated answers or actions.AI Evaluation & BenchmarkingThe ability to design repeatable tests that measure whether an AI system is accurate, useful, safe, reliable, efficient, and fit for a specific real-world purpose.

Keep building this skill

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