AI Security Monitoring and Incident Response

A focused practitioner course on the two things traditional IT monitoring and incident response handle poorly for AI systems: recognizing a compromise and knowing what to do about it. AI inherits every traditional attack surface and adds more through data, models, and inference behavior. Since an attacker can manipulate what a model learns from, or sees at runtime, without touching the infrastructure, monitoring has to shift from "is the system running" to "is it behaving as intended, on the right data, for its approved purposes."

The key takeaway is telling model drift from an actual attack. A signals table maps patterns to likely causes (near-identical unusual inputs suggest probing, a slow accuracy decline suggests drift, a query spike from one credential suggests extraction), paired with a first-hour triage sequence. The course then builds judgment: separating anomalies from incidents, preserving the evidence responders most often lose (model version, prompt and response logs, training data lineage, guardrail configuration), and rating severity by impact, so a wrong eligibility or pricing decision from a data integrity failure rates high even though nothing technically broke. The closing module applies the four-phase incident response lifecycle with AI-specific containment: rolling back to a validated model version, replacing automated decisions with human review, quarantining a compromised training data source, and closing 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