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AI Risk Identification Explained
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series explores AI risk identification in depth. It covers why AI risk is always contextual, how the familiar risk management life cycle applies to AI, the concrete steps for surfacing AI risk, and the well-known list of security risks for language-model applications. By the end you will be able to catch problems hiding inside tools you already use and speak a common language with the business about what could go wrong.
What this episode covers
- Why AI risk is always contextual — and why context is a moving target as technology and attackers evolve.
- The risk management life cycle applied to AI — familiar discipline applied to a new and shifting subject.
- The AI inventory problem — surfacing the AI that has been quietly baked into everyday tools.
- Risk in the wiring between systems — how poor separation between AI assistants can turn small oversights into serious breaches.
- The five-step identification sequence — mission, products and services, vital assets, value chain, and impact assessment.
- Loss-event scenarios — turning abstract risk into concrete, business-grounded impact.
- The top-ten language-model application risks — a ready checklist for assessing any LLM-based system.
- The auditor’s instinct — never accepting “we use AI” as a complete answer.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
Why is AI risk considered contextual?
AI risk never floats in a vacuum. To manage it you must understand the full context of how the AI is used, whether across an entire hiring process, at one stage of a digital supply chain, or simply when employees use everyday productivity tools. The risk also keeps shifting as the technology and its attackers evolve.
What are the steps for identifying AI risk?
Identifying AI risk follows a clear sequence. Map out the mission and objectives of the AI program, determine which products and services deliver that mission, identify the vital assets such as technology, processes, vendors, and data, establish an AI value chain that links it all together, and finally assess the impact AI could have through loss-event scenarios.
Why is inventorying AI use the first identification step?
Identification begins with accurately inventorying where AI is actually used because people often have no idea AI is baked into the tools around them. Without an accurate inventory you cannot manage what you do not know exists, and unseen AI is exactly where many of the worst incidents start.
What are the top language-model application risks?
The widely cited top-ten list for language-model applications includes prompt injection, sensitive information disclosure, supply chain risk, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation from hallucinations, and unbounded consumption that can cause denial of service, financial loss, or theft of the model itself.
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Reference: This article is based on concepts discussed in AI Risk Identification Explained.