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AI Incident Response — Identify and Report: Detection and Triage
This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series covers Phase 2 — Identify and Report, the detection and triage step in the five-phase incident-response lifecycle. You’ll see why AI systems are mathematically destined to make some errors, how teams set performance baselines so anomalies stand out, and how to spot the specific attack patterns aimed at AI tools without halting daily operations. The discussion equips auditors to judge whether a new automated tool can be watched safely once deployed.
What this episode covers
- Why AI errors are inevitable — mathematical probability, overfitting, and the challenge of distinguishing a normal miss from an incident.
- AI observability — continuously monitoring internal health and outputs in real time.
- Performance baselines — defining what normal looks like so abnormal behavior can be spotted.
- Human in the loop — pairing automated alerts with human judgment on flagged anomalies.
- Malicious data injection vs. traditional breaches — corruption of data in the pipeline rather than theft, and why it’s harder to spot.
- Detecting prompt injection — input sanitization and watching for atypical patterns.
- Detecting data poisoning — monitoring supply-chain logs, preparation scripts, and cryptographic hashes.
- Detecting adversarial inference — analyzing API connection logs for systematic probing patterns.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
Why do AI systems inevitably make mistakes?
Unlike traditional software that follows rigid rules, machine learning operates entirely on mathematical probability, so it is designed to guess the most likely outcome and will be wrong a certain percentage of the time. Engineers deliberately avoid forcing one hundred percent accuracy on the training data because that causes overfitting, where the system memorizes the exact data it saw but loses the ability to handle new, unfamiliar information.
What is AI observability and why does it need a human in the loop?
AI observability means constantly monitoring the internal health and real-time outputs of the system. Because outputs are never flawless, a company must define clear performance metrics and a baseline of normal behavior, since you cannot spot abnormal activity without defining what normal is. Automated software flags unusual spikes, but a human in the loop must review the anomalies and make subjective judgments computers cannot, like deciding whether a motion sensor caught a burglar or just the family dog.
How is malicious data injection different from a traditional data breach?
Traditional breaches involve data loss, such as hackers stealing customer files. Malicious data injection instead corrupts data as it flows through the pipeline. It is like a criminal sneaking into a currency mint and slightly changing the printing plates, so you still have money but every future bill is counterfeit and useless. These attacks are much harder to spot than traditional theft.
How do you detect prompt injection, data poisoning, and adversarial inference?
To detect prompt injection, sanitize all text before it enters the model and watch for atypical input patterns such as code-like text, excessive special characters, or thousands of near-identical requests. To detect data poisoning, continuously monitor access and change logs across the data supply chain, review preparation scripts, and verify dataset version histories and cryptographic hashes. To detect adversarial inference, analyze API logs for anomalous patterns that reveal someone systematically probing your defenses.
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Reference: This article is based on concepts discussed in AI Incident Response — Identify & Report: Detection & Triage.