AI Incident Response Orchestrator

Job Title: AI Incident Response Orchestrator

Role Summary

The AI Incident Response Orchestrator is responsible for coordinating and executing the organizations response to AI-related incidents. This includes monitoring AI system health, detecting and triaging AI failures or security risks, leading cross-functional response efforts, and ensuring compliance with AI governance standards. The role supports proactive risk detection, incident mitigation, and continuous improvement of AI operational processes.

Key Responsibilities

1. AI Incident Monitoring & Detection

• Monitor AI/ML models, LLM-based applications, and AI services for anomalies, performance degradation, drift, or abnormal outputs.
• Identify potential AI-related security risks such as prompt injection, model extraction, data poisoning, or hallucinations.
• Review logs, telemetry, inference responses, and risk indicators generated by monitoring platforms.
• Escalate early signals of AI misbehavior to senior engineers or risk teams.

2. Incident Triage & Classification

• Categorize AI incidents based on severity, impact, urgency, and compliance risk.
• Assess whether incidents involve:
• AI security threats
• Model bias or fairness issues
• Data leakage or misuse
• System outages or API failures
• Model drift or accuracy degradation
• Hallucinations affecting end users or business operations
• Determine appropriate containment and mitigation activities.

3. Incident Response Coordination

• Lead cross-functional response involving engineering, cybersecurity, data science, legal, compliance, and product teams.
• Ensure incidents follow established AI/ML incident response playbooks.
• Facilitate war-room sessions, align stakeholders, and track incident progress to closure.
• Coordinate rollback, patching, re-training, or disabling of affected AI components.

4. Remediation & Mitigation Support

• Support technical teams in implementing containment steps such as:
• Access restriction
• Traffic throttling
• Model isolation or fallback activation
• Update to guardrails, filters, or safety constraints
• Validate that remediation actions reduce future risk and restore model outputs to expected behavior.
• Assist with post-incident validation testing.

5. AI Risk & Governance Compliance

• Ensure incidents are documented according to internal AI governance frameworks.
• Support compliance reporting for standards such as:
• AI ethics guidelines
• Responsible AI policies
• Data privacy requirements (GDPR, etc.)
• Track recurring issues and identify gaps in AI policy or controls.

6. Incident Documentation & Reporting

• Create detailed incident reports including root cause, timelines, impact analysis, and resolution steps.
• Track all AI incidents within IR platforms such as ServiceNow, JIRA, or security incident tools.
• Contribute to incident dashboards and trend analysis.

7. Post-Incident Review & Continuous Improvement

• Lead after-action reviews and capture lessons learned.
• Recommend improvements in:
• AI guardrails and filters
• Monitoring rules
• Model validation processes
• Operational playbooks
• Help refine and enhance AI incident response frameworks and SOPs.

8. Collaboration with AI, Security & Engineering Teams

• Work with data scientists and MLOps to understand model behavior and risk factors.
• Collaborate with cybersecurity teams on AI threat detection and mitigation strategies.
• Coordinate with IT operations during AI service outages or infrastructure failures.

Required Qualifications

• 3–6 years of experience in incident response, security operations, AI/ML operations, or IT engineering.
• Working knowledge of:
• AI/ML lifecycle concepts (training, validation, deployment, monitoring).
• LLMs, generative AI systems, and API-based AI services.
• Security risks involving AI (prompt injection, model extraction, data poisoning, jailbreaking).
• Experience with monitoring tools, observability platforms, or AIOps systems.
• Strong understanding of incident response methodologies (NIST, MITRE ATLAS a plus).
• Experience with ITSM or IR tools (ServiceNow, JIRA, Splunk, Sentinel, etc.).
• Ability to evaluate AI outputs for quality, risk, or policy violations.

Preferred Qualifications

• Exposure to MLOps platforms (MLflow, Kubeflow, SageMaker).
• Understanding of responsible AI frameworks, fairness, and bias evaluation.
• Familiarity with LLM safety tools, filters, and content moderation systems.
• Basic scripting skills (Python preferred).
• Certifications such as:
• CompTIA Security+
• GIAC Incident Handler (GCIH)
• AI/ML certification (Microsoft, Google, AWS)
• Responsible AI or Trustworthy AI training

Core Competencies

• Strong analytical and problem-solving skills.
• Excellent coordination, communication, and stakeholder management abilities.
• Ability to remain calm under pressure and manage multiple incidents concurrently.
• Detail-oriented with strong documentation habits.
• Ethical judgment and sensitivity when handling AI governance and risk issues.