Course Objective & Scope
When an AI system gives a customer harmful advice, executes an erroneous action or leaks confidential data, the crisis compounds if teams cannot reconstruct which model version, system prompt, retrieved chunk and human check produced that exact output. This course teaches how to establish end-to-end AI traceability and a dedicated AI Incident Response process.
It covers audit logging schemas balanced against privacy retention rules, early-warning observability signals, an AI incident severity matrix (Sev 1–4), kill-switch and rollback playbooks, regulatory reporting triggers and blameless post-mortems. Participants build an AI Incident Response Playbook and run a tabletop scenario.
Who Should Attend?
- IT operations, security incident response (SOC/CSIRT) and business continuity teams
- AI product owners, LLMOps/MLOps leads and platform managers
- Enterprise risk, compliance, legal and crisis communications leaders
- Operations managers running high-impact or customer-facing AI systems
Key Takeaways
- Specify the audit log schema needed to reconstruct any AI decision across prompt, RAG context, model version and human override
- Balance regulatory log-retention duties (e.g., EU AI Act) with KVKK/GDPR privacy controls
- Classify AI incidents—hallucination spikes, jailbreaks, biased outcomes, runaway token spend—by severity
- Design rapid containment procedures including kill-switches, model/prompt rollbacks and manual fallback
- Run an AI incident tabletop exercise and establish a post-mortem to guardrail feedback loop
Curriculum
End-to-End AI Traceability and Audit Logging Architecture
- What to log to reconstruct any event: user input, system prompt version, retrieved RAG chunks, model ID, tool calls, output and human sign-off
- Data and model lineage: tracking training sets, configuration changes and deployment approvals
- Privacy-safe logging: PII redaction, role-based log access and retention periods (EU AI Act 6-month minimum)
Early-Warning Signals and Production Observability
- Monitoring guardrail trigger spikes, thumbs-down rates, override surges and token/latency anomalies
- Detecting upstream provider model changes and input data drift
AI Incident Taxonomy and Severity Matrix (Sev 1–4)
- How AI incidents differ from IT outages: the API returns '200 OK' while producing harmful or costly outputs
- Incident categories: PII leakage, viral hallucination, jailbreak, discriminatory output, runaway agent loop
- Defining Sev 1–4 thresholds, response SLAs and escalation paging trees
Containment, Rollback and Business Continuity Playbooks
- Activating kill-switches, rolling back prompt/model versions or downgrading to deterministic rules
- Triggering manual workflow fallback and coordinating legal and PR communications
- Regulatory notification timelines under KVKK/GDPR and EU AI Act serious-incident rules
Post-Mortem Learning Loop and Tabletop Exercise
- Turning every production incident into a new regression eval case and guardrail rule
- Running a cross-functional tabletop simulation on a realistic AI crisis
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