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Generative AI > AI Governance > Implementing an AI Incident Response Playbook

Implementing an AI Incident Response Playbook

Author: Venkata Sudhakar

When an AI system causes harm - returning wrong product prices, refusing valid refund requests, generating offensive content, or making biased recommendations - a well-defined incident response playbook determines whether the team contains the damage in minutes or spends days debugging in chaos. ShopMax India's AI systems handle thousands of customer interactions daily across Mumbai, Hyderabad, and Chennai. Without a structured playbook, a single model failure could cascade into customer complaints, regulatory scrutiny, and revenue loss before anyone even knows what went wrong.

An AI incident response playbook defines severity levels (P1 to P4), escalation paths, investigation checklists, and rollback procedures specific to AI system failures. Unlike traditional software bugs, AI incidents often involve probabilistic failures that are hard to reproduce and require inspecting model inputs, retrieved context, prompt templates, and output logs together. The playbook should also define a post-incident review process to update model cards, retrain guardrails, and prevent recurrence.

The example below implements a lightweight AI incident logger and response coordinator for ShopMax India. It classifies incidents by severity, triggers the right escalation path, and generates a structured incident report for the post-mortem.

from dataclasses import dataclass
from datetime import datetime
from enum import Enum

class Severity(Enum):
    P1 = "P1 - Critical: AI causing financial loss or legal risk"
    P2 = "P2 - High: AI giving systematically wrong answers"
    P3 = "P3 - Medium: AI degraded but functional"
    P4 = "P4 - Low: Cosmetic or edge case issue"

ESCALATION = {
    "P1": ["CTO", "Head of AI", "Legal", "On-call Engineer"],
    "P2": ["Head of AI", "On-call Engineer"],
    "P3": ["On-call Engineer"],
    "P4": ["AI Team Backlog"],
}

@dataclass
class AIIncident:
    title: str
    severity: Severity
    affected_system: str
    symptoms: str
    reporter: str
    city: str
    timestamp: str = None

def __post_init__(self):
        self.timestamp = datetime.utcnow().strftime("%Y-%m-%d %H:%M UTC")

def respond(self):
        level = self.severity.name
        print("=== AI INCIDENT REPORT ===")
        print("Time:", self.timestamp)
        print("Title:", self.title)
        print("Severity:", self.severity.value)
        print("System:", self.affected_system)
        print("City:", self.city)
        print("Symptoms:", self.symptoms)
        print("Reporter:", self.reporter)
        print()
        print("Escalate to:", ", ".join(ESCALATION[level]))
        print()
        print("Immediate Actions:")
        if level == "P1":
            print("  1. Disable AI feature immediately")
            print("  2. Switch to rule-based fallback")
            print("  3. Notify legal and communications team")
            print("  4. Preserve all logs for audit")
        elif level == "P2":
            print("  1. Increase human review rate to 100%")
            print("  2. Pull last 1000 AI responses for review")
            print("  3. Identify prompt or data change that triggered issue")
        else:
            print("  1. Log to backlog with reproduction steps")
            print("  2. Monitor for frequency increase")

incident = AIIncident(
    title="Product recommender returning prices 10x higher than catalog",
    severity=Severity.P1,
    affected_system="ShopMax Product Recommender v2.1.0",
    symptoms="Customers in Mumbai seeing Rs 7,49,990 for Galaxy S24 instead of Rs 74,999",
    reporter="Priya Sharma - Customer Support Lead",
    city="Mumbai"
)
incident.respond()

It gives the following output,

=== AI INCIDENT REPORT ===
Time: 2026-04-14 05:30 UTC
Title: Product recommender returning prices 10x higher than catalog
Severity: P1 - Critical: AI causing financial loss or legal risk
System: ShopMax Product Recommender v2.1.0
City: Mumbai
Symptoms: Customers in Mumbai seeing Rs 7,49,990 for Galaxy S24 instead of Rs 74,999
Reporter: Priya Sharma - Customer Support Lead

Escalate to: CTO, Head of AI, Legal, On-call Engineer

Immediate Actions:
  1. Disable AI feature immediately
  2. Switch to rule-based fallback
  3. Notify legal and communications team
  4. Preserve all logs for audit

In production, wire this playbook to your alerting system so incidents are auto-created from monitoring anomalies - for example, when RAGAS faithfulness score drops below 0.7 or response latency exceeds 2 seconds. Store all incidents in a database with links to the model card version active at the time, the prompt template used, and sample failing inputs. Run a mandatory post-mortem for every P1 and P2 within 48 hours, and publish the findings internally so the whole AI team learns from each failure. For ShopMax India, track the mean time to detect and mean time to resolve across AI incidents the same way you would for API outages - this data is essential for demonstrating AI reliability to enterprise clients and regulators.

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