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AI Incident Response — Identify and Report
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series covers the Identify and Report phase of the AI Incident Response lifecycle. Because AI systems are probabilistic by design, deciding when a wrong answer crosses the line into a genuine incident is far harder than spotting a traditional system failure. Knowing how to set sensible thresholds and put the right monitoring in place is what makes early detection possible.
What this episode covers
- Why AI incidents are uniquely hard to detect — the probabilistic nature of AI and the over-fitting problem.
- Thresholds — how to separate expected errors from real incidents in a system designed to be imperfect.
- Observability — the metrics and baselines needed to see clearly into AI behavior.
- Automated tools and human judgment — why borderline cases need a human in the loop.
- Data-attack visibility — why malicious data is harder to catch than traditional data-loss incidents.
- Detection techniques for prompt injection — input sanitization and pattern-watching.
- Detection techniques for data poisoning — change logs, preprocessing review, and dataset fingerprinting.
- Detection techniques for adversarial inference — call-log analysis for systematic probing patterns.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
Why are AI incidents so hard to detect?
AI is built on probability, which means it is designed to be wrong some of the time. Chasing perfect accuracy actually backfires because an over-fitted model fails on new data. So if some errors are normal and expected, the hard question is which wrong answer counts as an incident. Defining that threshold is what makes detecting AI threats much harder than detecting a simple system failure.
What is observability in AI incident response?
Observability is the ability to see clearly into how the AI system is behaving. Because outputs are probabilistic, the organization needs clear metrics and a baseline of normal performance so it can tell when a model has drifted off course. No single signal is enough, so observability combines automated tools with a human in the loop to judge subjective, borderline cases that no tool can settle on its own.
How do you detect prompt injection attacks?
For prompt injection, where hidden instructions are smuggled into input, you sanitize and evaluate inputs and watch for unusual patterns, like text full of code-like structure or strange characters. The aim is to catch malicious instructions before they reach the model and alter its behavior.
How do you detect data poisoning and adversarial inference?
For data poisoning, monitor access and change logs across the data supply chain, review preprocessing scripts for unauthorized edits, and check dataset versions and their fingerprints. For adversarial inference, where an attacker probes a model to reverse-engineer it, analyze the interface call logs and scan for the tell-tale patterns of systematic testing.
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Reference: This article is based on concepts discussed in AI Incident Response — Identify & Report.