Remediation Plan

The emergency steps taken to stop active harm and restore a safe operational state. This is about tactical response, not permanent fixes.

• Actions: Executing a model rollback, activating a circuit breaker, or triggering a kill switch.
• Goal: Minimize immediate blast radius and reduce Mean Time To Resolve (MTTR).
• Example: An automated rollback to the last known good model version when the hallucination rate breaches a critical threshold defined in the error budget.

The engineering and process changes designed to ensure the same incident class can never recur. This moves the system toward a stronger anti-fragile state.

• Engineering Fixes: Implementing new guardrails, enhancing drift detection monitors, or adding out-of-distribution detection.
• Process Fixes: Updating the runbook automation, modifying the escalation policy, or adding a new health check.
• Example: After a prompt injection attack, the fix is not just blocking the specific string, but implementing a new architectural bulkhead isolation pattern for the agent's tool-calling module.

A predefined plan for transparently notifying internal and external parties about the incident's status, impact, and resolution timeline. This manages legal, reputational, and customer risk.

• Internal: Automated alerts to the on-call SRE, security, and legal teams per the escalation policy.
• External: Templates for customer-facing status pages, regulatory notifications, and public relations statements.
• Example: A tiered notification system where a SEV-1 incident triggers an automatic update to the company's public status page within 15 minutes, followed by a post-mortem summary within 48 hours.

The specific tests and metrics used to confirm the remediation was successful and to continuously watch for a resurgence of the issue. This closes the incident loop.

• Verification: Running a canary deployment of the fix, replaying production traffic in shadow mode, and executing adversarial test suites.
• Monitoring: Creating a new SLO-based alert on the specific failure mode, tracking the burn rate of the new error budget.
• Example: After fixing a data poisoning vulnerability, a new real-time monitor is deployed to track the statistical divergence of incoming features from the trusted training baseline.

A formal, written record of the entire incident lifecycle, created without assigning individual fault. The goal is organizational learning and systemic resilience.

• Contents: A detailed timeline, the full RCA, a record of all corrective actions, the customer impact assessment, and a prioritized list of follow-up action items.
• Purpose: Serves as the immutable decision provenance for the incident, enabling future audits and preventing knowledge loss.
• Example: A post-mortem document that identifies a lack of automated testing for model fairness as the core gap, leading to a new CI/CD pipeline stage for bias detection.

A self-healing mechanism that triggers an immediate reversion to a prior model version when predefined performance thresholds or error budgets are breached. This is the primary automated execution arm of a remediation plan, eliminating the latency of human approval for known failure modes.

• Monitors drift detection signals and hallucination rate spikes
• Executes a canary rollback before full traffic cutover
• Logs the event for the blameless post-mortem

A stability pattern that automatically stops requests to a failing AI service to prevent cascading failures and allow the system to recover. When a remediation plan identifies an upstream dependency as the root cause, the circuit breaker isolates the fault domain.

• Transitions through closed, open, and half-open states
• Prevents thundering herd retry storms
• Works in concert with bulkhead isolation for tenant-level fault containment

A structured analysis of an AI incident focusing on systemic root causes and process improvements without assigning individual fault. The remediation plan provides the corrective actions, but the post-mortem ensures those actions address the underlying socio-technical failures.

• Documents the decision provenance leading to the incident
• Produces action items that update the runbook automation
• Distinguishes between proximate cause and root cause

The maximum amount of time an AI service can fail to meet its Service Level Objective (SLO) before triggering a freeze on new feature deployments. A remediation plan is invoked when the burn rate signals that the error budget is being consumed faster than acceptable.

• Calculated as 1 minus the SLO target (e.g., 99.9% uptime = 43 min/month budget)
• Burn rate alerts trigger the escalation policy
• Exhaustion forces a shift from feature velocity to reliability work

A manual or automated emergency mechanism that instantly disables an AI system's ability to act on its outputs when it poses an imminent threat. This is the most extreme action in a remediation plan, reserved for scenarios where guardrails and circuit breakers have failed.

• Severs the actuation path, not just the inference path
• Requires a documented two-person rule for manual activation in some compliance frameworks
• Triggers an immediate SEV-1 incident declaration

The automated monitoring process that identifies statistical changes in production input data or model predictions relative to a training baseline. Drift is often the earliest signal that a remediation plan must be activated before a full incident materializes.

• Data drift: Input distribution shifts (e.g., new user demographics)
• Concept drift: Relationship between inputs and target variable changes
• Prediction drift: Model output distribution changes independently of input drift