AI Security Monitoring and Incident Response

A tightly scoped, practitioner-level course on where conventional IT monitoring and incident response fall short with AI: knowing what a compromise looks like and what to do once you see one. An AI system carries every traditional attack surface plus more, bound up in data, models, and inference behavior, so an attacker can tamper with what a model learns from, or is shown at run time, without coming near the infrastructure. Monitoring has to move from "is the system up" to "is it behaving as intended, on the right data, for its approved purposes."

The practical core is a signals table mapping patterns to likely causes, paired with a first-hour triage sequence: near-identical unusual inputs read as probing, a slow accuracy decline as drift, a query spike from one credential as extraction. The course then turns to judgment: separating anomalies from incidents, holding on to the evidence responders routinely lose (model version, prompt and response logs, training data lineage, guardrail configuration), and grading severity by impact, so a wrong eligibility or pricing decision caused by a data integrity failure is high severity even though nothing technically broke. The last module runs the four-phase incident response lifecycle with containment built for AI: rolling a model back to a validated version, switching automated decisions to human review, quarantining a compromised training data source, and shutting a confirmed prompt injection vector.

What You Will Learn

• Explain how AI systems introduce security risks that differ from, or compound, traditional IT risks
• Use the working vocabulary of AI security incidents, including data poisoning, adversarial examples, prompt injection, model drift and model extraction
• Describe what continuous monitoring means for a high-impact AI system, beyond uptime and throughput
• Distinguish the four categories of AI monitoring and select the technique that fits each
• Apply a risk-based approach to security event logging for AI systems, and identify what must be captured and retained
• Read common monitoring signals and identify what each most likely indicates
• Recognize which anomalies qualify as AI security incidents and which do not
• Preserve the evidence unique to AI incidents, including model version, prompt and response logs, and training data lineage
• Classify incident severity by impact on safety, rights, mission and data rather than by technical complexity
• Apply the four-phase incident response lifecycle to an AI-specific scenario
• Select containment actions specific to AI systems, including model rollback and disabling automated decisioning
• Carry out post-incident activity including root cause analysis and revalidation before returning a system to automated operation

Course Outline

Module 1

• Foundations: The AI Security Landscape and Federal Policy Context (1h50)
• Why AI systems need a different security lens
• The shared vocabulary of data poisoning, adversarial examples, prompt injection, model drift, model extraction and supply-chain risk
• What counts as an AI incident, covering malicious, accidental and environmental causes
• The specific current guidance that bears on monitoring and incident response
• Who you work with day to day

Module 2

• AI Security Monitoring Practices (1h50)
• What continuous monitoring means for high-impact AI systems
• The four monitoring categories of data quality and integrity, model performance and behavior, access and usage, and output and impact
• Baseline and anomaly detection, threshold alerting, adversarial input detection and human-in-the-loop review
• Risk-based security logging built on continuous event monitoring and threat hunting capability areas
• A signals table mapping observed patterns to likely causes

Module 3

• Identifying and Classifying AI Security Incidents (1h50)
• Recognizing an AI security incident
• The evidence and artifacts unique to AI incidents
• Classifying severity by impact on safety, legal rights, benefits, critical infrastructure and sensitive data
• The initial triage sequence
• A prompt-injection case scenario worked end to end

Module 4

• AI Incident Response, Reporting, and Post-Incident Actions (1h50)
• The four-phase lifecycle of preparation, detection and analysis, containment, eradication and recovery, and post-incident activity, each mapped to AI-specific activity
• Containment actions specific to AI systems
• Internal reporting and escalation
• Root cause analysis, updating impact assessments and monitoring plans, revalidation before return to automated operation, and when stakeholder communication is warranted

This course is available for 365 days. You can choose when to start your access period. Once you activate, you will have 365 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.