AI Deployment Needs a Rollback Path
AI deployment is safer when a tested rollback path, a versioned artifact, and a defined go/no-go decision exist before the new model takes live traffic.
AI Cost Management Needs Usage Attribution
AI cost management works when spend is attributed to a specific use case, owner, and business outcome instead of tracked as one undifferentiated bill.
AI Model Monitoring Needs a Drift Baseline
AI model monitoring is useful when a stable baseline, defined drift signals, and an owner who can act sit behind the dashboard.
AI Access Logging Needs Prompt-Level Detail
Standard web access logs are not enough for AI systems. Prompt-level logging captures what an incident reconstruction actually needs.
Shadow AI Needs Discovery Before Policy
A policy written before discovery fails. Find actual AI use through network review, audits, procurement records, and non-punitive surveys.
AI Incident Response Needs a Model-Specific Playbook
A generic IT incident response plan misses AI failure modes. A model-specific playbook covers rollback, evidence, and decision rights.
AI Output Handling Needs Downstream Validation
Raw model output should never reach a database, shell, or user unvalidated. Apply the same discipline used for user input.
Training Data Poisoning Needs Source Control
Poisoning attacks corrupt training or fine-tuning data. Source control, provenance, and auditing reduce the risk before a training run.
AI Red Teaming Needs a Realistic Adversary
Red teaming an AI system differs from red teaming a network. Define the adversary first, then test, document, and feed findings back.
Model Supply Chain Needs Provenance Checks
An unverified model file is a supply chain risk. Provenance, signing, and version pinning bring model artifacts under control.