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The AI Threat Landscape: Technical, Nontechnical, and AI-Enabled Threats
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series maps the full AI threat landscape — both technical and nontechnical — examines the specific technical attacks in detail, and covers the system vulnerabilities and new threats that AI itself enables. By the end you will recognize an AI-specific attack when you see one, defend the models and data others overlook, and weigh the human and societal threats that no firewall can stop.
What this episode covers
- AI’s dual nature and why the landscape has widened beyond confidentiality, integrity, and availability.
- Key categories of AI-specific threats — regulatory, adversarial, poisoning, privacy, insider, explainability, and theft.
- Systemic risks from supply chain dependencies and vendor lock-in.
- How AI threats map to business risk — competitive, fines, productivity, reputational, and response.
- The three AI attack surfaces — development-time, runtime, and use.
- Major technical AI attacks — training data leakage, data and model poisoning, theft, prompt injection, evasion, and inversion.
- Nontechnical threats that security teams must own, from bias amplification to deepfakes and rogue behavior.
- AI system vulnerabilities and AI-enabled threats — and why defenses must reach beyond the traditional perimeter.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the key categories of AI-specific threats?
The main AI-specific threats include regulatory risk from fragmented rules, adversarial attacks that trick a model into wrong decisions, data poisoning of training data, data privacy and security breaches, insider threats, lack of explainability that hides errors, and model theft where the algorithm and parameters are copied. Systemic risks add supply chain vulnerabilities and vendor lock-in.
What are the three AI attack surfaces?
A helpful lens groups AI threats into three attack surfaces. Development-time threats arise during building. Runtime threats come from ordinary, non-AI weaknesses in the surrounding infrastructure. Threats through use arise during normal input and output activity. This lens helps teams allocate defenses across the full life cycle.
What are the main technical attacks on AI?
Technical AI attacks include training data leakage from weak access controls, data poisoning that inserts malicious training data, model poisoning that corrupts the model itself, model theft by stealing files or probing the model, prompt injection that overrides the model’s instructions, model evasion that slips inputs past correct judgment, and model inversion that probes the model to extract sensitive training data.
Why are nontechnical AI threats uniquely challenging?
Nontechnical AI threats are uniquely challenging because ethical concerns cannot be patched and biased decisions have no hot fix. They include hallucinations and bias amplification, overreliance on AI, job displacement, societal manipulation through deepfakes, brand-damaging scandals, regulatory infractions, surveillance privacy intrusion, rogue behavior, AI-powered cybercrime, and heavy energy and water consumption.
What are AI-enabled threats?
AI-enabled threats are attacks where adversaries weaponize AI itself, using purpose-built malicious models, crafting convincing deepfake audio and video that defeats old training, and optimizing every stage of an attack into sharper malware, ransomware, and phishing. They demand defenses that reach well beyond the traditional perimeter.
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Reference: This article is based on concepts discussed in The AI Threat Landscape: Technical, Nontechnical & AI-Enabled Threats.