Practical lesson

Examples AI Security

See where the skill appears in realistic work situations and what strong execution looks like.

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.

Real-world situations

  1. 1.Testing whether malicious instructions hidden in a retrieved webpage can cause an internal research agent to ignore its rules
  2. 2.Restricting a support agent's refund tool so the model cannot exceed policy even if prompted to do so
  3. 3.Investigating whether a vector knowledge base can expose documents a user was never authorized to retrieve
  4. 4.Designing an incident playbook for a compromised AI agent credential and tracing which tools, records, and actions may have been affected

What strong execution looks like

  1. 1.A skilled practitioner maps trust boundaries, identifies untrusted inputs, inventories tools and credentials, and assumes model output can be wrong or manipulated. They enforce authorization in deterministic systems outside the model, apply least privilege, separate data by user entitlement, validate and constrain high-impact actions, secure ingestion and retrieval, protect secrets, log relevant traces, and test both direct and indirect attacks. They prioritize containment: when prevention fails, the system should limit blast radius and support investigation and recovery.
  2. 2.Security engineers threat-model AI applications; developers implement validation and least privilege; AI engineers harden retrieval and agent designs; identity teams scope credentials; red teams test adversarial paths; risk and governance teams translate technical findings into controls; incident responders investigate AI-specific attack chains.

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.

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